2024-12-17T23:46:19.3354329Z Current runner version: '2.321.0' 2024-12-17T23:46:19.3361809Z Runner name: 'i-00cc61a0ee6903cab' 2024-12-17T23:46:19.3362814Z Runner group name: 'Default' 2024-12-17T23:46:19.3363756Z Machine name: 'ip-10-0-61-127' 2024-12-17T23:46:19.3368411Z ##[group]GITHUB_TOKEN Permissions 2024-12-17T23:46:19.3371466Z Actions: read 2024-12-17T23:46:19.3372152Z Attestations: read 2024-12-17T23:46:19.3372800Z Checks: read 2024-12-17T23:46:19.3373426Z Contents: read 2024-12-17T23:46:19.3374300Z Deployments: read 2024-12-17T23:46:19.3374907Z Discussions: read 2024-12-17T23:46:19.3375566Z Issues: read 2024-12-17T23:46:19.3376172Z Metadata: read 2024-12-17T23:46:19.3376835Z Packages: read 2024-12-17T23:46:19.3377496Z Pages: read 2024-12-17T23:46:19.3378075Z PullRequests: read 2024-12-17T23:46:19.3378708Z RepositoryProjects: read 2024-12-17T23:46:19.3379453Z SecurityEvents: read 2024-12-17T23:46:19.3380109Z Statuses: read 2024-12-17T23:46:19.3380733Z ##[endgroup] 2024-12-17T23:46:19.3384252Z Secret source: Actions 2024-12-17T23:46:19.3385447Z Prepare workflow directory 2024-12-17T23:46:19.3880400Z Prepare all required actions 2024-12-17T23:46:19.3919664Z Getting action download info 2024-12-17T23:46:19.6104000Z Download action repository 'pytorch/test-infra@release/2.6' (SHA:eb0adf5a84668865394af69e26428b32c8105c1c) 2024-12-17T23:46:21.2203052Z Download action repository 'pytorch/pytorch@release/2.6' (SHA:0cdf8b1d09254cfda66191d1bd01e3041c3c76f7) 2024-12-17T23:46:33.1050119Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-12-17T23:46:33.3012325Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-12-17T23:46:33.5576501Z Getting action download info 2024-12-17T23:46:33.6894367Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-12-17T23:46:33.8898084Z Getting action download info 2024-12-17T23:46:34.0121238Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2024-12-17T23:46:34.1790161Z Getting action download info 2024-12-17T23:46:34.3097001Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-12-17T23:46:34.4536221Z Getting action download info 2024-12-17T23:46:34.6286001Z Download action repository 'pytorch/test-infra@main' (SHA:a07505a74641a4ff5123d635defac481ef28ef1e) 2024-12-17T23:46:36.0302853Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/release/2.6 (0cdf8b1d09254cfda66191d1bd01e3041c3c76f7) 2024-12-17T23:46:36.0304911Z ##[group] Inputs 2024-12-17T23:46:36.0305293Z build-environment: linux-focal-py3.13-clang10 2024-12-17T23:46:36.0307687Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]} 2024-12-17T23:46:36.0310416Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:46:36.0311175Z sync-tag: 2024-12-17T23:46:36.0311988Z timeout-minutes: 600 2024-12-17T23:46:36.0312277Z use-gha: 2024-12-17T23:46:36.0312522Z dashboard-tag: 2024-12-17T23:46:36.0312772Z s3-bucket: gha-artifacts 2024-12-17T23:46:36.0313062Z aws-role-to-assume: 2024-12-17T23:46:36.0313635Z disable-monitor: false 2024-12-17T23:46:36.0314175Z ##[endgroup] 2024-12-17T23:46:36.0314710Z Complete job name: linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:46:36.0821727Z A job started hook has been configured by the self-hosted runner administrator 2024-12-17T23:46:36.0921744Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-12-17T23:46:36.0930698Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:46:36.0931412Z ##[endgroup] 2024-12-17T23:46:37.1993489Z Runner Type: linux.4xlarge 2024-12-17T23:46:37.1993997Z Instance Type: c5.4xlarge 2024-12-17T23:46:37.1994288Z AMI Name: unknown 2024-12-17T23:46:37.2020193Z AMI ID: ami-0fff1b9a61dec8a5f 2024-12-17T23:46:42.9356472Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@release/2.6 2024-12-17T23:46:42.9356988Z with: 2024-12-17T23:46:42.9357785Z github-secret: *** 2024-12-17T23:46:42.9358656Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-12-17T23:46:42.9359488Z activate-with-label: false 2024-12-17T23:46:42.9359785Z label: with-ssh 2024-12-17T23:46:42.9360037Z remove-existing-keys: true 2024-12-17T23:46:42.9360333Z fail-silently: true 2024-12-17T23:46:42.9360593Z env: 2024-12-17T23:46:42.9360819Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:46:42.9361107Z ##[endgroup] 2024-12-17T23:46:43.0526458Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-12-17T23:46:43.0528329Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2024-12-17T23:46:43.0650781Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@release/2.6 2024-12-17T23:46:43.0651280Z with: 2024-12-17T23:46:43.0651504Z no-sudo: true 2024-12-17T23:46:43.0651764Z submodules: recursive 2024-12-17T23:46:43.0652058Z fetch-depth: 0 2024-12-17T23:46:43.0652306Z env: 2024-12-17T23:46:43.0652527Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:46:43.0652809Z ##[endgroup] 2024-12-17T23:46:43.0735351Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:46:43.0736370Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:46:43.0745202Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:46:43.0745608Z env: 2024-12-17T23:46:43.0745830Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:46:43.0746110Z ##[endgroup] 2024-12-17T23:46:43.0835113Z ##[group]Run retry () { 2024-12-17T23:46:43.0835427Z retry () { 2024-12-17T23:46:43.0835816Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-12-17T23:46:43.0836274Z } 2024-12-17T23:46:43.0836532Z echo "${GITHUB_WORKSPACE}" 2024-12-17T23:46:43.0836876Z if [ -z "${NO_SUDO}" ]; then 2024-12-17T23:46:43.0837249Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-12-17T23:46:43.0837618Z else 2024-12-17T23:46:43.0837880Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-12-17T23:46:43.0838333Z fi 2024-12-17T23:46:43.0838588Z mkdir "${GITHUB_WORKSPACE}" 2024-12-17T23:46:43.0844689Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:46:43.0845098Z env: 2024-12-17T23:46:43.0845318Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:46:43.0845600Z NO_SUDO: true 2024-12-17T23:46:43.0845843Z ##[endgroup] 2024-12-17T23:46:43.0868036Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:46:43.0950799Z ##[group]Run malfet/checkout@silent-checkout 2024-12-17T23:46:43.0951159Z with: 2024-12-17T23:46:43.0951490Z ref: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:46:43.0951866Z fetch-depth: 0 2024-12-17T23:46:43.0952124Z submodules: recursive 2024-12-17T23:46:43.0952402Z quiet-checkout: true 2024-12-17T23:46:43.0952678Z repository: pytorch/pytorch 2024-12-17T23:46:43.0953083Z token: *** 2024-12-17T23:46:43.0953518Z ssh-strict: true 2024-12-17T23:46:43.0953785Z persist-credentials: true 2024-12-17T23:46:43.0954074Z clean: true 2024-12-17T23:46:43.0954319Z sparse-checkout-cone-mode: true 2024-12-17T23:46:43.0954628Z lfs: false 2024-12-17T23:46:43.0954873Z set-safe-directory: true 2024-12-17T23:46:43.0955150Z env: 2024-12-17T23:46:43.0955375Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:46:43.0955640Z ##[endgroup] 2024-12-17T23:46:43.1794368Z Syncing repository: pytorch/pytorch 2024-12-17T23:46:43.1795730Z ##[group]Getting Git version info 2024-12-17T23:46:43.1796261Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-12-17T23:46:43.1796972Z [command]/usr/bin/git version 2024-12-17T23:46:43.1797269Z git version 2.40.1 2024-12-17T23:46:43.1805648Z ##[endgroup] 2024-12-17T23:46:43.1817666Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/d22d8a7c-d872-4ec3-80d3-5202052c2f56' before making global git config changes 2024-12-17T23:46:43.1818690Z Adding repository directory to the temporary git global config as a safe directory 2024-12-17T23:46:43.1821144Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:46:43.1845977Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-12-17T23:46:43.1848728Z ##[group]Initializing the repository 2024-12-17T23:46:43.1851049Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:46:43.1873930Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-12-17T23:46:43.1874563Z hint: is subject to change. To configure the initial branch name to use in all 2024-12-17T23:46:43.1875158Z hint: of your new repositories, which will suppress this warning, call: 2024-12-17T23:46:43.1875597Z hint: 2024-12-17T23:46:43.1875893Z hint: git config --global init.defaultBranch 2024-12-17T23:46:43.1876260Z hint: 2024-12-17T23:46:43.1876613Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-12-17T23:46:43.1877213Z hint: 'development'. The just-created branch can be renamed via this command: 2024-12-17T23:46:43.1877675Z hint: 2024-12-17T23:46:43.1877901Z hint: git branch -m 2024-12-17T23:46:43.1878531Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-12-17T23:46:43.1884312Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-12-17T23:46:43.1905875Z ##[endgroup] 2024-12-17T23:46:43.1906341Z ##[group]Disabling automatic garbage collection 2024-12-17T23:46:43.1908520Z [command]/usr/bin/git config --local gc.auto 0 2024-12-17T23:46:43.1928814Z ##[endgroup] 2024-12-17T23:46:43.1929233Z ##[group]Setting up auth 2024-12-17T23:46:43.1933373Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-12-17T23:46:43.1953481Z [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' || :" 2024-12-17T23:46:43.2219570Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-12-17T23:46:43.2239966Z [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' || :" 2024-12-17T23:46:43.2490138Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-12-17T23:46:43.2524590Z ##[endgroup] 2024-12-17T23:46:43.2525048Z ##[group]Fetching the repository 2024-12-17T23:46:43.2530270Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --progress --no-recurse-submodules --quiet origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2024-12-17T23:46:46.1399395Z remote: Enumerating objects: 1056644 2024-12-17T23:46:46.1399822Z remote: Enumerating objects: 1057229, done. 2024-12-17T23:46:46.1401040Z remote: 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0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:47:31.7850935Z ##[endgroup] 2024-12-17T23:47:31.7851401Z ##[group]Setting up auth for fetching submodules 2024-12-17T23:47:31.7854272Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-12-17T23:47:31.7890184Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-12-17T23:47:31.7911361Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-12-17T23:47:31.7933688Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-12-17T23:47:31.7956373Z ##[endgroup] 2024-12-17T23:47:31.7956814Z ##[group]Fetching submodules 2024-12-17T23:47:31.7958513Z [command]/usr/bin/git submodule sync --recursive 2024-12-17T23:47:31.8234167Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-12-17T23:47:31.8505202Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-12-17T23:47:31.8506660Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-12-17T23:47:31.8508715Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-12-17T23:47:31.8511159Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-12-17T23:47:31.8513553Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2024-12-17T23:47:31.8516458Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-12-17T23:47:31.8518895Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-12-17T23:47:31.8521698Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-12-17T23:47:31.8524655Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2024-12-17T23:47:31.8527540Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-12-17T23:47:31.8530526Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-12-17T23:47:31.8533872Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-12-17T23:47:31.8536961Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-12-17T23:47:31.8540309Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-12-17T23:47:31.8543718Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-12-17T23:47:31.8548112Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-12-17T23:47:31.8553247Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-12-17T23:47:31.8557257Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:47:31.8561088Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-12-17T23:47:31.8565099Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-12-17T23:47:31.8569005Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-12-17T23:47:31.8573121Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-12-17T23:47:31.8577486Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-12-17T23:47:31.8581771Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-12-17T23:47:31.8586063Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-12-17T23:47:31.8590499Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-12-17T23:47:31.8595015Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-12-17T23:47:31.8600058Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-12-17T23:47:31.8604586Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-12-17T23:47:31.8609613Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-12-17T23:47:31.8614359Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-12-17T23:47:31.8619468Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-12-17T23:47:31.8624420Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-12-17T23:47:31.8630987Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-12-17T23:47:31.8636323Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-12-17T23:47:31.8641853Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-12-17T23:47:31.8668338Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-12-17T23:47:32.1447623Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-12-17T23:47:32.3185599Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-12-17T23:47:32.4930600Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-12-17T23:47:32.7217134Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2024-12-17T23:47:33.0284809Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-12-17T23:47:34.9248114Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-12-17T23:47:44.8584858Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-12-17T23:47:45.2364214Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2024-12-17T23:47:47.2553449Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-12-17T23:47:47.7479337Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-12-17T23:47:48.3332658Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-12-17T23:47:49.4536996Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-12-17T23:47:51.3860387Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-12-17T23:47:56.4110034Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-12-17T23:47:57.8725473Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-12-17T23:47:58.9508391Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-12-17T23:48:00.0647660Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-12-17T23:48:00.4619617Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-12-17T23:48:00.8159434Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-12-17T23:48:01.7928328Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-12-17T23:48:02.1443414Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-12-17T23:48:02.4300687Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-12-17T23:48:04.3358436Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-12-17T23:48:05.1663708Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-12-17T23:48:05.5111683Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-12-17T23:48:12.2364589Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-12-17T23:48:14.1069019Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-12-17T23:48:18.9189653Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-12-17T23:48:19.1418891Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-12-17T23:48:27.5553214Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-12-17T23:48:27.7551729Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-12-17T23:48:27.9487295Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-12-17T23:48:28.9204305Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-12-17T23:48:29.2186951Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-12-17T23:48:29.8122014Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-12-17T23:48:30.2577614Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-12-17T23:48:30.2690193Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-12-17T23:48:30.2775967Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-12-17T23:48:30.3010085Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-12-17T23:48:30.3336149Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2024-12-17T23:48:30.3704079Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-12-17T23:48:31.0543041Z Submodule path 'third_party/XNNPACK': checked out '4ea82e595b36106653175dcb04b2aa532660d0d8' 2024-12-17T23:48:31.0764008Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-12-17T23:48:31.2958148Z Submodule path 'third_party/composable_kernel': checked out '50ee4267e27b875d149e642f4cebd47be1dc3b57' 2024-12-17T23:48:31.3420525Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-12-17T23:48:31.4358932Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2024-12-17T23:48:31.4693803Z Submodule path 'third_party/cudnn_frontend': checked out '936021bfed8c91dc416af1588b2c4eca631a9e45' 2024-12-17T23:48:31.9505958Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-12-17T23:48:32.1928120Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-12-17T23:48:32.2840165Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-12-17T23:48:32.2856953Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:48:32.2859253Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:48:32.2861671Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:48:32.2864197Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:48:32.2866832Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:48:32.2891929Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-12-17T23:48:33.1888133Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-12-17T23:48:33.7633404Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-12-17T23:48:35.7016162Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-12-17T23:48:36.6710493Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-12-17T23:48:37.0160235Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-12-17T23:48:37.1092968Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-12-17T23:48:37.4963887Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-12-17T23:48:37.5570848Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-12-17T23:48:37.5694317Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-12-17T23:48:37.6948689Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-12-17T23:48:37.7329271Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-12-17T23:48:37.7714774Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-12-17T23:48:37.7966850Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-12-17T23:48:37.8389281Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2024-12-17T23:48:37.8514444Z Submodule path 'third_party/ideep': checked out 'c7ccd5bdbe5434ba156f4e856dcef0601637334b' 2024-12-17T23:48:37.8530285Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-12-17T23:48:37.8553140Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-12-17T23:48:51.3733954Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2024-12-17T23:48:51.3909296Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-12-17T23:48:51.4748003Z Submodule path 'third_party/kineto': checked out '338140f58a28d599da3434ced4fd2d75dd1a213d' 2024-12-17T23:48:51.4765995Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:48:51.4767972Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:48:51.4770581Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:48:51.4796458Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-12-17T23:48:52.2408617Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-12-17T23:48:53.3582288Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-12-17T23:48:54.4253619Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-12-17T23:48:54.4269230Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:48:54.4271933Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:48:54.4274386Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:48:54.4277051Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:48:54.4279860Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:48:54.4282616Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:48:54.4285490Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:48:54.4288275Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:48:54.4314843Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-12-17T23:48:55.3675962Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-12-17T23:48:55.7146103Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-12-17T23:48:56.8603933Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-12-17T23:48:57.1528921Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-12-17T23:48:57.6098189Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-12-17T23:48:58.6044365Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-12-17T23:49:05.1255626Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-12-17T23:49:05.6448435Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-12-17T23:49:05.6623018Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-12-17T23:49:05.6969851Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-12-17T23:49:05.7097124Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-12-17T23:49:05.7111809Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:49:05.7135192Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-12-17T23:49:06.2052572Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-12-17T23:49:06.2229474Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-12-17T23:49:06.2623999Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-12-17T23:49:06.3668829Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-12-17T23:49:06.3828453Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-12-17T23:49:06.4210769Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-12-17T23:49:06.4780768Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-12-17T23:49:06.5145969Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-12-17T23:49:06.5430957Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-12-17T23:49:06.6499735Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-12-17T23:49:06.9947359Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c' 2024-12-17T23:49:06.9981834Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-12-17T23:49:07.0006421Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-12-17T23:49:08.3994410Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b' 2024-12-17T23:49:08.4659991Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-12-17T23:49:08.4678194Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:49:08.4680685Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:49:08.4682994Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:49:08.4685491Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:49:08.4688312Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:49:08.4690928Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:49:08.4693601Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for 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'third_party/pocketfft' 2024-12-17T23:49:36.2627690Z Entering 'third_party/protobuf' 2024-12-17T23:49:36.2670286Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:49:36.2710177Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:49:36.2750599Z Entering 'third_party/psimd' 2024-12-17T23:49:36.2790611Z Entering 'third_party/pthreadpool' 2024-12-17T23:49:36.2830672Z Entering 'third_party/pybind11' 2024-12-17T23:49:36.2871057Z Entering 'third_party/python-peachpy' 2024-12-17T23:49:36.2911295Z Entering 'third_party/sleef' 2024-12-17T23:49:36.2950032Z Entering 'third_party/tensorpipe' 2024-12-17T23:49:36.2990081Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:49:36.3029373Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:49:36.3067708Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:49:36.3105887Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:49:36.3142874Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:49:36.3194659Z ##[endgroup] 2024-12-17T23:49:36.3229158Z [command]/usr/bin/git log -1 --format='%H' 2024-12-17T23:49:36.3247855Z '0cdf8b1d09254cfda66191d1bd01e3041c3c76f7' 2024-12-17T23:49:36.3405836Z Prepare all required actions 2024-12-17T23:49:36.3406426Z Getting action download info 2024-12-17T23:49:36.5150772Z ##[group]Run ./.github/actions/setup-linux 2024-12-17T23:49:36.5151165Z env: 2024-12-17T23:49:36.5151382Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:36.5151658Z ##[endgroup] 2024-12-17T23:49:36.5196018Z ##[group]Run set -euo pipefail 2024-12-17T23:49:36.5196401Z set -euo pipefail 2024-12-17T23:49:36.5196698Z function get_ec2_metadata() { 2024-12-17T23:49:36.5197100Z  # Pulled from instance metadata endpoint for EC2 2024-12-17T23:49:36.5197776Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-12-17T23:49:36.5198363Z  category=$1 2024-12-17T23:49:36.5198851Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-12-17T23:49:36.5199313Z  runner_name_str=i-00cc61a0ee6903cab 2024-12-17T23:49:36.5199712Z  if [[ -f /.inarc ]]; then 2024-12-17T23:49:36.5200078Z  echo "ARC Runner, no info on ec2 metadata" 2024-12-17T23:49:36.5200475Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-12-17T23:49:36.5200963Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-12-17T23:49:36.5201429Z  else 2024-12-17T23:49:36.5202325Z  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}" 2024-12-17T23:49:36.5203277Z  fi 2024-12-17T23:49:36.5203510Z } 2024-12-17T23:49:36.5203777Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-12-17T23:49:36.5204223Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-12-17T23:49:36.5204721Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-12-17T23:49:36.5205159Z echo "system info $(uname -a)" 2024-12-17T23:49:36.5213547Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:36.5213958Z env: 2024-12-17T23:49:36.5214176Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:36.5214462Z ##[endgroup] 2024-12-17T23:49:36.5347484Z ami-id: ami-0fff1b9a61dec8a5f 2024-12-17T23:49:36.5444178Z instance-id: i-00cc61a0ee6903cab 2024-12-17T23:49:36.5533199Z instance-type: c5.4xlarge 2024-12-17T23:49:36.5543373Z system info Linux ip-10-0-61-127.ec2.internal 6.1.109-118.189.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Sep 10 08:59:12 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-12-17T23:49:36.5567994Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:49:36.5568984Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:49:36.5575754Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:36.5576173Z env: 2024-12-17T23:49:36.5576394Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:36.5576674Z ##[endgroup] 2024-12-17T23:49:36.5630616Z ##[group]Run if systemctl is-active --quiet docker; then 2024-12-17T23:49:36.5631081Z if systemctl is-active --quiet docker; then 2024-12-17T23:49:36.5631491Z  echo "Docker daemon is running..."; 2024-12-17T23:49:36.5631822Z else 2024-12-17T23:49:36.5632196Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-12-17T23:49:36.5632638Z fi 2024-12-17T23:49:36.5638148Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:36.5638810Z env: 2024-12-17T23:49:36.5639037Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:36.5639303Z ##[endgroup] 2024-12-17T23:49:36.5712141Z Docker daemon is running... 2024-12-17T23:49:36.5762091Z ##[group]Run nick-fields/retry@v3.0.0 2024-12-17T23:49:36.5762561Z with: 2024-12-17T23:49:36.5762845Z shell: bash 2024-12-17T23:49:36.5763406Z timeout_minutes: 5 2024-12-17T23:49:36.5763878Z max_attempts: 3 2024-12-17T23:49:36.5764173Z retry_wait_seconds: 30 2024-12-17T23:49:36.5766547Z command: AWS_ACCOUNT_ID=$(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 2024-12-17T23:49:36.5768944Z polling_interval_seconds: 1 2024-12-17T23:49:36.5769245Z warning_on_retry: true 2024-12-17T23:49:36.5769523Z continue_on_error: false 2024-12-17T23:49:36.5769796Z env: 2024-12-17T23:49:36.5770011Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:36.5770293Z AWS_RETRY_MODE: standard 2024-12-17T23:49:36.5770566Z AWS_MAX_ATTEMPTS: 5 2024-12-17T23:49:36.5770834Z AWS_DEFAULT_REGION: us-east-1 2024-12-17T23:49:36.5771112Z ##[endgroup] 2024-12-17T23:49:37.7729454Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:49:37.7730121Z Configure a credential helper to remove this warning. See 2024-12-17T23:49:37.7731104Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:49:37.7731508Z 2024-12-17T23:49:37.7731630Z Login Succeeded 2024-12-17T23:49:38.6576335Z Command completed after 1 attempt(s). 2024-12-17T23:49:38.6631937Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:49:38.6632492Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:49:38.6632996Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:49:38.6640018Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:38.6640409Z env: 2024-12-17T23:49:38.6640637Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:38.6640914Z ##[endgroup] 2024-12-17T23:49:38.6725797Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-12-17T23:49:38.6726398Z # ignore expansion of "docker ps -q" since it could be empty 2024-12-17T23:49:38.6726849Z # shellcheck disable=SC2046 2024-12-17T23:49:38.6727189Z docker stop $(docker ps -q) || true 2024-12-17T23:49:38.6727555Z # Prune all of the docker images 2024-12-17T23:49:38.6727899Z docker system prune -af 2024-12-17T23:49:38.6733735Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:38.6734138Z env: 2024-12-17T23:49:38.6734367Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:38.6734644Z ##[endgroup] 2024-12-17T23:49:38.6984043Z "docker stop" requires at least 1 argument. 2024-12-17T23:49:38.6984450Z See 'docker stop --help'. 2024-12-17T23:49:38.6984631Z 2024-12-17T23:49:38.6984837Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-12-17T23:49:38.6985117Z 2024-12-17T23:49:38.6985243Z Stop one or more running containers 2024-12-17T23:49:38.7137174Z Total reclaimed space: 0B 2024-12-17T23:49:38.7184756Z ##[group]Run set +e 2024-12-17T23:49:38.7185061Z set +e 2024-12-17T23:49:38.7185309Z set -x 2024-12-17T23:49:38.7185536Z  2024-12-17T23:49:38.7185798Z PT_DOMAIN=download.pytorch.org 2024-12-17T23:49:38.7186414Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-12-17T23:49:38.7187335Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-12-17T23:49:38.7187892Z # one is returned at random 2024-12-17T23:49:38.7188309Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-12-17T23:49:38.7188695Z  2024-12-17T23:49:38.7189047Z if [ -z "${RESOLVED_IP}" ]; then 2024-12-17T23:49:38.7189501Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-12-17T23:49:38.7190047Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-12-17T23:49:38.7190458Z  2024-12-17T23:49:38.7190693Z  if [ -z "${RESOLVED_IP}" ]; then 2024-12-17T23:49:38.7191101Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-12-17T23:49:38.7191483Z  exit 1 2024-12-17T23:49:38.7191734Z  fi 2024-12-17T23:49:38.7191961Z fi 2024-12-17T23:49:38.7192171Z  2024-12-17T23:49:38.7192478Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-12-17T23:49:38.7192860Z  # Clean up any old records first 2024-12-17T23:49:38.7193243Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-12-17T23:49:38.7193593Z fi 2024-12-17T23:49:38.7193806Z  2024-12-17T23:49:38.7194139Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-12-17T23:49:38.7194558Z cat /etc/hosts 2024-12-17T23:49:38.7200292Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:38.7200694Z env: 2024-12-17T23:49:38.7200925Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:38.7201191Z ##[endgroup] 2024-12-17T23:49:38.7222993Z + PT_DOMAIN=download.pytorch.org 2024-12-17T23:49:38.7228158Z ++ dig -4 +short download.pytorch.org 2024-12-17T23:49:38.7228988Z ++ tail -n1 2024-12-17T23:49:38.7912330Z + RESOLVED_IP=3.167.99.70 2024-12-17T23:49:38.7912643Z + '[' -z 3.167.99.70 ']' 2024-12-17T23:49:38.7912950Z + grep -r download.pytorch.org /etc/hosts 2024-12-17T23:49:38.7926863Z + echo '3.167.99.70 download.pytorch.org' 2024-12-17T23:49:38.7927900Z + sudo tee -a /etc/hosts 2024-12-17T23:49:39.0243620Z 3.167.99.70 download.pytorch.org 2024-12-17T23:49:39.0258329Z + cat /etc/hosts 2024-12-17T23:49:39.0266222Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-12-17T23:49:39.0273172Z ::1 localhost6 localhost6.localdomain6 2024-12-17T23:49:39.0273775Z 3.167.99.70 download.pytorch.org 2024-12-17T23:49:39.0431820Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@release/2.6 2024-12-17T23:49:39.0432325Z with: 2024-12-17T23:49:39.0433017Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0433807Z docker-build-dir: .ci/docker 2024-12-17T23:49:39.0434109Z working-directory: . 2024-12-17T23:49:39.0434472Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:39.0434898Z force-push: false 2024-12-17T23:49:39.0435132Z env: 2024-12-17T23:49:39.0435353Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:39.0435630Z ##[endgroup] 2024-12-17T23:49:39.0458464Z ##[group]Run set -ex 2024-12-17T23:49:39.0458776Z set -ex 2024-12-17T23:49:39.0459022Z  2024-12-17T23:49:39.0459438Z # If the docker build directory or the build script doesn't exist, the action will 2024-12-17T23:49:39.0460188Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-12-17T23:49:39.0460771Z # job could then download the pre-built image as usual 2024-12-17T23:49:39.0461315Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-12-17T23:49:39.0461814Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0462289Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0462719Z  2024-12-17T23:49:39.0463214Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-12-17T23:49:39.0463660Z  exit 0 2024-12-17T23:49:39.0463901Z else 2024-12-17T23:49:39.0464180Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0464525Z fi 2024-12-17T23:49:39.0464754Z  2024-12-17T23:49:39.0465100Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-12-17T23:49:39.0465732Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-12-17T23:49:39.0466290Z  # use it as it is, but first let's extract the tag 2024-12-17T23:49:39.0466794Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-12-17T23:49:39.0467320Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0467832Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0468263Z else 2024-12-17T23:49:39.0468575Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-12-17T23:49:39.0469054Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0469715Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0470297Z fi 2024-12-17T23:49:39.0477013Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:39.0477415Z env: 2024-12-17T23:49:39.0477634Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:39.0477915Z REPO_NAME: pytorch 2024-12-17T23:49:39.0478708Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0479490Z DOCKER_BUILD_DIR: .ci/docker 2024-12-17T23:49:39.0479877Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:39.0480273Z ##[endgroup] 2024-12-17T23:49:39.0504044Z + [[ ! -d .ci/docker ]] 2024-12-17T23:49:39.0504346Z + [[ ! -f .ci/docker/build.sh ]] 2024-12-17T23:49:39.0504651Z + echo skip=false 2024-12-17T23:49:39.0505642Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 == *\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* ]] 2024-12-17T23:49:39.0510673Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0511405Z ++ awk -F '[:,]' '{print $2}' 2024-12-17T23:49:39.0530265Z + DOCKER_TAG=45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0530726Z + echo docker-tag=45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0531564Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0558323Z ##[group]Run set +e 2024-12-17T23:49:39.0558778Z set +e 2024-12-17T23:49:39.0559030Z set -x 2024-12-17T23:49:39.0559272Z  2024-12-17T23:49:39.0559486Z login() { 2024-12-17T23:49:39.0559992Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-12-17T23:49:39.0560537Z } 2024-12-17T23:49:39.0560763Z  2024-12-17T23:49:39.0560984Z retry () { 2024-12-17T23:49:39.0561272Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-12-17T23:49:39.0561605Z } 2024-12-17T23:49:39.0561829Z  2024-12-17T23:49:39.0562076Z retry login "${DOCKER_REGISTRY}" 2024-12-17T23:49:39.0562402Z  2024-12-17T23:49:39.0562622Z START_TIME=$(date +%s) 2024-12-17T23:49:39.0562932Z # Wait up to 90 minutes 2024-12-17T23:49:39.0563313Z while [[ $(( $(date +%s) - 5400 )) -lt $START_TIME ]]; do 2024-12-17T23:49:39.0563972Z  # Check if image already exists, if it does then skip building it 2024-12-17T23:49:39.0564490Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-12-17T23:49:39.0564873Z  exit 0 2024-12-17T23:49:39.0565111Z  fi 2024-12-17T23:49:39.0565347Z  2024-12-17T23:49:39.0565751Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2024-12-17T23:49:39.0566448Z  # use this to differentiate between the Docker build and regular build jobs. For the 2024-12-17T23:49:39.0567139Z  # latter, it will wait for the Docker images to become available before continuing 2024-12-17T23:49:39.0567680Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2024-12-17T23:49:39.0568085Z  # It's a Docker build job, let's build the image 2024-12-17T23:49:39.0568448Z  break 2024-12-17T23:49:39.0568695Z  else 2024-12-17T23:49:39.0569054Z  # It's a regular build job, wait for the image to become available 2024-12-17T23:49:39.0569488Z  sleep 300 2024-12-17T23:49:39.0569737Z  fi 2024-12-17T23:49:39.0569969Z done 2024-12-17T23:49:39.0570199Z  2024-12-17T23:49:39.0570563Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-12-17T23:49:39.0571156Z # be empty. The default action would be to continue rebuild the image 2024-12-17T23:49:39.0571678Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-12-17T23:49:39.0572153Z  # if we're on the base branch then use the parent commit 2024-12-17T23:49:39.0572570Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-12-17T23:49:39.0572901Z else 2024-12-17T23:49:39.0573242Z  # otherwise we're on a PR, so use the most recent base commit 2024-12-17T23:49:39.0573960Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-12-17T23:49:39.0574333Z fi 2024-12-17T23:49:39.0574564Z  2024-12-17T23:49:39.0574810Z if [[ -z "${MERGE_BASE}" ]]; then 2024-12-17T23:49:39.0575191Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0575543Z  2024-12-17T23:49:39.0576018Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-12-17T23:49:39.0576596Z  exit 0 2024-12-17T23:49:39.0576844Z fi 2024-12-17T23:49:39.0577199Z  2024-12-17T23:49:39.0577523Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-12-17T23:49:39.0578236Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-12-17T23:49:39.0578833Z  exit 1 2024-12-17T23:49:39.0579074Z fi 2024-12-17T23:49:39.0579297Z  2024-12-17T23:49:39.0579675Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-12-17T23:49:39.0580360Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-12-17T23:49:39.0580961Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-12-17T23:49:39.0581668Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-12-17T23:49:39.0582464Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-12-17T23:49:39.0582946Z fi 2024-12-17T23:49:39.0583174Z  2024-12-17T23:49:39.0583452Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:39.0588780Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:39.0589168Z env: 2024-12-17T23:49:39.0589402Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:39.0589692Z DOCKER_BUILD_DIR: .ci/docker 2024-12-17T23:49:39.0590053Z BASE_REVISION: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:39.0591012Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0591812Z DOCKER_TAG: 45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.0592258Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:39.0592661Z DOCKER_PUSH: 2024-12-17T23:49:39.0592907Z ##[endgroup] 2024-12-17T23:49:39.0614115Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:39.0614590Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:39.0616605Z + aws ecr get-login-password --region us-east-1 2024-12-17T23:49:39.0618107Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:39.6092823Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:49:39.6093461Z Configure a credential helper to remove this warning. See 2024-12-17T23:49:39.6094084Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:49:39.6094630Z 2024-12-17T23:49:39.6094736Z Login Succeeded 2024-12-17T23:49:39.6108219Z ++ date +%s 2024-12-17T23:49:39.6117013Z + START_TIME=1734479379 2024-12-17T23:49:39.6119893Z ++ date +%s 2024-12-17T23:49:39.6127758Z + [[ 1734473979 -lt 1734479379 ]] 2024-12-17T23:49:39.6128659Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:39.8500574Z { 2024-12-17T23:49:39.8500895Z "schemaVersion": 2, 2024-12-17T23:49:39.8501366Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-12-17T23:49:39.8501822Z "config": { 2024-12-17T23:49:39.8502180Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-12-17T23:49:39.8502605Z "size": 41580, 2024-12-17T23:49:39.8503036Z "digest": "sha256:bca7896c0150bd62bb439228906ce331a02b4d3cd9ef140e4946794ede2ac12d" 2024-12-17T23:49:39.8503568Z }, 2024-12-17T23:49:39.8503770Z "layers": [ 2024-12-17T23:49:39.8503998Z { 2024-12-17T23:49:39.8504344Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8504788Z "size": 28583948, 2024-12-17T23:49:39.8505223Z "digest": "sha256:86e5016c269355b382c9cabab4f6646d56d75914f20d545289970436dae431b1" 2024-12-17T23:49:39.8505697Z }, 2024-12-17T23:49:39.8505905Z { 2024-12-17T23:49:39.8506478Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8506915Z "size": 1825, 2024-12-17T23:49:39.8507352Z "digest": "sha256:aec9e3dc3b214d1977be2640618dfe86f312acecc8ee5a35eac1e27caba083da" 2024-12-17T23:49:39.8507862Z }, 2024-12-17T23:49:39.8508068Z { 2024-12-17T23:49:39.8508404Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8508836Z "size": 313456486, 2024-12-17T23:49:39.8509275Z "digest": "sha256:c6543eb4cb51a83a4cf9224d08038ae94174bceee64de8b104164118d2c985a1" 2024-12-17T23:49:39.8509754Z }, 2024-12-17T23:49:39.8509958Z { 2024-12-17T23:49:39.8510291Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8510719Z "size": 864, 2024-12-17T23:49:39.8511130Z "digest": "sha256:71f87d3b18fa2a22e22b909afc30e4a8ba8181bc5089ed9f2e3f86d40d24bd4e" 2024-12-17T23:49:39.8511626Z }, 2024-12-17T23:49:39.8511828Z { 2024-12-17T23:49:39.8512167Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8512607Z "size": 79405092, 2024-12-17T23:49:39.8513041Z "digest": "sha256:c6ad7e035002e6ff80ebd83ce0bf3cb9db51f553cac3b74f1ea19486a19b6dbf" 2024-12-17T23:49:39.8513545Z }, 2024-12-17T23:49:39.8513752Z { 2024-12-17T23:49:39.8514092Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8514552Z "size": 704, 2024-12-17T23:49:39.8514962Z "digest": "sha256:28a6604dc7e22554839ce9c1c94b72a436d8caa1a96bf90c37adf00afa4ca0d4" 2024-12-17T23:49:39.8515450Z }, 2024-12-17T23:49:39.8515760Z { 2024-12-17T23:49:39.8516096Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8516527Z "size": 1260, 2024-12-17T23:49:39.8516923Z "digest": "sha256:9a955de2200893769521154542f692c4cf210566dbd5e5347075d3de812ca1c6" 2024-12-17T23:49:39.8517399Z }, 2024-12-17T23:49:39.8517603Z { 2024-12-17T23:49:39.8517944Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8518379Z "size": 484, 2024-12-17T23:49:39.8518921Z "digest": "sha256:c704eb5496d38fe253dab9a721fcda4b6241c9312b3e7d89158760b781451f23" 2024-12-17T23:49:39.8519413Z }, 2024-12-17T23:49:39.8519620Z { 2024-12-17T23:49:39.8519959Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8520393Z "size": 110, 2024-12-17T23:49:39.8520802Z "digest": "sha256:3e321eab5dfaf17692f88bdf4e0e6ec2d75cc319f474d9e564d1f29988fa23b4" 2024-12-17T23:49:39.8521294Z }, 2024-12-17T23:49:39.8521494Z { 2024-12-17T23:49:39.8521824Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8522258Z "size": 4152, 2024-12-17T23:49:39.8522664Z "digest": "sha256:df85228900459b006599c7caa7fe3cf6d07ecf17c81f45036ff33f47322c52af" 2024-12-17T23:49:39.8523148Z }, 2024-12-17T23:49:39.8523347Z { 2024-12-17T23:49:39.8523680Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8524107Z "size": 1860, 2024-12-17T23:49:39.8524525Z "digest": "sha256:6efa7ba6eaf91bc340fa98abb452844d2c53ee52dc1122056bd399b887ed75df" 2024-12-17T23:49:39.8525015Z }, 2024-12-17T23:49:39.8525215Z { 2024-12-17T23:49:39.8525547Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8525971Z "size": 701, 2024-12-17T23:49:39.8526375Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:49:39.8526859Z }, 2024-12-17T23:49:39.8527060Z { 2024-12-17T23:49:39.8527395Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8527825Z "size": 479, 2024-12-17T23:49:39.8528233Z "digest": "sha256:8dbdd9e962a97ea1ee0dd73599b1a4f82c377975d1138b2cf2c5e83702e21bbd" 2024-12-17T23:49:39.8528720Z }, 2024-12-17T23:49:39.8528924Z { 2024-12-17T23:49:39.8529257Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8529689Z "size": 2884231269, 2024-12-17T23:49:39.8530254Z "digest": "sha256:45260f60bb0a474a8aab43cf3eaf58799b9c24a3c40cbd7df1addfe8ff59b4e3" 2024-12-17T23:49:39.8530753Z }, 2024-12-17T23:49:39.8530963Z { 2024-12-17T23:49:39.8531299Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8531731Z "size": 32, 2024-12-17T23:49:39.8532143Z "digest": 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"sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8642336Z }, 2024-12-17T23:49:39.8642525Z { 2024-12-17T23:49:39.8642857Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8643284Z "size": 161, 2024-12-17T23:49:39.8643698Z "digest": "sha256:4699807ef69f0d61ab68f825da259c68cb093b18af932839fc36c93e00203139" 2024-12-17T23:49:39.8644265Z }, 2024-12-17T23:49:39.8644456Z { 2024-12-17T23:49:39.8644796Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8645228Z "size": 682, 2024-12-17T23:49:39.8645646Z "digest": "sha256:633926ef19787d7d6a27cb1f248dd3e8601a0d4e77a7b5a88b7173cc958e46f3" 2024-12-17T23:49:39.8646133Z }, 2024-12-17T23:49:39.8646328Z { 2024-12-17T23:49:39.8646663Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8647087Z "size": 701, 2024-12-17T23:49:39.8647509Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:49:39.8647995Z }, 2024-12-17T23:49:39.8648184Z { 2024-12-17T23:49:39.8648518Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8648944Z "size": 138, 2024-12-17T23:49:39.8649355Z "digest": "sha256:4dd1e4338891022059638c6ea550e879ba0f49e323740b7f4566567de12c90ba" 2024-12-17T23:49:39.8649835Z }, 2024-12-17T23:49:39.8650030Z { 2024-12-17T23:49:39.8650367Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8650796Z "size": 32, 2024-12-17T23:49:39.8651218Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8651706Z }, 2024-12-17T23:49:39.8651894Z { 2024-12-17T23:49:39.8652229Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8652659Z "size": 159, 2024-12-17T23:49:39.8653076Z "digest": "sha256:fafe335b683238800f721cab6383022f05d8214d09a8da384f393faa5a47a065" 2024-12-17T23:49:39.8653647Z }, 2024-12-17T23:49:39.8653841Z { 2024-12-17T23:49:39.8654182Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8654613Z "size": 907, 2024-12-17T23:49:39.8655042Z "digest": "sha256:7b96d6fc18dbf9a8cd85ae285775fe5cb88151abd34a1ec34d55963c185836a8" 2024-12-17T23:49:39.8655542Z }, 2024-12-17T23:49:39.8655734Z { 2024-12-17T23:49:39.8656073Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8656505Z "size": 701, 2024-12-17T23:49:39.8656924Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:49:39.8657414Z }, 2024-12-17T23:49:39.8657605Z { 2024-12-17T23:49:39.8657935Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8658363Z "size": 135, 2024-12-17T23:49:39.8658786Z "digest": "sha256:5eb2aa388cbde039dc7f3a6a21bbf5862212c3f99e3188382acce5c4d1c7f574" 2024-12-17T23:49:39.8659280Z }, 2024-12-17T23:49:39.8659468Z { 2024-12-17T23:49:39.8659801Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8660228Z "size": 32, 2024-12-17T23:49:39.8660646Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8661135Z }, 2024-12-17T23:49:39.8661327Z { 2024-12-17T23:49:39.8661668Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8662097Z "size": 159, 2024-12-17T23:49:39.8662511Z "digest": "sha256:6647884d3ec59ea0f46ec545856797837a5fe92da31d679138d5d8a356cf9990" 2024-12-17T23:49:39.8662993Z }, 2024-12-17T23:49:39.8663185Z { 2024-12-17T23:49:39.8663518Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8663943Z "size": 1519, 2024-12-17T23:49:39.8664366Z "digest": "sha256:fb0d52d47e5d449f8b26c0e67df88e5eafd8275912b9d61a47d100fcee2b364c" 2024-12-17T23:49:39.8664863Z }, 2024-12-17T23:49:39.8665052Z { 2024-12-17T23:49:39.8665383Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8665809Z "size": 32, 2024-12-17T23:49:39.8666229Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8666719Z }, 2024-12-17T23:49:39.8666910Z { 2024-12-17T23:49:39.8667243Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8667738Z "size": 137, 2024-12-17T23:49:39.8668170Z "digest": "sha256:2e082b79862beb2f9fbaf83954fb4c9dd0c4fe7d53c33f3b5275d549599aad8c" 2024-12-17T23:49:39.8668664Z }, 2024-12-17T23:49:39.8668860Z { 2024-12-17T23:49:39.8669198Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8669629Z "size": 379, 2024-12-17T23:49:39.8670063Z "digest": "sha256:a649e2ecceb3b5dfa0ef623faea2c287bf95eb5dc3757ba847ced37aa97f7cf7" 2024-12-17T23:49:39.8670551Z }, 2024-12-17T23:49:39.8670753Z { 2024-12-17T23:49:39.8671091Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8671517Z "size": 32, 2024-12-17T23:49:39.8671936Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8672412Z }, 2024-12-17T23:49:39.8672612Z { 2024-12-17T23:49:39.8672947Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8673371Z "size": 104, 2024-12-17T23:49:39.8674010Z "digest": "sha256:0f48fbbac9ba4b9e5a957f8bcd8ae7b540b18cd34e64cdaf5fdc949c27909169" 2024-12-17T23:49:39.8674504Z }, 2024-12-17T23:49:39.8674714Z { 2024-12-17T23:49:39.8675049Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8675479Z "size": 1932, 2024-12-17T23:49:39.8675904Z "digest": "sha256:d0ca2167ad3f44d47ddf0c04913584aa7168e753b77ed9f0b9140d55764099d3" 2024-12-17T23:49:39.8676378Z }, 2024-12-17T23:49:39.8676575Z { 2024-12-17T23:49:39.8676906Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8677464Z "size": 234174408, 2024-12-17T23:49:39.8677902Z "digest": "sha256:a7966f3017643e03d22935c9ebc74f40f62db1ee412e68aa7c274fb2cee73745" 2024-12-17T23:49:39.8678372Z }, 2024-12-17T23:49:39.8678633Z { 2024-12-17T23:49:39.8678972Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8679404Z "size": 106, 2024-12-17T23:49:39.8679822Z "digest": "sha256:5a7317de868d498389a798f3d564217b316747791255113df27e652c22ecbb23" 2024-12-17T23:49:39.8680280Z }, 2024-12-17T23:49:39.8680483Z { 2024-12-17T23:49:39.8680818Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8681245Z "size": 165, 2024-12-17T23:49:39.8681665Z "digest": "sha256:fe1994ffc6714ff248de0f7f85378ea8c5b9132f3be7d3fd9c7531a61738e48f" 2024-12-17T23:49:39.8682144Z }, 2024-12-17T23:49:39.8682347Z { 2024-12-17T23:49:39.8682681Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8683114Z "size": 7943, 2024-12-17T23:49:39.8683529Z "digest": "sha256:c5d0d5389f3b75505f972b820e171b25fa7277a75f3386e745e16fa04467dc6b" 2024-12-17T23:49:39.8683996Z }, 2024-12-17T23:49:39.8684197Z { 2024-12-17T23:49:39.8684532Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8684960Z "size": 8070, 2024-12-17T23:49:39.8685387Z "digest": "sha256:642c500944e63596bfc4e43fd9f7aea7b12098519eb254472a00f7fb8e6de3d7" 2024-12-17T23:49:39.8685857Z }, 2024-12-17T23:49:39.8686059Z { 2024-12-17T23:49:39.8686394Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8686810Z "size": 304, 2024-12-17T23:49:39.8687224Z "digest": "sha256:990f6a099a4741eec3ed17414d2fb550f632f9fbb4c361be5729f35b0e132be2" 2024-12-17T23:49:39.8687705Z }, 2024-12-17T23:49:39.8687905Z { 2024-12-17T23:49:39.8688233Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8688644Z "size": 32, 2024-12-17T23:49:39.8689065Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8689549Z }, 2024-12-17T23:49:39.8689748Z { 2024-12-17T23:49:39.8690078Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8690489Z "size": 108, 2024-12-17T23:49:39.8690896Z "digest": "sha256:c5d5ea84cde4e2d1bd30fe4191995c8c0fd57093728055818d2fd1960aa8c608" 2024-12-17T23:49:39.8691379Z }, 2024-12-17T23:49:39.8691670Z { 2024-12-17T23:49:39.8692003Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8692418Z "size": 54145664, 2024-12-17T23:49:39.8692852Z "digest": "sha256:32258a594a306cdb6b4d7164dd7fa649727be4bc32b05edab4b1b19a9e84aeae" 2024-12-17T23:49:39.8693343Z }, 2024-12-17T23:49:39.8693540Z { 2024-12-17T23:49:39.8693868Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:49:39.8694280Z "size": 32, 2024-12-17T23:49:39.8694693Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:49:39.8695186Z } 2024-12-17T23:49:39.8695385Z ] 2024-12-17T23:49:39.8695577Z } 2024-12-17T23:49:39.8726635Z ##[group]Run set -eux 2024-12-17T23:49:39.8726938Z set -eux 2024-12-17T23:49:39.8727867Z 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 2024-12-17T23:49:39.8734313Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:39.8734708Z env: 2024-12-17T23:49:39.8734923Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:39.8735202Z ##[endgroup] 2024-12-17T23:49:39.8760873Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2024-12-17T23:49:39.8761664Z + jq --raw-output .SecretString 2024-12-17T23:49:39.8762830Z + jq -r .docker_hub_readonly_token 2024-12-17T23:49:39.8763904Z + docker login --username pytorchbot --password-stdin 2024-12-17T23:49:40.4923526Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:49:40.4924256Z Configure a credential helper to remove this warning. See 2024-12-17T23:49:40.4924944Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:49:40.4925358Z 2024-12-17T23:49:40.4925481Z Login Succeeded 2024-12-17T23:49:40.5004507Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-12-17T23:49:40.5004887Z tag=${ECR_DOCKER_IMAGE##*/} 2024-12-17T23:49:40.5005311Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-12-17T23:49:40.5011287Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:40.5011703Z env: 2024-12-17T23:49:40.5011934Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:40.5012710Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:40.5013477Z ##[endgroup] 2024-12-17T23:49:40.5038997Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.13-clang10-45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:40.5084840Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@release/2.6 2024-12-17T23:49:40.5085360Z with: 2024-12-17T23:49:40.5086027Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:40.5086904Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:40.5087335Z env: 2024-12-17T23:49:40.5087561Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:40.5087836Z ##[endgroup] 2024-12-17T23:49:40.5107418Z ##[group]Run set -x 2024-12-17T23:49:40.5107706Z set -x 2024-12-17T23:49:40.5107948Z set +e 2024-12-17T23:49:40.5108169Z  2024-12-17T23:49:40.5108393Z login() { 2024-12-17T23:49:40.5108888Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-12-17T23:49:40.5109445Z } 2024-12-17T23:49:40.5109667Z  2024-12-17T23:49:40.5109933Z retry () { 2024-12-17T23:49:40.5110218Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-12-17T23:49:40.5110536Z } 2024-12-17T23:49:40.5110756Z  2024-12-17T23:49:40.5111002Z retry login "${DOCKER_REGISTRY}" 2024-12-17T23:49:40.5111327Z  2024-12-17T23:49:40.5111544Z set -e 2024-12-17T23:49:40.5111887Z # ignore output since only exit code is used for conditional 2024-12-17T23:49:40.5112396Z # only pull docker image if it's not available locally 2024-12-17T23:49:40.5112961Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-12-17T23:49:40.5113484Z  retry docker pull "${DOCKER_IMAGE}" 2024-12-17T23:49:40.5113822Z fi 2024-12-17T23:49:40.5119107Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:40.5119511Z env: 2024-12-17T23:49:40.5119755Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:40.5120485Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:40.5121336Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:40.5121744Z ##[endgroup] 2024-12-17T23:49:40.5143410Z + set +e 2024-12-17T23:49:40.5144159Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:40.5144660Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:40.5146793Z + aws ecr get-login-password --region us-east-1 2024-12-17T23:49:40.5148136Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:49:41.0790173Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:49:41.0790822Z Configure a credential helper to remove this warning. See 2024-12-17T23:49:41.0791691Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:49:41.0792086Z 2024-12-17T23:49:41.0792207Z Login Succeeded 2024-12-17T23:49:41.0802628Z + set -e 2024-12-17T23:49:41.0803736Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:41.0930090Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:41.0931319Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:41.3559630Z 45e1356b47a284893081276eff3000b7b534f3b1: Pulling from pytorch/pytorch-linux-focal-py3.13-clang10 2024-12-17T23:49:41.3560792Z 86e5016c2693: Pulling fs layer 2024-12-17T23:49:41.3561325Z aec9e3dc3b21: Pulling fs layer 2024-12-17T23:49:41.3561823Z c6543eb4cb51: Pulling fs layer 2024-12-17T23:49:41.3562363Z 71f87d3b18fa: Pulling fs layer 2024-12-17T23:49:41.3562897Z c6ad7e035002: Pulling fs layer 2024-12-17T23:49:41.3563720Z 28a6604dc7e2: Pulling fs layer 2024-12-17T23:49:41.3564265Z 9a955de22008: Pulling fs layer 2024-12-17T23:49:41.3564740Z c704eb5496d3: Pulling fs layer 2024-12-17T23:49:41.3565268Z 3e321eab5dfa: Pulling fs layer 2024-12-17T23:49:41.3565773Z df8522890045: Pulling fs layer 2024-12-17T23:49:41.3566297Z 6efa7ba6eaf9: Pulling fs layer 2024-12-17T23:49:41.3566804Z f29844292cf7: Pulling fs layer 2024-12-17T23:49:41.3567275Z 8dbdd9e962a9: Pulling fs layer 2024-12-17T23:49:41.3567796Z 45260f60bb0a: Pulling fs layer 2024-12-17T23:49:41.3568305Z 4f4fb700ef54: Pulling fs layer 2024-12-17T23:49:41.3568797Z 28a6604dc7e2: Waiting 2024-12-17T23:49:41.3569238Z 3668f833b0e4: Pulling fs layer 2024-12-17T23:49:41.3569679Z c704eb5496d3: Waiting 2024-12-17T23:49:41.3569943Z aa4035866598: Pulling fs layer 2024-12-17T23:49:41.3570227Z c6ad7e035002: Waiting 2024-12-17T23:49:41.3570500Z 2d39f659a403: Pulling fs layer 2024-12-17T23:49:41.3570781Z 4f4fb700ef54: Waiting 2024-12-17T23:49:41.3571042Z cb7d736bf0d8: Pulling fs layer 2024-12-17T23:49:41.3571375Z 71f87d3b18fa: Waiting 2024-12-17T23:49:41.3571628Z 45260f60bb0a: Waiting 2024-12-17T23:49:41.3571890Z 3cc97a252241: Pulling fs layer 2024-12-17T23:49:41.3572188Z d901eaf7f5a0: Pulling fs layer 2024-12-17T23:49:41.3572475Z 3668f833b0e4: Waiting 2024-12-17T23:49:41.3572726Z c8b4328c0ae1: Pulling fs layer 2024-12-17T23:49:41.3573006Z 2d39f659a403: Waiting 2024-12-17T23:49:41.3573265Z c3c70a336d7d: Pulling fs layer 2024-12-17T23:49:41.3573546Z 3cc97a252241: Waiting 2024-12-17T23:49:41.3574013Z 0e26764e2949: Pulling fs layer 2024-12-17T23:49:41.3574286Z cb7d736bf0d8: Waiting 2024-12-17T23:49:41.3574545Z d901eaf7f5a0: Waiting 2024-12-17T23:49:41.3574807Z 12920fcbf355: Pulling fs layer 2024-12-17T23:49:41.3575100Z 3b8143344aee: Pulling fs layer 2024-12-17T23:49:41.3575378Z ad803458908b: Pulling fs layer 2024-12-17T23:49:41.3575666Z 8dbdd9e962a9: Waiting 2024-12-17T23:49:41.3575931Z d4e3b9c27d53: Pulling fs layer 2024-12-17T23:49:41.3576211Z f29844292cf7: Waiting 2024-12-17T23:49:41.3576476Z 0704e0edafd4: Pulling fs layer 2024-12-17T23:49:41.3576758Z eff65b2f6bc4: Pulling fs layer 2024-12-17T23:49:41.3577038Z 0e26764e2949: Waiting 2024-12-17T23:49:41.3577290Z c3d37a289144: Pulling fs layer 2024-12-17T23:49:41.3577578Z 8a082c3c11ca: Pulling fs layer 2024-12-17T23:49:41.3577855Z ad803458908b: Waiting 2024-12-17T23:49:41.3578092Z 12920fcbf355: Waiting 2024-12-17T23:49:41.3578340Z 3b8143344aee: Waiting 2024-12-17T23:49:41.3578666Z 5f373dd245ee: Pulling fs layer 2024-12-17T23:49:41.3578952Z 3e321eab5dfa: Waiting 2024-12-17T23:49:41.3579202Z 9a955de22008: Waiting 2024-12-17T23:49:41.3579437Z 8a082c3c11ca: Waiting 2024-12-17T23:49:41.3579685Z 5f373dd245ee: Waiting 2024-12-17T23:49:41.3579952Z f81fdfe75589: Pulling fs layer 2024-12-17T23:49:41.3580248Z 57a000ae2392: Pulling fs layer 2024-12-17T23:49:41.3580536Z 0704e0edafd4: Waiting 2024-12-17T23:49:41.3580948Z f81fdfe75589: Waiting 2024-12-17T23:49:41.3581212Z 0bcf8b6039ec: Pulling fs layer 2024-12-17T23:49:41.3581496Z 57a000ae2392: Waiting 2024-12-17T23:49:41.3581769Z 29eb39eca539: Pulling fs layer 2024-12-17T23:49:41.3582049Z a4e958f84c25: Pulling fs layer 2024-12-17T23:49:41.3582343Z ea68936d1a73: Pulling fs layer 2024-12-17T23:49:41.3582631Z 7745f672cbb1: Pulling fs layer 2024-12-17T23:49:41.3582915Z ea68936d1a73: Waiting 2024-12-17T23:49:41.3583176Z a69e77d01a8c: Pulling fs layer 2024-12-17T23:49:41.3583455Z c478133b8a8b: Pulling fs layer 2024-12-17T23:49:41.3583736Z 6efa7ba6eaf9: Waiting 2024-12-17T23:49:41.3584001Z c56df19c4d1f: Pulling fs layer 2024-12-17T23:49:41.3584286Z c478133b8a8b: Waiting 2024-12-17T23:49:41.3584547Z c6619167a3e6: Pulling fs layer 2024-12-17T23:49:41.3584833Z 7dbfc0fcaa06: Pulling fs layer 2024-12-17T23:49:41.3585121Z 29eb39eca539: Waiting 2024-12-17T23:49:41.3585384Z 88d2b1d09ff6: Pulling fs layer 2024-12-17T23:49:41.3585675Z 74215dfb05b9: Pulling fs layer 2024-12-17T23:49:41.3585964Z 0bcf8b6039ec: Waiting 2024-12-17T23:49:41.3586203Z 74215dfb05b9: Waiting 2024-12-17T23:49:41.3586459Z e2f8bba90803: Pulling fs layer 2024-12-17T23:49:41.3586870Z 56e66f0db375: Pulling fs layer 2024-12-17T23:49:41.3587159Z 5e47d6303582: Pulling fs layer 2024-12-17T23:49:41.3587466Z c6619167a3e6: Waiting 2024-12-17T23:49:41.3587730Z f14b36f011bf: Pulling fs layer 2024-12-17T23:49:41.3588020Z 88d2b1d09ff6: Waiting 2024-12-17T23:49:41.3588273Z 5e47d6303582: Waiting 2024-12-17T23:49:41.3588516Z 56e66f0db375: Waiting 2024-12-17T23:49:41.3588782Z f40f5582e662: Pulling fs layer 2024-12-17T23:49:41.3589080Z 4699807ef69f: Pulling fs layer 2024-12-17T23:49:41.3589376Z 633926ef1978: Pulling fs layer 2024-12-17T23:49:41.3589664Z e2f8bba90803: Waiting 2024-12-17T23:49:41.3589916Z 4dd1e4338891: Pulling fs layer 2024-12-17T23:49:41.3590202Z 633926ef1978: Waiting 2024-12-17T23:49:41.3590455Z 4699807ef69f: Waiting 2024-12-17T23:49:41.3590720Z fafe335b6832: Pulling fs layer 2024-12-17T23:49:41.3591012Z 7b96d6fc18db: Pulling fs layer 2024-12-17T23:49:41.3591315Z 5eb2aa388cbd: Pulling fs layer 2024-12-17T23:49:41.3591603Z 7b96d6fc18db: Waiting 2024-12-17T23:49:41.3591872Z 6647884d3ec5: Pulling fs layer 2024-12-17T23:49:41.3592168Z fb0d52d47e5d: Pulling fs layer 2024-12-17T23:49:41.3592442Z 6647884d3ec5: Waiting 2024-12-17T23:49:41.3592701Z 2e082b79862b: Pulling fs layer 2024-12-17T23:49:41.3592997Z a649e2ecceb3: Pulling fs layer 2024-12-17T23:49:41.3593278Z a649e2ecceb3: Waiting 2024-12-17T23:49:41.3593538Z 0f48fbbac9ba: Pulling fs layer 2024-12-17T23:49:41.3593810Z fb0d52d47e5d: Waiting 2024-12-17T23:49:41.3594070Z d0ca2167ad3f: Pulling fs layer 2024-12-17T23:49:41.3594359Z a7966f301764: Pulling fs layer 2024-12-17T23:49:41.3594638Z 0f48fbbac9ba: Waiting 2024-12-17T23:49:41.3594897Z 5a7317de868d: Pulling fs layer 2024-12-17T23:49:41.3595179Z fe1994ffc671: Pulling fs layer 2024-12-17T23:49:41.3595459Z 5a7317de868d: Waiting 2024-12-17T23:49:41.3595721Z c5d0d5389f3b: Pulling fs layer 2024-12-17T23:49:41.3596017Z 642c500944e6: Pulling fs layer 2024-12-17T23:49:41.3596311Z 990f6a099a47: Pulling fs layer 2024-12-17T23:49:41.3596580Z c5d0d5389f3b: Waiting 2024-12-17T23:49:41.3596850Z c5d5ea84cde4: Pulling fs layer 2024-12-17T23:49:41.3597144Z 32258a594a30: Pulling fs layer 2024-12-17T23:49:41.3597421Z 642c500944e6: Waiting 2024-12-17T23:49:41.3597657Z fe1994ffc671: Waiting 2024-12-17T23:49:41.3597909Z 990f6a099a47: Waiting 2024-12-17T23:49:41.3598159Z c5d5ea84cde4: Waiting 2024-12-17T23:49:41.3598487Z 32258a594a30: Waiting 2024-12-17T23:49:41.4265055Z aec9e3dc3b21: Download complete 2024-12-17T23:49:41.5157490Z 71f87d3b18fa: Download complete 2024-12-17T23:49:41.6889045Z 86e5016c2693: Download complete 2024-12-17T23:49:41.7573529Z 28a6604dc7e2: Verifying Checksum 2024-12-17T23:49:41.7574152Z 28a6604dc7e2: Download complete 2024-12-17T23:49:41.8476191Z 9a955de22008: Download complete 2024-12-17T23:49:41.9225716Z c704eb5496d3: Verifying Checksum 2024-12-17T23:49:41.9226102Z c704eb5496d3: Download complete 2024-12-17T23:49:42.0332185Z 3e321eab5dfa: Verifying Checksum 2024-12-17T23:49:42.0332567Z 3e321eab5dfa: Download complete 2024-12-17T23:49:42.1089244Z df8522890045: Verifying Checksum 2024-12-17T23:49:42.1089631Z df8522890045: Download complete 2024-12-17T23:49:42.2239828Z 6efa7ba6eaf9: Verifying Checksum 2024-12-17T23:49:42.2240223Z 6efa7ba6eaf9: Download complete 2024-12-17T23:49:42.3015585Z f29844292cf7: Verifying Checksum 2024-12-17T23:49:42.3015962Z f29844292cf7: Download complete 2024-12-17T23:49:42.3692715Z c6ad7e035002: Verifying Checksum 2024-12-17T23:49:42.3693209Z c6ad7e035002: Download complete 2024-12-17T23:49:42.3920543Z 8dbdd9e962a9: Verifying Checksum 2024-12-17T23:49:42.3920931Z 8dbdd9e962a9: Download complete 2024-12-17T23:49:42.4159977Z 4f4fb700ef54: Verifying Checksum 2024-12-17T23:49:42.4160311Z 4f4fb700ef54: Download complete 2024-12-17T23:49:42.4576276Z 86e5016c2693: Pull complete 2024-12-17T23:49:42.4791186Z aec9e3dc3b21: Pull complete 2024-12-17T23:49:42.5177276Z 3668f833b0e4: Verifying Checksum 2024-12-17T23:49:42.5177753Z 3668f833b0e4: Download complete 2024-12-17T23:49:42.6240320Z aa4035866598: Verifying Checksum 2024-12-17T23:49:42.6241134Z aa4035866598: Download complete 2024-12-17T23:49:42.7028992Z 2d39f659a403: Verifying Checksum 2024-12-17T23:49:42.7029374Z 2d39f659a403: Download complete 2024-12-17T23:49:42.8098775Z cb7d736bf0d8: Verifying Checksum 2024-12-17T23:49:42.8099322Z cb7d736bf0d8: Download complete 2024-12-17T23:49:42.8867170Z 3cc97a252241: Verifying Checksum 2024-12-17T23:49:42.8867561Z 3cc97a252241: Download complete 2024-12-17T23:49:42.9620081Z d901eaf7f5a0: Verifying Checksum 2024-12-17T23:49:43.0368920Z c8b4328c0ae1: Verifying Checksum 2024-12-17T23:49:43.0369324Z c8b4328c0ae1: Download complete 2024-12-17T23:49:43.1005915Z c3c70a336d7d: Download complete 2024-12-17T23:49:43.1964874Z 0e26764e2949: Verifying Checksum 2024-12-17T23:49:43.1965227Z 0e26764e2949: Download complete 2024-12-17T23:49:44.4740364Z 12920fcbf355: Verifying Checksum 2024-12-17T23:49:44.4740985Z 12920fcbf355: Download complete 2024-12-17T23:49:44.5423110Z c6543eb4cb51: Verifying Checksum 2024-12-17T23:49:44.5423548Z c6543eb4cb51: Download complete 2024-12-17T23:49:44.5620164Z 3b8143344aee: Download complete 2024-12-17T23:49:44.6342296Z ad803458908b: Verifying Checksum 2024-12-17T23:49:44.6342677Z ad803458908b: Download complete 2024-12-17T23:49:44.6416184Z d4e3b9c27d53: Verifying Checksum 2024-12-17T23:49:44.6416560Z d4e3b9c27d53: Download complete 2024-12-17T23:49:44.7129571Z 0704e0edafd4: Download complete 2024-12-17T23:49:44.7383443Z eff65b2f6bc4: Verifying Checksum 2024-12-17T23:49:44.7383827Z eff65b2f6bc4: Download complete 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2024-12-17T23:51:20.4197984Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:51:20.4439612Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:51:20.4484795Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:51:20.4485808Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:51:20.4495111Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:20.4495506Z env: 2024-12-17T23:51:20.4495745Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:20.4496026Z ##[endgroup] 2024-12-17T23:51:20.4726914Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-12-17T23:51:20.4727523Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-12-17T23:51:20.4728037Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-12-17T23:51:20.4728534Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:51:20.4735157Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:20.4735560Z env: 2024-12-17T23:51:20.4735868Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:20.4736138Z ##[endgroup] 2024-12-17T23:51:21.3241027Z Defaulting to user installation because normal site-packages is not writeable 2024-12-17T23:51:21.7296687Z Collecting psutil==5.9.1 2024-12-17T23:51:21.7653607Z Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB) 2024-12-17T23:51:21.8961068Z Collecting nvidia-ml-py==11.525.84 2024-12-17T23:51:21.9002486Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2024-12-17T23:51:22.0373093Z Installing collected packages: psutil, nvidia-ml-py 2024-12-17T23:51:22.2662277Z Successfully installed nvidia-ml-py-11.525.84 psutil-5.9.1 2024-12-17T23:51:22.5037860Z Prepare all required actions 2024-12-17T23:51:22.5038836Z Getting action download info 2024-12-17T23:51:22.6292195Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-12-17T23:51:23.3950652Z Download action repository 'actions/download-artifact@v4' (SHA:fa0a91b85d4f404e444e00e005971372dc801d16) 2024-12-17T23:51:25.4266339Z ##[group]Run ./.github/actions/download-build-artifacts 2024-12-17T23:51:25.4266701Z with: 2024-12-17T23:51:25.4266932Z name: linux-focal-py3.13-clang10 2024-12-17T23:51:25.4267248Z s3-bucket: gha-artifacts 2024-12-17T23:51:25.4267501Z env: 2024-12-17T23:51:25.4267702Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:25.4267953Z ##[endgroup] 2024-12-17T23:51:25.4434138Z ##[group]Run seemethere/download-artifact-s3@v4 2024-12-17T23:51:25.4434492Z with: 2024-12-17T23:51:25.4434720Z name: linux-focal-py3.13-clang10 2024-12-17T23:51:25.4435039Z s3-bucket: gha-artifacts 2024-12-17T23:51:25.4435373Z region: us-east-1 2024-12-17T23:51:25.4435618Z env: 2024-12-17T23:51:25.4435827Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:25.4436100Z ##[endgroup] 2024-12-17T23:51:26.2583763Z (node:40664) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-12-17T23:51:26.2584305Z 2024-12-17T23:51:26.2584505Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-12-17T23:51:26.2585067Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-12-17T23:51:26.2585647Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-12-17T23:51:26.4590242Z Found 1 objects with prefix pytorch/pytorch/12383255652/linux-focal-py3.13-clang10/ 2024-12-17T23:51:26.4590997Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-12-17T23:51:30.4547249Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-12-17T23:51:30.4553961Z Artifact download has finished successfully 2024-12-17T23:51:30.4717311Z ##[group]Run unzip -o artifacts.zip 2024-12-17T23:51:30.4717686Z unzip -o artifacts.zip 2024-12-17T23:51:30.4723424Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:30.4723814Z env: 2024-12-17T23:51:30.4724048Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:30.4724328Z ##[endgroup] 2024-12-17T23:51:30.4788859Z Archive: artifacts.zip 2024-12-17T23:51:30.4789863Z creating: dist/ 2024-12-17T23:51:31.4474296Z inflating: dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl 2024-12-17T23:51:31.4475169Z creating: build/custom_test_artifacts/ 2024-12-17T23:51:31.4475589Z creating: build/custom_test_artifacts/custom-op-build/ 2024-12-17T23:51:31.4476107Z creating: 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inflating: build/bin/op_registration_test 2024-12-17T23:51:36.7411803Z inflating: build/bin/native_test 2024-12-17T23:51:36.7462385Z inflating: build/bin/math_kernel_test 2024-12-17T23:51:36.7512630Z inflating: build/bin/memory_format_test 2024-12-17T23:51:36.7560609Z inflating: build/bin/reduce_ops_test 2024-12-17T23:51:36.7610104Z inflating: build/bin/packedtensoraccessor_test 2024-12-17T23:51:36.7678271Z inflating: build/bin/pow_test 2024-12-17T23:51:36.7726557Z inflating: build/bin/reportMemoryUsage_test 2024-12-17T23:51:36.7780456Z inflating: build/bin/quantized_test 2024-12-17T23:51:36.7830909Z inflating: build/bin/test_edge_op_registration 2024-12-17T23:51:36.7834898Z inflating: build/bin/torch_shm_manager 2024-12-17T23:51:36.7850405Z inflating: build/bin/tutorial_tensorexpr 2024-12-17T23:51:36.8839530Z inflating: build/bin/test_tensorexpr 2024-12-17T23:51:36.9397586Z inflating: build/bin/test_jit 2024-12-17T23:51:36.9398237Z creating: .additional_ci_files/ 2024-12-17T23:51:36.9483817Z inflating: .additional_ci_files/test-times.json 2024-12-17T23:51:36.9826003Z inflating: .additional_ci_files/test-class-times.json 2024-12-17T23:51:36.9936826Z ##[group]Run rm artifacts.zip 2024-12-17T23:51:36.9937159Z rm artifacts.zip 2024-12-17T23:51:36.9946714Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:36.9947126Z env: 2024-12-17T23:51:36.9947366Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:36.9947649Z ##[endgroup] 2024-12-17T23:51:37.1675412Z ##[group]Run df -H 2024-12-17T23:51:37.1675700Z df -H 2024-12-17T23:51:37.1681555Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:37.1681964Z env: 2024-12-17T23:51:37.1682200Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:37.1682489Z ##[endgroup] 2024-12-17T23:51:37.1721205Z Filesystem Size Used Avail Use% Mounted on 2024-12-17T23:51:37.1721838Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-12-17T23:51:37.1722217Z tmpfs 17G 0 17G 0% /dev/shm 2024-12-17T23:51:37.1722566Z tmpfs 6.6G 521k 6.6G 1% /run 2024-12-17T23:51:37.1723014Z /dev/nvme0n1p1 161G 26G 136G 16% / 2024-12-17T23:51:37.1723424Z tmpfs 17G 13k 17G 1% /tmp 2024-12-17T23:51:37.1723788Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-12-17T23:51:37.1757178Z Prepare all required actions 2024-12-17T23:51:37.1757675Z Getting action download info 2024-12-17T23:51:37.5540996Z ##[group]Run ./.github/actions/download-td-artifacts 2024-12-17T23:51:37.5541383Z with: 2024-12-17T23:51:37.5541603Z env: 2024-12-17T23:51:37.5541833Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:37.5542094Z ##[endgroup] 2024-12-17T23:51:37.5671524Z ##[group]Run seemethere/download-artifact-s3@v4 2024-12-17T23:51:37.5671895Z with: 2024-12-17T23:51:37.5672127Z name: td_results 2024-12-17T23:51:37.5672384Z s3-bucket: gha-artifacts 2024-12-17T23:51:37.5672655Z region: us-east-1 2024-12-17T23:51:37.5673035Z env: 2024-12-17T23:51:37.5673259Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:37.5673530Z ##[endgroup] 2024-12-17T23:51:38.0176759Z (node:40684) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-12-17T23:51:38.0177284Z 2024-12-17T23:51:38.0177568Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-12-17T23:51:38.0178136Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-12-17T23:51:38.0178702Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-12-17T23:51:38.1405822Z Found 1 objects with prefix pytorch/pytorch/12383255652/td_results/ 2024-12-17T23:51:38.1406511Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-12-17T23:51:38.1923063Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-12-17T23:51:38.1928148Z Artifact download has finished successfully 2024-12-17T23:51:38.2183671Z ##[group]Run mkdir -p .additional_ci_files 2024-12-17T23:51:38.2184066Z mkdir -p .additional_ci_files 2024-12-17T23:51:38.2184524Z mv td_results.json .additional_ci_files/td_results.json || true 2024-12-17T23:51:38.2190887Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:38.2191290Z env: 2024-12-17T23:51:38.2191531Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:38.2191807Z ##[endgroup] 2024-12-17T23:51:38.2387376Z ##[group]Run .github/scripts/parse_ref.py 2024-12-17T23:51:38.2388049Z .github/scripts/parse_ref.py 2024-12-17T23:51:38.2394932Z shell: /usr/bin/bash -e {0} 2024-12-17T23:51:38.2395243Z env: 2024-12-17T23:51:38.2395472Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:38.2395737Z ##[endgroup] 2024-12-17T23:51:38.2656169Z Prepare all required actions 2024-12-17T23:51:38.4649996Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-12-17T23:51:38.4650367Z with: 2024-12-17T23:51:38.4650805Z github-token: *** 2024-12-17T23:51:38.4651060Z env: 2024-12-17T23:51:38.4651306Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:38.4651582Z ##[endgroup] 2024-12-17T23:51:38.4733172Z ##[group]Run set -eux 2024-12-17T23:51:38.4733464Z set -eux 2024-12-17T23:51:38.4733917Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-12-17T23:51:38.4739950Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:38.4740346Z env: 2024-12-17T23:51:38.4740573Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:38.4741096Z GITHUB_TOKEN: *** 2024-12-17T23:51:38.4741350Z ##[endgroup] 2024-12-17T23:51:38.4764590Z + python3 .github/scripts/get_workflow_job_id.py 12383255652 i-00cc61a0ee6903cab 2024-12-17T23:51:40.0653597Z setting job-id=34566065955 2024-12-17T23:51:40.0654216Z setting job-name=linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:51:40.0843225Z Prepare all required actions 2024-12-17T23:51:40.0843630Z Getting action download info 2024-12-17T23:51:40.2737067Z ##[group]Run ./.github/actions/filter-test-configs 2024-12-17T23:51:40.2737468Z with: 2024-12-17T23:51:40.2737912Z github-token: *** 2024-12-17T23:51:40.2740089Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]} 2024-12-17T23:51:40.2742539Z job-name: linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:51:40.2743009Z env: 2024-12-17T23:51:40.2743224Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:40.2743663Z ##[endgroup] 2024-12-17T23:51:40.2911349Z ##[group]Run nick-fields/retry@v3.0.0 2024-12-17T23:51:40.2911688Z with: 2024-12-17T23:51:40.2911914Z shell: bash 2024-12-17T23:51:40.2912139Z timeout_minutes: 10 2024-12-17T23:51:40.2912403Z max_attempts: 5 2024-12-17T23:51:40.2912659Z retry_wait_seconds: 30 2024-12-17T23:51:40.2913486Z 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 2024-12-17T23:51:40.2914360Z polling_interval_seconds: 1 2024-12-17T23:51:40.2914645Z warning_on_retry: true 2024-12-17T23:51:40.2914923Z continue_on_error: false 2024-12-17T23:51:40.2915196Z env: 2024-12-17T23:51:40.2915417Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:40.2916175Z GITHUB_TOKEN: *** 2024-12-17T23:51:40.2916429Z ##[endgroup] 2024-12-17T23:51:40.3822105Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-12-17T23:51:40.6189262Z Defaulting to user installation because normal site-packages is not writeable 2024-12-17T23:51:40.8926754Z Collecting requests==2.27.1 2024-12-17T23:51:40.9251349Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2024-12-17T23:51:41.1509063Z Collecting pyyaml==6.0.1 2024-12-17T23:51:41.1546555Z Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB) 2024-12-17T23:51:41.2227578Z Collecting certifi>=2017.4.17 2024-12-17T23:51:41.2273439Z Downloading certifi-2024.12.14-py3-none-any.whl (164 kB) 2024-12-17T23:51:41.2322950Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-12-17T23:51:41.5252276Z Collecting charset-normalizer~=2.0.0 2024-12-17T23:51:41.5288778Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2024-12-17T23:51:41.5328628Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-12-17T23:51:41.6074720Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2024-12-17T23:51:41.7162360Z Successfully installed certifi-2024.12.14 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1 2024-12-17T23:51:42.3666661Z Command completed after 1 attempt(s). 2024-12-17T23:51:42.3729690Z ##[group]Run set -x 2024-12-17T23:51:42.3729975Z set -x 2024-12-17T23:51:42.3730222Z  2024-12-17T23:51:42.3730625Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-12-17T23:51:42.3731120Z # in runner workspace 2024-12-17T23:51:42.3731530Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-12-17T23:51:42.3737345Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:42.3737742Z env: 2024-12-17T23:51:42.3738152Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:42.3738439Z ##[endgroup] 2024-12-17T23:51:42.3762196Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-12-17T23:51:42.4013177Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-12-17T23:51:42.4013621Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-12-17T23:51:42.4013982Z echo "Job name: ${JOB_NAME}" 2024-12-17T23:51:42.4014286Z  2024-12-17T23:51:42.4014686Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-12-17T23:51:42.4015192Z # in runner workspace 2024-12-17T23:51:42.4015640Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-12-17T23:51:42.4016144Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-12-17T23:51:42.4016499Z  --job-name "${JOB_NAME}" \ 2024-12-17T23:51:42.4018782Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]}" \ 2024-12-17T23:51:42.4021217Z  --selected-test-configs "" \ 2024-12-17T23:51:42.4021572Z  --pr-number "${PR_NUMBER}" \ 2024-12-17T23:51:42.4021888Z  --tag "${TAG}" \ 2024-12-17T23:51:42.4022196Z  --event-name "${EVENT_NAME}" \ 2024-12-17T23:51:42.4022545Z  --schedule "${SCHEDULE}" \ 2024-12-17T23:51:42.4022881Z  --branch "${HEAD_BRANCH}" 2024-12-17T23:51:42.4028590Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:42.4028992Z env: 2024-12-17T23:51:42.4029209Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:42.4029696Z GITHUB_TOKEN: *** 2024-12-17T23:51:42.4030099Z JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:51:42.4030561Z PR_NUMBER: 2024-12-17T23:51:42.4030793Z TAG: 2024-12-17T23:51:42.4031017Z EVENT_NAME: push 2024-12-17T23:51:42.4031252Z SCHEDULE: 2024-12-17T23:51:42.4031483Z HEAD_BRANCH: 2024-12-17T23:51:42.4031720Z ##[endgroup] 2024-12-17T23:51:42.4054221Z Workflow: pull 2024-12-17T23:51:42.4054654Z Job name: linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:51:42.6574849Z ##[group]Run echo "Filtered matrix:" 2024-12-17T23:51:42.6575218Z echo "Filtered matrix:" 2024-12-17T23:51:42.6577508Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]}" 2024-12-17T23:51:42.6579766Z  2024-12-17T23:51:42.6579994Z echo 2024-12-17T23:51:42.6580277Z echo "Is the current job unstable? False" 2024-12-17T23:51:42.6580638Z  2024-12-17T23:51:42.6580862Z echo 2024-12-17T23:51:42.6581145Z echo "Is keep-going label set? False" 2024-12-17T23:51:42.6581490Z  2024-12-17T23:51:42.6581701Z echo 2024-12-17T23:51:42.6582104Z echo "Renabled issues? " 2024-12-17T23:51:42.6587798Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:42.6588204Z env: 2024-12-17T23:51:42.6588460Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:42.6588731Z ##[endgroup] 2024-12-17T23:51:42.6610807Z Filtered matrix: 2024-12-17T23:51:42.6613320Z {include: [{config: default, shard: 1, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: linux.4xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: linux.2xlarge}]} 2024-12-17T23:51:42.6615380Z 2024-12-17T23:51:42.6615503Z Is the current job unstable? False 2024-12-17T23:51:42.6615728Z 2024-12-17T23:51:42.6615854Z Is keep-going label set? False 2024-12-17T23:51:42.6616048Z 2024-12-17T23:51:42.6616157Z Renabled issues? 2024-12-17T23:51:42.6654756Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-12-17T23:51:42.6655412Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-12-17T23:51:42.6660730Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:51:42.6661126Z env: 2024-12-17T23:51:42.6661355Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:42.6661633Z JOB_TIMEOUT: 600 2024-12-17T23:51:42.6661867Z ##[endgroup] 2024-12-17T23:51:42.6727795Z ##[group]Run set -x 2024-12-17T23:51:42.6728133Z set -x 2024-12-17T23:51:42.6728378Z  2024-12-17T23:51:42.6728648Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-12-17T23:51:42.6729065Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-12-17T23:51:42.6729472Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-12-17T23:51:42.6729855Z  TEST_COMMAND=.ci/onnx/test.sh 2024-12-17T23:51:42.6730178Z else 2024-12-17T23:51:42.6730447Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-12-17T23:51:42.6730785Z fi 2024-12-17T23:51:42.6730997Z  2024-12-17T23:51:42.6731352Z # detached container should get cleaned up by teardown_ec2_linux 2024-12-17T23:51:42.6731913Z # TODO: Stop building test binaries as part of the build phase 2024-12-17T23:51:42.6732410Z # Used for GPU_FLAG since that doesn't play nice 2024-12-17T23:51:42.6732846Z # shellcheck disable=SC2086,SC2090 2024-12-17T23:51:42.6733204Z container_name=$(docker run \ 2024-12-17T23:51:42.6733547Z  ${GPU_FLAG:-} \ 2024-12-17T23:51:42.6733876Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2024-12-17T23:51:42.6734247Z  -e BUILD_ENVIRONMENT \ 2024-12-17T23:51:42.6734567Z  -e PR_NUMBER \ 2024-12-17T23:51:42.6734864Z  -e GITHUB_ACTIONS \ 2024-12-17T23:51:42.6735164Z  -e GITHUB_REPOSITORY \ 2024-12-17T23:51:42.6735486Z  -e GITHUB_WORKFLOW \ 2024-12-17T23:51:42.6735797Z  -e GITHUB_JOB \ 2024-12-17T23:51:42.6736099Z  -e GITHUB_RUN_ID \ 2024-12-17T23:51:42.6736402Z  -e GITHUB_RUN_NUMBER \ 2024-12-17T23:51:42.6736709Z  -e GITHUB_RUN_ATTEMPT \ 2024-12-17T23:51:42.6737025Z  -e JOB_ID \ 2024-12-17T23:51:42.6737306Z  -e JOB_NAME \ 2024-12-17T23:51:42.6737588Z  -e BASE_SHA \ 2024-12-17T23:51:42.6737866Z  -e BRANCH \ 2024-12-17T23:51:42.6738124Z  -e SHA1 \ 2024-12-17T23:51:42.6738394Z  -e AWS_DEFAULT_REGION \ 2024-12-17T23:51:42.6738713Z  -e IN_WHEEL_TEST \ 2024-12-17T23:51:42.6739007Z  -e SHARD_NUMBER \ 2024-12-17T23:51:42.6739302Z  -e TEST_CONFIG \ 2024-12-17T23:51:42.6739582Z  -e NUM_TEST_SHARDS \ 2024-12-17T23:51:42.6739891Z  -e REENABLED_ISSUES \ 2024-12-17T23:51:42.6740214Z  -e CONTINUE_THROUGH_ERROR \ 2024-12-17T23:51:42.6740550Z  -e VERBOSE_TEST_LOGS \ 2024-12-17T23:51:42.6740864Z  -e TEST_SHOWLOCALS \ 2024-12-17T23:51:42.6741175Z  -e NO_TEST_TIMEOUT \ 2024-12-17T23:51:42.6741460Z  -e NO_TD \ 2024-12-17T23:51:42.6741727Z  -e TD_DISTRIBUTED \ 2024-12-17T23:51:42.6742027Z  -e PR_LABELS \ 2024-12-17T23:51:42.6742338Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-12-17T23:51:42.6742688Z  -e SCCACHE_BUCKET \ 2024-12-17T23:51:42.6742980Z  -e SCCACHE_REGION \ 2024-12-17T23:51:42.6743291Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-12-17T23:51:42.6743615Z  -e XLA_CUDA \ 2024-12-17T23:51:42.6743923Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-12-17T23:51:42.6744305Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-12-17T23:51:42.6744685Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-12-17T23:51:42.6745070Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-12-17T23:51:42.6745430Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-12-17T23:51:42.6745779Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2024-12-17T23:51:42.6746259Z  -e DASHBOARD_TAG \ 2024-12-17T23:51:42.6746545Z  -e IS_A100_RUNNER \ 2024-12-17T23:51:42.6746856Z  -e ARTIFACTS_FILE_SUFFIX \ 2024-12-17T23:51:42.6747241Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-12-17T23:51:42.6747663Z  --security-opt seccomp=unconfined \ 2024-12-17T23:51:42.6748115Z  --cap-add=SYS_PTRACE \ 2024-12-17T23:51:42.6748432Z  --ipc=host \ 2024-12-17T23:51:42.6748709Z  --shm-size="${SHM_SIZE}" \ 2024-12-17T23:51:42.6749029Z  --tty \ 2024-12-17T23:51:42.6749287Z  --detach \ 2024-12-17T23:51:42.6749573Z  --name="${container_name}" \ 2024-12-17T23:51:42.6749911Z  --user jenkins \ 2024-12-17T23:51:42.6750267Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-12-17T23:51:42.6750684Z  -w /var/lib/jenkins/workspace \ 2024-12-17T23:51:42.6751026Z  "${DOCKER_IMAGE}" 2024-12-17T23:51:42.6751310Z ) 2024-12-17T23:51:42.6751618Z # Propagate download.pytorch.org IP to container 2024-12-17T23:51:42.6752309Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-12-17T23:51:42.6753052Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-12-17T23:51:42.6753786Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-12-17T23:51:42.6759408Z shell: /usr/bin/bash -e {0} 2024-12-17T23:51:42.6759700Z env: 2024-12-17T23:51:42.6759928Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:51:42.6760259Z BUILD_ENVIRONMENT: linux-focal-py3.13-clang10 2024-12-17T23:51:42.6760607Z PR_NUMBER: 2024-12-17T23:51:42.6760863Z GITHUB_REPOSITORY: pytorch/pytorch 2024-12-17T23:51:42.6761185Z GITHUB_WORKFLOW: pull 2024-12-17T23:51:42.6761452Z GITHUB_JOB: test 2024-12-17T23:51:42.6761694Z GITHUB_RUN_ID: 12383255652 2024-12-17T23:51:42.6761988Z GITHUB_RUN_NUMBER: 276594 2024-12-17T23:51:42.6762271Z GITHUB_RUN_ATTEMPT: 1 2024-12-17T23:51:42.6762532Z JOB_ID: 34566065955 2024-12-17T23:51:42.6762930Z JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:51:42.6763394Z BRANCH: release/2.6 2024-12-17T23:51:42.6763678Z SHA1: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:51:42.6764076Z BASE_SHA: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:51:42.6764434Z TEST_CONFIG: default 2024-12-17T23:51:42.6764695Z SHARD_NUMBER: 1 2024-12-17T23:51:42.6764947Z NUM_TEST_SHARDS: 5 2024-12-17T23:51:42.6765192Z REENABLED_ISSUES: 2024-12-17T23:51:42.6765465Z CONTINUE_THROUGH_ERROR: False 2024-12-17T23:51:42.6765766Z VERBOSE_TEST_LOGS: False 2024-12-17T23:51:42.6766049Z TEST_SHOWLOCALS: False 2024-12-17T23:51:42.6766328Z NO_TEST_TIMEOUT: False 2024-12-17T23:51:42.6766584Z NO_TD: False 2024-12-17T23:51:42.6766826Z TD_DISTRIBUTED: False 2024-12-17T23:51:42.6767156Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-12-17T23:51:42.6767537Z SCCACHE_REGION: us-east-1 2024-12-17T23:51:42.6767829Z SCCACHE_S3_KEY_PREFIX: pull 2024-12-17T23:51:42.6768104Z SHM_SIZE: 1g 2024-12-17T23:51:42.6768781Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:51:42.6769531Z XLA_CUDA: 2024-12-17T23:51:42.6769912Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:51:42.6770388Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-12-17T23:51:42.6770716Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-12-17T23:51:42.6771040Z DASHBOARD_TAG: 2024-12-17T23:51:42.6771492Z HUGGING_FACE_HUB_TOKEN: *** 2024-12-17T23:51:42.6771928Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2024-12-17T23:51:42.6772244Z IS_A100_RUNNER: 0 2024-12-17T23:51:42.6772611Z ARTIFACTS_FILE_SUFFIX: test-default-1-5-linux.4xlarge_34566065955 2024-12-17T23:51:42.6773043Z ##[endgroup] 2024-12-17T23:51:42.6794228Z + [[ default == \m\u\l\t\i\g\p\u ]] 2024-12-17T23:51:42.6794594Z + [[ linux-focal-py3.13-clang10 == *onnx* ]] 2024-12-17T23:51:42.6794957Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-12-17T23:51:42.6802005Z +++ nproc --ignore=2 2024-12-17T23:51:42.6825945Z ++ 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 SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -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 --env-file=/tmp/github_env_12383255652 --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-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:51:56.9263661Z + container_name=009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-17T23:51:56.9265405Z + grep download.pytorch.org /etc/hosts 2024-12-17T23:51:56.9266996Z + docker exec -i 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 sudo bash -c '/bin/cat >> /etc/hosts' 2024-12-17T23:51:57.0886390Z + echo DOCKER_CONTAINER_ID=009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-17T23:51:57.0889471Z ++ echo dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl 2024-12-17T23:51:57.0891717Z + docker exec -t 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 sh -c 'python3 -m pip install dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-12-17T23:51:57.4678927Z Processing ./dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl (from torch==2.6.0a0+git0cdf8b1) 2024-12-17T23:51:57.8902162Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.16.1) 2024-12-17T23:51:57.8905056Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (4.12.2) 2024-12-17T23:51:57.8907997Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2.8.8) 2024-12-17T23:51:57.8910898Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.1.4) 2024-12-17T23:51:57.8913726Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2024.10.0) 2024-12-17T23:51:57.8925242Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (75.1.0) 2024-12-17T23:51:57.8929737Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.13.1) 2024-12-17T23:51:57.8944581Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy==1.13.1->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.3.0) 2024-12-17T23:51:57.8960477Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.3.0) 2024-12-17T23:51:57.8974163Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2.1.2) 2024-12-17T23:51:57.9072948Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.0.2) 2024-12-17T23:51:58.0923487Z Installing collected packages: torch 2024-12-17T23:52:06.4727610Z Successfully installed torch-2.6.0a0+git0cdf8b1 2024-12-17T23:52:06.5543416Z + export TERM=vt100 2024-12-17T23:52:06.5543881Z + TERM=vt100 2024-12-17T23:52:06.5545746Z ++ dirname .ci/pytorch/test.sh 2024-12-17T23:52:06.5552341Z + source .ci/pytorch/common.sh 2024-12-17T23:52:06.5555276Z +++ dirname .ci/pytorch/common.sh 2024-12-17T23:52:06.5561232Z ++ source .ci/pytorch/common_utils.sh 2024-12-17T23:52:06.5562701Z +++ declare -f -t trap_add 2024-12-17T23:52:06.5568050Z ++ set -ex 2024-12-17T23:52:06.5568441Z ++ [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:52:06.5568936Z ++ BUILD_TEST_LIBTORCH=0 2024-12-17T23:52:06.5569254Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-17T23:52:06.5569899Z + [[ -d /var/lib/jenkins/workspace ]] 2024-12-17T23:52:06.5572359Z ++ stat -c %u /var/lib/jenkins/workspace 2024-12-17T23:52:06.5583416Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-12-17T23:52:06.5583806Z + trap_add cleanup_workspace EXIT 2024-12-17T23:52:06.5584141Z + trap_add_cmd=cleanup_workspace 2024-12-17T23:52:06.5584428Z + shift 2024-12-17T23:52:06.5584666Z + for trap_add_name in "$@" 2024-12-17T23:52:06.5589621Z +++ trap -p EXIT 2024-12-17T23:52:06.5592147Z ++ eval 'extract_trap_cmd ' 2024-12-17T23:52:06.5592449Z +++ extract_trap_cmd 2024-12-17T23:52:06.5592789Z +++ printf '%s\n' '' 2024-12-17T23:52:06.5593064Z ++ printf '%s\n' cleanup_workspace 2024-12-17T23:52:06.5595210Z + trap -- ' 2024-12-17T23:52:06.5595494Z cleanup_workspace' EXIT 2024-12-17T23:52:06.5595831Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-12-17T23:52:07.0053810Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-12-17T23:52:07.0068451Z + echo 'Environment variables:' 2024-12-17T23:52:07.0068841Z Environment variables: 2024-12-17T23:52:07.0069122Z + env 2024-12-17T23:52:07.0075689Z INSTALLED_DB=yes 2024-12-17T23:52:07.0076295Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:52:07.0076883Z CONTINUE_THROUGH_ERROR=False 2024-12-17T23:52:07.0077549Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10 2024-12-17T23:52:07.0078154Z HOSTNAME=009cb12578f8 2024-12-17T23:52:07.0078824Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0079480Z GITHUB_ACTION=__self 2024-12-17T23:52:07.0079751Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-12-17T23:52:07.0080072Z GITHUB_RUN_NUMBER=276594 2024-12-17T23:52:07.0080357Z TEST_CONFIG=default 2024-12-17T23:52:07.0080630Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-12-17T23:52:07.0080974Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-12-17T23:52:07.0081288Z IS_A100_RUNNER=0 2024-12-17T23:52:07.0081783Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2024-12-17T23:52:07.0082102Z GITHUB_TRIGGERING_ACTOR=malfet 2024-12-17T23:52:07.0082492Z GITHUB_REF_TYPE=branch 2024-12-17T23:52:07.0082782Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-12-17T23:52:07.0083116Z BASE_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0083472Z XLA_CUDA= 2024-12-17T23:52:07.0083818Z HUGGING_FACE_HUB_TOKEN=*** 2024-12-17T23:52:07.0085327Z *** 2024-12-17T23:52:07.0085578Z GITHUB_REPOSITORY_ID=65600975 2024-12-17T23:52:07.0085882Z GITHUB_ACTIONS=true 2024-12-17T23:52:07.0086171Z SHA1=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0086566Z GITHUB_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0087118Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/release/2.6 2024-12-17T23:52:07.0087856Z UCC_HOME=/usr 2024-12-17T23:52:07.0088105Z VERBOSE_TEST_LOGS=False 2024-12-17T23:52:07.0088392Z GITHUB_REF=refs/heads/release/2.6 2024-12-17T23:52:07.0088699Z SHARD_NUMBER=1 2024-12-17T23:52:07.0088939Z GITHUB_REF_PROTECTED=true 2024-12-17T23:52:07.0089219Z HOME=/var/lib/jenkins 2024-12-17T23:52:07.0089521Z GITHUB_API_URL=https://api.github.com 2024-12-17T23:52:07.0090000Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-12-17T23:52:07.0090313Z UCX_COMMIT= 2024-12-17T23:52:07.0090540Z SCCACHE_S3_KEY_PREFIX=pull 2024-12-17T23:52:07.0090827Z NUM_TEST_SHARDS=5 2024-12-17T23:52:07.0091070Z UCX_HOME=/usr 2024-12-17T23:52:07.0091653Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0092448Z JOB_NAME=linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:52:07.0093214Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0094050Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-12-17T23:52:07.0094575Z GITHUB_EVENT_NAME=push 2024-12-17T23:52:07.0094841Z DASHBOARD_TAG= 2024-12-17T23:52:07.0095088Z GITHUB_RUN_ID=12383255652 2024-12-17T23:52:07.0095729Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0096430Z GITHUB_ACTOR=malfet 2024-12-17T23:52:07.0096680Z PR_NUMBER= 2024-12-17T23:52:07.0096908Z DESIRED_CUDA= 2024-12-17T23:52:07.0097153Z GITHUB_RUN_ATTEMPT=1 2024-12-17T23:52:07.0097417Z ANACONDA_PYTHON_VERSION=3.13 2024-12-17T23:52:07.0097771Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-12-17T23:52:07.0098140Z TERM=vt100 2024-12-17T23:52:07.0098368Z INSTALLED_VISION=yes 2024-12-17T23:52:07.0098628Z BRANCH=release/2.6 2024-12-17T23:52:07.0098872Z SCCACHE_REGION=us-east-1 2024-12-17T23:52:07.0099159Z OPENSSL_ROOT_DIR=/opt/openssl 2024-12-17T23:52:07.0099461Z CUDA_PATH=/usr/local/cuda 2024-12-17T23:52:07.0099997Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-12-17T23:52:07.0100602Z GITHUB_SERVER_URL=https://github.com 2024-12-17T23:52:07.0100906Z UCC_COMMIT= 2024-12-17T23:52:07.0101140Z REENABLED_ISSUES= 2024-12-17T23:52:07.0101384Z DOCS= 2024-12-17T23:52:07.0101596Z SHLVL=1 2024-12-17T23:52:07.0101822Z MAX_JOBS=14 2024-12-17T23:52:07.0102041Z GITHUB_ACTOR_ID=2453524 2024-12-17T23:52:07.0102394Z GITHUB_WORKFLOW_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0102802Z GITHUB_REF_NAME=release/2.6 2024-12-17T23:52:07.0103221Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:52:07.0103668Z GITHUB_JOB=test 2024-12-17T23:52:07.0103902Z NO_TEST_TIMEOUT=False 2024-12-17T23:52:07.0104164Z TD_DISTRIBUTED=False 2024-12-17T23:52:07.0104442Z GITHUB_REPOSITORY=pytorch/pytorch 2024-12-17T23:52:07.0104759Z GITHUB_RETENTION_DAYS=90 2024-12-17T23:52:07.0105031Z OPENSSL_DIR=/opt/openssl 2024-12-17T23:52:07.0105310Z GITHUB_ACTION_REPOSITORY= 2024-12-17T23:52:07.0106123Z 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 2024-12-17T23:52:07.0106957Z GITHUB_BASE_REF= 2024-12-17T23:52:07.0107203Z INSTALLED_ACL= 2024-12-17T23:52:07.0107564Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-linux.4xlarge_34566065955 2024-12-17T23:52:07.0107973Z CI=true 2024-12-17T23:52:07.0108210Z GITHUB_REPOSITORY_OWNER=pytorch 2024-12-17T23:52:07.0108502Z JOB_ID=34566065955 2024-12-17T23:52:07.0108753Z INSTALLED_PROTOBUF=yes 2024-12-17T23:52:07.0109004Z GITHUB_HEAD_REF= 2024-12-17T23:52:07.0109250Z GITHUB_ACTION_REF= 2024-12-17T23:52:07.0109552Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-12-17T23:52:07.0109922Z TEST_SHOWLOCALS=False 2024-12-17T23:52:07.0110187Z GITHUB_WORKFLOW=pull 2024-12-17T23:52:07.0110450Z DEBIAN_FRONTEND=noninteractive 2024-12-17T23:52:07.0111191Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0111852Z NO_TD=False 2024-12-17T23:52:07.0112097Z SKIP_SCCACHE_INITIALIZATION=1 2024-12-17T23:52:07.0112393Z _=/usr/bin/env 2024-12-17T23:52:07.0112717Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-12-17T23:52:07.0220273Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2024-12-17T23:52:07.0220976Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2024-12-17T23:52:07.0221667Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2024-12-17T23:52:07.0222313Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2024-12-17T23:52:07.0222843Z + BUILD_DIR=build 2024-12-17T23:52:07.0223097Z + BUILD_RENAMED_DIR=build_renamed 2024-12-17T23:52:07.0223408Z + BUILD_BIN_DIR=build/bin 2024-12-17T23:52:07.0223691Z + SHARD_NUMBER=1 2024-12-17T23:52:07.0223943Z + NUM_TEST_SHARDS=5 2024-12-17T23:52:07.0224196Z + export VALGRIND=ON 2024-12-17T23:52:07.0224440Z + VALGRIND=ON 2024-12-17T23:52:07.0224712Z + [[ linux-focal-py3.13-clang10 == *clang9* ]] 2024-12-17T23:52:07.0225083Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-17T23:52:07.0225409Z + [[ 0 == \1 ]] 2024-12-17T23:52:07.0225643Z + [[ False == \1 ]] 2024-12-17T23:52:07.0225916Z + [[ linux-focal-py3.13-clang10 != *bazel* ]] 2024-12-17T23:52:07.0226401Z ++ realpath build/custom_test_artifacts 2024-12-17T23:52:07.0233554Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-12-17T23:52:07.0234128Z + [[ -n '' ]] 2024-12-17T23:52:07.0234383Z + echo 'Environment variables' 2024-12-17T23:52:07.0234688Z Environment variables 2024-12-17T23:52:07.0234936Z + env 2024-12-17T23:52:07.0240472Z INSTALLED_DB=yes 2024-12-17T23:52:07.0241017Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:52:07.0241702Z CONTINUE_THROUGH_ERROR=False 2024-12-17T23:52:07.0242110Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10 2024-12-17T23:52:07.0242452Z HOSTNAME=009cb12578f8 2024-12-17T23:52:07.0243192Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0243858Z GITHUB_ACTION=__self 2024-12-17T23:52:07.0244145Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-12-17T23:52:07.0244480Z GITHUB_RUN_NUMBER=276594 2024-12-17T23:52:07.0244761Z TEST_CONFIG=default 2024-12-17T23:52:07.0245020Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-12-17T23:52:07.0245365Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-12-17T23:52:07.0245695Z IS_A100_RUNNER=0 2024-12-17T23:52:07.0246223Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2024-12-17T23:52:07.0246614Z GITHUB_TRIGGERING_ACTOR=malfet 2024-12-17T23:52:07.0247006Z GITHUB_REF_TYPE=branch 2024-12-17T23:52:07.0247371Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-12-17T23:52:07.0247791Z BASE_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0248277Z XLA_CUDA= 2024-12-17T23:52:07.0248667Z HUGGING_FACE_HUB_TOKEN=*** 2024-12-17T23:52:07.0249249Z *** 2024-12-17T23:52:07.0249548Z GITHUB_REPOSITORY_ID=65600975 2024-12-17T23:52:07.0249885Z GITHUB_ACTIONS=true 2024-12-17T23:52:07.0250170Z SHA1=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0250591Z GITHUB_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0251159Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/release/2.6 2024-12-17T23:52:07.0251680Z UCC_HOME=/usr 2024-12-17T23:52:07.0251911Z VERBOSE_TEST_LOGS=False 2024-12-17T23:52:07.0252197Z GITHUB_REF=refs/heads/release/2.6 2024-12-17T23:52:07.0252502Z SHARD_NUMBER=1 2024-12-17T23:52:07.0252752Z GITHUB_REF_PROTECTED=true 2024-12-17T23:52:07.0253032Z HOME=/var/lib/jenkins 2024-12-17T23:52:07.0253311Z GITHUB_API_URL=https://api.github.com 2024-12-17T23:52:07.0253657Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-12-17T23:52:07.0253964Z UCX_COMMIT= 2024-12-17T23:52:07.0254200Z SCCACHE_S3_KEY_PREFIX=pull 2024-12-17T23:52:07.0254646Z NUM_TEST_SHARDS=5 2024-12-17T23:52:07.0254877Z UCX_HOME=/usr 2024-12-17T23:52:07.0255468Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0256274Z JOB_NAME=linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-17T23:52:07.0257131Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0257971Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-12-17T23:52:07.0258496Z GITHUB_EVENT_NAME=push 2024-12-17T23:52:07.0258753Z DASHBOARD_TAG= 2024-12-17T23:52:07.0259005Z GITHUB_RUN_ID=12383255652 2024-12-17T23:52:07.0259668Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0260369Z GITHUB_ACTOR=malfet 2024-12-17T23:52:07.0260619Z PR_NUMBER= 2024-12-17T23:52:07.0260838Z DESIRED_CUDA= 2024-12-17T23:52:07.0261077Z GITHUB_RUN_ATTEMPT=1 2024-12-17T23:52:07.0261330Z VALGRIND=ON 2024-12-17T23:52:07.0261572Z ANACONDA_PYTHON_VERSION=3.13 2024-12-17T23:52:07.0261908Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-12-17T23:52:07.0262273Z TERM=vt100 2024-12-17T23:52:07.0262502Z INSTALLED_VISION=yes 2024-12-17T23:52:07.0262759Z BRANCH=release/2.6 2024-12-17T23:52:07.0263022Z SCCACHE_REGION=us-east-1 2024-12-17T23:52:07.0263299Z OPENSSL_ROOT_DIR=/opt/openssl 2024-12-17T23:52:07.0263596Z CUDA_PATH=/usr/local/cuda 2024-12-17T23:52:07.0264134Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-12-17T23:52:07.0264738Z GITHUB_SERVER_URL=https://github.com 2024-12-17T23:52:07.0265050Z UCC_COMMIT= 2024-12-17T23:52:07.0265267Z REENABLED_ISSUES= 2024-12-17T23:52:07.0265506Z DOCS= 2024-12-17T23:52:07.0265716Z SHLVL=1 2024-12-17T23:52:07.0265928Z MAX_JOBS=14 2024-12-17T23:52:07.0266159Z GITHUB_ACTOR_ID=2453524 2024-12-17T23:52:07.0266505Z GITHUB_WORKFLOW_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:52:07.0266916Z GITHUB_REF_NAME=release/2.6 2024-12-17T23:52:07.0267338Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:52:07.0267785Z GITHUB_JOB=test 2024-12-17T23:52:07.0268031Z NO_TEST_TIMEOUT=False 2024-12-17T23:52:07.0268284Z TD_DISTRIBUTED=False 2024-12-17T23:52:07.0268567Z GITHUB_REPOSITORY=pytorch/pytorch 2024-12-17T23:52:07.0268884Z GITHUB_RETENTION_DAYS=90 2024-12-17T23:52:07.0269172Z OPENSSL_DIR=/opt/openssl 2024-12-17T23:52:07.0269443Z GITHUB_ACTION_REPOSITORY= 2024-12-17T23:52:07.0270249Z 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 2024-12-17T23:52:07.0271087Z GITHUB_BASE_REF= 2024-12-17T23:52:07.0271330Z INSTALLED_ACL= 2024-12-17T23:52:07.0284609Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-linux.4xlarge_34566065955 2024-12-17T23:52:07.0285137Z CI=true 2024-12-17T23:52:07.0285405Z GITHUB_REPOSITORY_OWNER=pytorch 2024-12-17T23:52:07.0285717Z JOB_ID=34566065955 2024-12-17T23:52:07.0285981Z INSTALLED_PROTOBUF=yes 2024-12-17T23:52:07.0286254Z GITHUB_HEAD_REF= 2024-12-17T23:52:07.0286491Z GITHUB_ACTION_REF= 2024-12-17T23:52:07.0286799Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-12-17T23:52:07.0287238Z TEST_SHOWLOCALS=False 2024-12-17T23:52:07.0287518Z GITHUB_WORKFLOW=pull 2024-12-17T23:52:07.0287784Z DEBIAN_FRONTEND=noninteractive 2024-12-17T23:52:07.0288552Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_35f53d0f-01a1-4624-902f-fcd210c5ee1c 2024-12-17T23:52:07.0289219Z NO_TD=False 2024-12-17T23:52:07.0289471Z SKIP_SCCACHE_INITIALIZATION=1 2024-12-17T23:52:07.0289766Z _=/usr/bin/env 2024-12-17T23:52:07.0290001Z + echo 'Testing pytorch' 2024-12-17T23:52:07.0290280Z Testing pytorch 2024-12-17T23:52:07.0290539Z + export LANG=C.UTF-8 2024-12-17T23:52:07.0290801Z + LANG=C.UTF-8 2024-12-17T23:52:07.0291044Z + PR_NUMBER= 2024-12-17T23:52:07.0291436Z + [[ default == \d\e\f\a\u\l\t ]] 2024-12-17T23:52:07.0291750Z + export CUDA_VISIBLE_DEVICES=0 2024-12-17T23:52:07.0292050Z + CUDA_VISIBLE_DEVICES=0 2024-12-17T23:52:07.0292358Z + export HIP_VISIBLE_DEVICES=0 2024-12-17T23:52:07.0292654Z + HIP_VISIBLE_DEVICES=0 2024-12-17T23:52:07.0292920Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-12-17T23:52:07.0293244Z + [[ default == \s\l\o\w ]] 2024-12-17T23:52:07.0293693Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]] 2024-12-17T23:52:07.0294098Z + [[ linux-focal-py3.13-clang10 == *cuda* ]] 2024-12-17T23:52:07.0294458Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:52:07.0294858Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-17T23:52:07.0295197Z + [[ default == *crossref* ]] 2024-12-17T23:52:07.0295511Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:52:07.0295867Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-17T23:52:07.0296236Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]] 2024-12-17T23:52:07.0296594Z + pip_install --user ninja==1.10.2 2024-12-17T23:52:07.0297003Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:52:07.0297514Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2024-12-17T23:52:07.4561558Z Collecting ninja==1.10.2 2024-12-17T23:52:07.4725785Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-12-17T23:52:07.4806803Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-12-17T23:52:07.6326226Z Installing collected packages: ninja 2024-12-17T23:52:07.6403030Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-12-17T23:52:07.6404070Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-12-17T23:52:07.6440491Z Successfully installed ninja-1.10.2 2024-12-17T23:52:07.7177093Z + 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 2024-12-17T23:52:07.7178828Z + 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 2024-12-17T23:52:07.7179853Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]] 2024-12-17T23:52:07.7180212Z + install_tlparse 2024-12-17T23:52:07.7180489Z + pip_install --user tlparse==0.3.25 2024-12-17T23:52:07.7180911Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:52:07.7181419Z + python3 -m pip install --progress-bar off --user tlparse==0.3.25 2024-12-17T23:52:08.1363109Z Collecting tlparse==0.3.25 2024-12-17T23:52:08.1533607Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB) 2024-12-17T23:52:08.1688439Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-12-17T23:52:08.3569061Z Installing collected packages: tlparse 2024-12-17T23:52:08.4319073Z Successfully installed tlparse-0.3.25 2024-12-17T23:52:08.5052969Z ++ python -m site --user-base 2024-12-17T23:52:08.5199713Z + 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 2024-12-17T23:52:08.5200911Z + [[ linux-focal-py3.13-clang10 == *asan* ]] 2024-12-17T23:52:08.5201331Z + [[ linux-focal-py3.13-clang10 == *-debug* ]] 2024-12-17T23:52:08.5201714Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]] 2024-12-17T23:52:08.5202314Z + echo 'We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass' 2024-12-17T23:52:08.5203043Z We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass 2024-12-17T23:52:08.5203579Z + cd test 2024-12-17T23:52:08.5204200Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-12-17T23:52:09.8959944Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-12-17T23:52:09.8960381Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-12-17T23:52:09.8964280Z + DYNAMO_BENCHMARK_FLAGS=() 2024-12-17T23:52:09.8965357Z + [[ default == *pr_time_benchmarks* ]] 2024-12-17T23:52:09.8965687Z + [[ default == *dynamo_eager* ]] 2024-12-17T23:52:09.8966300Z + [[ default == *aot_eager* ]] 2024-12-17T23:52:09.8966615Z + [[ default == *aot_inductor* ]] 2024-12-17T23:52:09.8966928Z + [[ default == *inductor* ]] 2024-12-17T23:52:09.8967222Z + [[ default == *dynamic* ]] 2024-12-17T23:52:09.8967499Z + [[ default == *cpu* ]] 2024-12-17T23:52:09.8967816Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-12-17T23:52:09.8997038Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]] 2024-12-17T23:52:09.8997464Z + [[ linux-focal-py3.13-clang10 == *-bazel-* ]] 2024-12-17T23:52:09.9000034Z + cd test 2024-12-17T23:52:09.9000386Z + python -c 'import torch; print(torch.__config__.show())' 2024-12-17T23:52:11.0356010Z PyTorch built with: 2024-12-17T23:52:11.0356357Z - GCC 4.2 2024-12-17T23:52:11.0356608Z - C++ Version: 201703 2024-12-17T23:52:11.0356876Z - clang 10.0.0 2024-12-17T23:52:11.0357454Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-12-17T23:52:11.0358247Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2024-12-17T23:52:11.0358784Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-12-17T23:52:11.0359149Z - LAPACK is enabled (usually provided by MKL) 2024-12-17T23:52:11.0359491Z - NNPACK is enabled 2024-12-17T23:52:11.0359775Z - CPU capability usage: AVX512 2024-12-17T23:52:11.0365457Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7, 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-type-limits -Wno-array-bounds -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 -Wno-missing-braces -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=2.6.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, 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, 2024-12-17T23:52:11.0371303Z 2024-12-17T23:52:11.2689375Z + cd test 2024-12-17T23:52:11.2689942Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-12-17T23:52:12.4004778Z ATen/Parallel: 2024-12-17T23:52:12.4005152Z at::get_num_threads() : 8 2024-12-17T23:52:12.4005484Z at::get_num_interop_threads() : 8 2024-12-17T23:52:12.4005842Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-12-17T23:52:12.4006155Z omp_get_max_threads() : 8 2024-12-17T23:52:12.4006753Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-12-17T23:52:12.4007366Z mkl_get_max_threads() : 8 2024-12-17T23:52:12.4007776Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2024-12-17T23:52:12.4008266Z std::thread::hardware_concurrency() : 16 2024-12-17T23:52:12.4008609Z Environment variables: 2024-12-17T23:52:12.4008889Z OMP_NUM_THREADS : [not set] 2024-12-17T23:52:12.4009171Z MKL_NUM_THREADS : [not set] 2024-12-17T23:52:12.4009810Z ATen parallel backend: OpenMP 2024-12-17T23:52:12.4010020Z 2024-12-17T23:52:12.6584065Z + [[ default == *numpy_2* ]] 2024-12-17T23:52:12.6584480Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]] 2024-12-17T23:52:12.6584843Z + [[ default == *backward* ]] 2024-12-17T23:52:12.6585121Z + [[ default == *xla* ]] 2024-12-17T23:52:12.6585397Z + [[ default == *executorch* ]] 2024-12-17T23:52:12.6585962Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]] 2024-12-17T23:52:12.6586324Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]] 2024-12-17T23:52:12.6586683Z + [[ default == distributed ]] 2024-12-17T23:52:12.6586983Z + [[ default == *inductor_distributed* ]] 2024-12-17T23:52:12.6587327Z + [[ default == *inductor-halide* ]] 2024-12-17T23:52:12.6587666Z + [[ default == *inductor-triton-cpu* ]] 2024-12-17T23:52:12.6588040Z + [[ default == *inductor-micro-benchmark* ]] 2024-12-17T23:52:12.6588453Z + [[ default == *huggingface* ]] 2024-12-17T23:52:12.6588797Z + [[ default == *timm* ]] 2024-12-17T23:52:12.6589093Z + [[ default == *torchbench* ]] 2024-12-17T23:52:12.6589405Z + [[ default == *inductor_cpp_wrapper* ]] 2024-12-17T23:52:12.6589745Z + [[ default == *inductor* ]] 2024-12-17T23:52:12.6590041Z + [[ default == *dynamo_wrapped* ]] 2024-12-17T23:52:12.6590364Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:52:12.6590693Z + [[ 1 == 1 ]] 2024-12-17T23:52:12.6590931Z + [[ 5 -gt 1 ]] 2024-12-17T23:52:12.6591181Z + test_without_numpy 2024-12-17T23:52:12.6591458Z ++ dirname .ci/pytorch/test.sh 2024-12-17T23:52:12.6598564Z + pushd .ci/pytorch 2024-12-17T23:52:12.6598935Z ~/workspace/.ci/pytorch ~/workspace 2024-12-17T23:52:12.6599803Z + 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())' 2024-12-17T23:52:13.3698136Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:275: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:81.) 2024-12-17T23:52:13.3699649Z cpu = _conversion_method_template(device=torch.device("cpu")) 2024-12-17T23:52:13.9539756Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))' 2024-12-17T23:52:14.6682550Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:275: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:81.) 2024-12-17T23:52:14.6684027Z cpu = _conversion_method_template(device=torch.device("cpu")) 2024-12-17T23:52:15.0180354Z tensor([0., 1.]) 2024-12-17T23:52:15.2371293Z + [[ default == *dynamo_wrapped* ]] 2024-12-17T23:52:15.2371684Z + popd 2024-12-17T23:52:15.2371900Z ~/workspace 2024-12-17T23:52:15.2372139Z + install_torchvision 2024-12-17T23:52:15.2372430Z + local orig_preload 2024-12-17T23:52:15.2372687Z + local commit 2024-12-17T23:52:15.2374964Z ++ get_pinned_commit vision 2024-12-17T23:52:15.2375269Z ++ cat .github/ci_commit_pins/vision.txt 2024-12-17T23:52:15.2388424Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:15.2388807Z + orig_preload= 2024-12-17T23:52:15.2389050Z + '[' -n '' ']' 2024-12-17T23:52:15.2389660Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:15.2390392Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:52:15.2391216Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:15.5682074Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:15.5687048Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-uemvjx0o 2024-12-17T23:52:15.5705559Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-uemvjx0o 2024-12-17T23:52:16.9278824Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-12-17T23:52:16.9295887Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:18.2622013Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:18.5796359Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:52:20.5018566Z Preparing metadata (setup.py) ... [?25l- \ done 2024-12-17T23:52:20.5049659Z [?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) 2024-12-17T23:52:20.5053036Z Requirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.6.0a0+git0cdf8b1) 2024-12-17T23:52:20.5057031Z 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) 2024-12-17T23:52:20.5121822Z 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) 2024-12-17T23:52:20.5125362Z 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) 2024-12-17T23:52:20.5128065Z 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) 2024-12-17T23:52:20.5130755Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-12-17T23:52:20.5133494Z 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) 2024-12-17T23:52:20.5144006Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.1.0) 2024-12-17T23:52:20.5148667Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2024-12-17T23:52:20.5161500Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2024-12-17T23:52:20.5261238Z 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) 2024-12-17T23:52:20.5357977Z Building wheels for collected packages: torchvision 2024-12-17T23:52:55.0864062Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2024-12-17T23:52:55.0899659Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp313-cp313-linux_x86_64.whl size=1117899 sha256=70932162d31b269c0a6ce50a9769b0884915dd9cf72a57c1fc934396a3772ef7 2024-12-17T23:52:55.0900988Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/81/60/73/f2acb628a45eebe28dd9ff5468e774a0d5e194728570f8ff6f 2024-12-17T23:52:55.0934665Z Successfully built torchvision 2024-12-17T23:52:55.2203352Z Installing collected packages: torchvision 2024-12-17T23:52:55.6712029Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-12-17T23:52:55.7690415Z + '[' -n '' ']' 2024-12-17T23:52:55.7690762Z + test_python_shard 1 2024-12-17T23:52:55.7691031Z + [[ -z 5 ]] 2024-12-17T23:52:55.7691691Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 5 --verbose --upload-artifacts-while-running 2024-12-17T23:52:55.8713242Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-17T23:52:55.8714141Z import pkg_resources 2024-12-17T23:52:59.0910331Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-17T23:52:59.1378370Z Ignoring disabled issues: [''] 2024-12-17T23:52:59.1536401Z Found test times from artifacts 2024-12-17T23:52:59.2143814Z Found test times from artifacts 2024-12-17T23:52:59.2165050Z Running all tests 2024-12-17T23:52:59.2484135Z Running parallel tests on 3 processes 2024-12-17T23:52:59.2490003Z Name: tests to run (est. time: 107.59min) 2024-12-17T23:52:59.2490345Z Serial tests (44): 2024-12-17T23:52:59.2490602Z test_reductions 1/1 2024-12-17T23:52:59.2490909Z test_cpp_extensions_jit 1/1 2024-12-17T23:52:59.2491262Z test_cpp_extensions_open_device_registration 1/1 2024-12-17T23:52:59.2491627Z test_utils 1/1 2024-12-17T23:52:59.2491915Z test_cpp_extensions_stream_and_event 1/1 2024-12-17T23:52:59.2492262Z test_cpp_extensions_mtia_backend 1/1 2024-12-17T23:52:59.2492600Z test_cpp_api_parity 1/1 2024-12-17T23:52:59.2492895Z test_dispatch 1/1 2024-12-17T23:52:59.2493176Z inductor/test_cutlass_backend 1/1 2024-12-17T23:52:59.2493516Z inductor/test_benchmark_fusion 1/1 2024-12-17T23:52:59.2493842Z inductor/test_flex_attention 1/1 2024-12-17T23:52:59.2494167Z inductor/test_max_autotune 1/1 2024-12-17T23:52:59.2494504Z inductor/test_distributed_patterns 1/1 2024-12-17T23:52:59.2494848Z test_autograd_fallback 1/1 2024-12-17T23:52:59.2495151Z test_cuda_nvml_based_avail 1/1 2024-12-17T23:52:59.2495444Z test_cuda_trace 1/1 2024-12-17T23:52:59.2495717Z test_cuda_primary_ctx 1/1 2024-12-17T23:52:59.2496027Z test_tensor_creation_ops 1/1 2024-12-17T23:52:59.2496346Z test_cpp_extensions_aot_ninja 1/1 2024-12-17T23:52:59.2496671Z test_spectral_ops 1/1 2024-12-17T23:52:59.2496958Z test_cpp_extensions_aot_no_ninja 1/1 2024-12-17T23:52:59.2497295Z test_ci_sanity_check_fail 1/1 2024-12-17T23:52:59.2497628Z test_show_pickle 1/1 2024-12-17T23:52:59.2497919Z test_namedtuple_return_api 1/1 2024-12-17T23:52:59.2498240Z test_jit_disabled 1/1 2024-12-17T23:52:59.2498515Z test_autocast 1/1 2024-12-17T23:52:59.2498762Z test_torch 1/1 2024-12-17T23:52:59.2499013Z test_tensorexpr 1/1 2024-12-17T23:52:59.2499282Z test_fake_tensor 1/1 2024-12-17T23:52:59.2499553Z test_fx 1/1 2024-12-17T23:52:59.2499805Z test_multiprocessing 1/1 2024-12-17T23:52:59.2500083Z test_native_mha 1/1 2024-12-17T23:52:59.2500353Z test_sort_and_select 1/1 2024-12-17T23:52:59.2500639Z nn/test_pooling 1/1 2024-12-17T23:52:59.2500915Z test_python_dispatch 1/1 2024-12-17T23:52:59.2501202Z test_mobile_optimizer 1/1 2024-12-17T23:52:59.2501501Z nn/test_convolution 1/1 2024-12-17T23:52:59.2501779Z test_nn 1/1 2024-12-17T23:52:59.2502040Z test_multiprocessing_spawn 1/1 2024-12-17T23:52:59.2502352Z test_overrides 1/1 2024-12-17T23:52:59.2502630Z distributions/test_distributions 1/1 2024-12-17T23:52:59.2502983Z test_autoload_disable 1/1 2024-12-17T23:52:59.2503279Z doctests 1/1 2024-12-17T23:52:59.2503536Z test_autoload_enable 1/1 2024-12-17T23:52:59.2503830Z Parallel tests (76): 2024-12-17T23:52:59.2504097Z inductor/test_aot_inductor 1/1 2024-12-17T23:52:59.2504428Z inductor/test_aot_inductor_arrayref 1/3 2024-12-17T23:52:59.2504797Z inductor/test_aot_inductor_custom_ops 1/1 2024-12-17T23:52:59.2505156Z inductor/test_kernel_benchmark 1/1 2024-12-17T23:52:59.2505506Z inductor/test_torchinductor_opinfo 3/47 2024-12-17T23:52:59.2505868Z inductor/test_torchinductor_opinfo 5/47 2024-12-17T23:52:59.2506232Z inductor/test_torchinductor_opinfo 6/47 2024-12-17T23:52:59.2506816Z inductor/test_torchinductor_opinfo 18/47 2024-12-17T23:52:59.2507171Z inductor/test_torchinductor_opinfo 20/47 2024-12-17T23:52:59.2507536Z inductor/test_torchinductor_opinfo 21/47 2024-12-17T23:52:59.2507904Z inductor/test_torchinductor_opinfo 33/47 2024-12-17T23:52:59.2508269Z inductor/test_torchinductor_opinfo 35/47 2024-12-17T23:52:59.2508724Z inductor/test_torchinductor_opinfo 36/47 2024-12-17T23:52:59.2509076Z inductor/test_compiled_optimizers 1/1 2024-12-17T23:52:59.2509422Z dynamo/test_base_output 1/1 2024-12-17T23:52:59.2509752Z inductor/test_cooperative_reductions 1/1 2024-12-17T23:52:59.2510101Z test_import_stats 1/1 2024-12-17T23:52:59.2510388Z test_binary_ufuncs 1/1 2024-12-17T23:52:59.2510689Z export/test_functionalized_assertions 1/1 2024-12-17T23:52:59.2511045Z export/test_tree_utils 1/1 2024-12-17T23:52:59.2511361Z inductor/test_inductor_annotations 1/1 2024-12-17T23:52:59.2511696Z dynamo/test_nops 1/1 2024-12-17T23:52:59.2511979Z inductor/test_utils 1/1 2024-12-17T23:52:59.2512259Z inductor/test_indexing 1/1 2024-12-17T23:52:59.2512562Z inductor/test_pad_mm 1/1 2024-12-17T23:52:59.2512853Z dynamo/test_utils 1/1 2024-12-17T23:52:59.2513144Z inductor/test_compile_worker 1/1 2024-12-17T23:52:59.2513460Z functorch/test_aotdispatch 1/5 2024-12-17T23:52:59.2513786Z functorch/test_aotdispatch 3/5 2024-12-17T23:52:59.2514107Z functorch/test_aotdispatch 5/5 2024-12-17T23:52:59.2514463Z dynamo/test_cudagraphs_expandable_segments 1/1 2024-12-17T23:52:59.2514832Z functorch/test_ops 2/6 2024-12-17T23:52:59.2515106Z functorch/test_ops 5/6 2024-12-17T23:52:59.2515399Z inductor/test_snode_runtime 1/1 2024-12-17T23:52:59.2515716Z inductor/test_perf 1/1 2024-12-17T23:52:59.2515998Z inductor/test_fp8 1/1 2024-12-17T23:52:59.2516285Z test_ops_fwd_gradients 1/1 2024-12-17T23:52:59.2516591Z inductor/test_cpu_select_algorithm 8/17 2024-12-17T23:52:59.2516957Z inductor/test_cpu_select_algorithm 11/17 2024-12-17T23:52:59.2517316Z inductor/test_cpu_select_algorithm 15/17 2024-12-17T23:52:59.2517657Z inductor/test_halide 1/1 2024-12-17T23:52:59.2517963Z inductor/test_unbacked_symints 1/1 2024-12-17T23:52:59.2518267Z test_ops 2/6 2024-12-17T23:52:59.2518612Z test_ops 6/6 2024-12-17T23:52:59.2518860Z test_matmul_cuda 1/1 2024-12-17T23:52:59.2519134Z test_hub 1/1 2024-12-17T23:52:59.2519410Z torch_np/numpy_tests/core/test_einsum 1/1 2024-12-17T23:52:59.2519744Z test_out_dtype_op 1/1 2024-12-17T23:52:59.2520064Z torch_np/numpy_tests/lib/test_histograms 1/1 2024-12-17T23:52:59.2520417Z test_logging 1/1 2024-12-17T23:52:59.2520682Z test_namedtensor 1/1 2024-12-17T23:52:59.2520977Z distributions/test_constraints 1/1 2024-12-17T23:52:59.2521290Z test_decomp 1/10 2024-12-17T23:52:59.2521548Z test_decomp 6/10 2024-12-17T23:52:59.2521804Z test_decomp 10/10 2024-12-17T23:52:59.2522098Z torch_np/numpy_tests/core/test_getlimits 1/1 2024-12-17T23:52:59.2522463Z test_functional_optim 1/1 2024-12-17T23:52:59.2522752Z nn/test_load_state_dict 1/1 2024-12-17T23:52:59.2523057Z torch_np/test_function_base 1/1 2024-12-17T23:52:59.2523365Z test_mkldnn 1/1 2024-12-17T23:52:59.2523627Z test_xnnpack_integration 1/1 2024-12-17T23:52:59.2523929Z test_indexing 1/1 2024-12-17T23:52:59.2524225Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 2024-12-17T23:52:59.2524582Z test_datapipe 1/1 2024-12-17T23:52:59.2524882Z torch_np/numpy_tests/fft/test_pocketfft 1/1 2024-12-17T23:52:59.2525252Z torch_np/test_ndarray_methods 1/1 2024-12-17T23:52:59.2525609Z torch_np/numpy_tests/lib/test_twodim_base 1/1 2024-12-17T23:52:59.2525980Z torch_np/numpy_tests/core/test_dlpack 1/1 2024-12-17T23:52:59.2526361Z torch_np/numpy_tests/lib/test_index_tricks 1/1 2024-12-17T23:52:59.2526725Z nn/test_module_hooks 1/1 2024-12-17T23:52:59.2527021Z test_function_schema 1/1 2024-12-17T23:52:59.2527429Z torch_np/numpy_tests/fft/test_helper 1/1 2024-12-17T23:52:59.2527837Z torch_np/numpy_tests/core/test_scalar_methods 1/1 2024-12-17T23:52:59.2528213Z test_schema_check 1/1 2024-12-17T23:52:59.2528497Z test_quantization 2/2 2024-12-17T23:52:59.2528775Z optim/test_lrscheduler 1/1 2024-12-17T23:52:59.2529087Z Name: excluded (est. time: 0.0min) 2024-12-17T23:52:59.2529399Z Serial tests (0): 2024-12-17T23:52:59.2529735Z Parallel tests (0): 2024-12-17T23:52:59.2559564Z Running test_reductions 1/1 ... [2024-12-17 23:52:59.255660] 2024-12-17T23:52:59.2559997Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-17T23:52:59.2562361Z 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'] ... [2024-12-17 23:52:59.255990] 2024-12-17T23:56:27.0239942Z 2024-12-17T23:56:27.0240921Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_b75a6a755aeb1c34_.log 2024-12-17T23:56:27.1972890Z Running 4625 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, 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test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2024-12-17T23:56:27.3670559Z 2024-12-17T23:56:27.3670805Z Running test_cpp_extensions_jit 1/1 ... [2024-12-17 23:56:27.029153] 2024-12-17T23:56:27.3671278Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-17T23:56:27.3672357Z 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'] ... [2024-12-17 23:56:27.029451] 2024-12-17T23:56:58.5845402Z 2024-12-17T23:56:58.5846373Z 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_134ceb2c7d90c5de_.log 2024-12-17T23:56:58.5857935Z Running 27 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_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_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 2024-12-17T23:56:58.5868658Z 2024-12-17T23:56:58.5868964Z Running test_cpp_extensions_open_device_registration 1/1 ... [2024-12-17 23:56:58.584722] 2024-12-17T23:56:58.5869511Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-17T23:56:58.5870687Z 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'] ... [2024-12-17 23:56:58.584995] 2024-12-17T23:57:11.8163145Z 2024-12-17T23:57:11.8164326Z 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_24b02fa5d0daef1a_.log 2024-12-17T23:57:11.8177847Z 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 2024-12-17T23:57:11.8190629Z 2024-12-17T23:57:11.8190798Z Running test_utils 1/1 ... [2024-12-17 23:57:11.816475] 2024-12-17T23:57:11.8191195Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-17T23:57:11.8192223Z 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'] ... [2024-12-17 23:57:11.816761] 2024-12-18T00:00:23.8398520Z 2024-12-18T00:00:23.8399348Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_6fc7ce4bdf37c897_.log 2024-12-18T00:00:24.0512695Z Running 6020 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::TestExtensionUtils::test_external_module_register, test/test_utils.py::TestExtensionUtils::test_external_module_register_with_renamed_backend, 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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int8, 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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float16, 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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex64, 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_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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32, 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_det_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_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, 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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_bool, 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_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_uint8, 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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float64, 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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_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, 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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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int16, 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_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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, 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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, 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 2024-12-18T00:00:24.2958732Z 2024-12-18T00:00:24.2959244Z Running test_cpp_extensions_stream_and_event 1/1 ... [2024-12-18 00:00:23.845970] 2024-12-18T00:00:24.2960174Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:00:24.2962310Z 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'] ... [2024-12-18 00:00:23.846273] 2024-12-18T00:00:27.5153700Z 2024-12-18T00:00:27.5154780Z 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_d4bad11e142a1d44_.log 2024-12-18T00:00:27.5156145Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2024-12-18T00:00:27.5156723Z 2024-12-18T00:00:27.5156968Z Running test_cpp_extensions_mtia_backend 1/1 ... [2024-12-18 00:00:27.515518] 2024-12-18T00:00:27.5157450Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:00:27.5160480Z 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'] ... [2024-12-18 00:00:27.515796] 2024-12-18T00:00:31.0846387Z 2024-12-18T00:00:31.0847472Z 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_a8f88b3cdc409acf_.log 2024-12-18T00:00:31.0850902Z 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 2024-12-18T00:00:31.0853336Z 2024-12-18T00:00:31.0853561Z Running test_cpp_api_parity 1/1 ... [2024-12-18 00:00:31.084804] 2024-12-18T00:00:31.0853992Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:00:31.0855051Z 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'] ... [2024-12-18 00:00:31.085089] 2024-12-18T00:00:40.4617552Z 2024-12-18T00:00:40.4618656Z 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_c9d11a7dfa7bea9e_.log 2024-12-18T00:00:40.4808965Z 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, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_thnn, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_thnn_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_padding, 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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 2024-12-18T00:00:40.4996853Z 2024-12-18T00:00:40.4997061Z Running test_dispatch 1/1 ... [2024-12-18 00:00:40.462533] 2024-12-18T00:00:40.4997466Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:00:40.4998600Z 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'] ... [2024-12-18 00:00:40.462832] 2024-12-18T00:01:08.4127360Z 2024-12-18T00:01:08.4128224Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_2ea115ee73d69ed0_.log 2024-12-18T00:01:08.4138682Z 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 2024-12-18T00:01:08.4148386Z 2024-12-18T00:01:08.4148642Z Running inductor/test_cutlass_backend 1/1 ... [2024-12-18 00:01:08.412862] 2024-12-18T00:01:08.4149120Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:01:08.4150226Z 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'] ... [2024-12-18 00:01:08.413134] 2024-12-18T00:01:11.9079841Z 2024-12-18T00:01:11.9080923Z 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_d987449f509f071b_.log 2024-12-18T00:01:11.9081697Z 2024-12-18T00:01:11.9081943Z Running inductor/test_benchmark_fusion 1/1 ... [2024-12-18 00:01:11.908046] 2024-12-18T00:01:11.9082461Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:01:11.9085000Z 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'] ... [2024-12-18 00:01:11.908307] 2024-12-18T00:01:30.2961261Z 2024-12-18T00:01:30.2962365Z 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_d8f3a7b2f2b92453_.log 2024-12-18T00:01:30.2965640Z 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 2024-12-18T00:01:30.2968448Z 2024-12-18T00:01:30.2968692Z Running inductor/test_flex_attention 1/1 ... [2024-12-18 00:01:30.296318] 2024-12-18T00:01:30.2969163Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:01:30.2970389Z 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'] ... [2024-12-18 00:01:30.296629] 2024-12-18T00:01:34.7668372Z 2024-12-18T00:01:34.7669375Z 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_cf3142e242eb9d33_.log 2024-12-18T00:01:34.7931367Z Running 528 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, test/inductor/test_flex_attention.py::TestFlexAttention::test_aot_eager_gradcheck_score_mod1, test/inductor/test_flex_attention.py::TestFlexAttention::test_aot_eager_gradcheck_score_mod2, test/inductor/test_flex_attention.py::TestFlexAttention::test_aot_eager_gradcheck_score_mod3, test/inductor/test_flex_attention.py::TestFlexAttention::test_aot_eager_gradcheck_score_mod4, test/inductor/test_flex_attention.py::TestFlexAttention::test_aot_eager_gradcheck_score_mod5, test/inductor/test_flex_attention.py::TestFlexAttention::test_autograd_function_in_score_mod, test/inductor/test_flex_attention.py::TestFlexAttention::test_block_mask_non_divisible, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod0, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod1, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod4, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod5, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod6, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_automatic_dynamic_float32_score_mod7, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod0, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod1, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod4, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod5, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod6, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_bfloat16_score_mod7, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod0_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod0_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod0_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod0_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod1_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod2_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod2_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod2_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod2_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod3_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod3_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod3_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod3_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod4_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod4_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod4_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod4_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod5_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod5_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod5_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod5_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod6_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod6_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod6_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod6_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod7_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod7_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod7_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_bfloat16_score_mod7_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod0_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod0_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod0_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod0_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod1_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod2_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod2_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod2_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod2_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod3_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod3_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod3_BLOCK_SIZE_128, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod3_BLOCK_SIZE_256, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod4_BLOCK_SIZE2, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod4_BLOCK_SIZE3, test/inductor/test_flex_attention.py::TestFlexAttention::test_builtin_score_mods_different_block_size_float32_score_mod4_BLOCK_SIZE_128, 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test/inductor/test_flex_attention.py::TestPagedAttention::test_paged_builtin_score_mods_float32_score_mod4, test/inductor/test_flex_attention.py::TestPagedAttention::test_paged_builtin_score_mods_float32_score_mod5, test/inductor/test_flex_attention.py::TestPagedAttention::test_paged_builtin_score_mods_float32_score_mod6, test/inductor/test_flex_attention.py::TestPagedAttention::test_paged_builtin_score_mods_float32_score_mod7, test/inductor/test_flex_attention.py::TestPagedAttention::test_update, test/inductor/test_flex_attention.py::TestLearnableBiases::test_absolute_2d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_absolute_2d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_absolute_2d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_absolute_2d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_absolute_2d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_absolute_2d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_batch_head_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_batch_head_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_batch_head_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_batch_head_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_batch_head_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_batch_head_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_distinct_biases_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_distinct_biases_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_distinct_biases_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_distinct_biases_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_flipped_indexed_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_flipped_indexed_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_flipped_indexed_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_flipped_indexed_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_global_tokens_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_global_tokens_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_global_tokens_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_global_tokens_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_global_tokens_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_global_tokens_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_indirect_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_indirect_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_indirect_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_indirect_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_local_window_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_local_window_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_local_window_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_local_window_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_local_window_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_local_window_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default, test/inductor/test_flex_attention.py::TestLearnableBiases::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs, test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32, test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiases::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32, 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:float32 2024-12-18T00:01:34.8187072Z 2024-12-18T00:01:34.8187331Z Running inductor/test_max_autotune 1/1 ... [2024-12-18 00:01:34.767789] 2024-12-18T00:01:34.8187806Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:01:34.8188910Z 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'] ... [2024-12-18 00:01:34.768086] 2024-12-18T00:01:38.2881174Z 2024-12-18T00:01:38.2882184Z 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_d923d8277e7bc3f7_.log 2024-12-18T00:01:38.2882928Z 2024-12-18T00:01:38.2884717Z Running inductor/test_distributed_patterns 1/1 ... [2024-12-18 00:01:38.288251] 2024-12-18T00:01:38.2885260Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:01:38.2887686Z 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'] ... [2024-12-18 00:01:38.288576] 2024-12-18T00:02:20.4073484Z 2024-12-18T00:02:20.4074945Z 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_632d1b859862dea8_.log 2024-12-18T00:02:20.4085780Z 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 2024-12-18T00:02:20.4095013Z 2024-12-18T00:02:20.4095225Z Running test_autograd_fallback 1/1 ... [2024-12-18 00:02:20.407563] 2024-12-18T00:02:20.4095665Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:02:20.4096753Z 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'] ... [2024-12-18 00:02:20.407897] 2024-12-18T00:02:23.7263896Z 2024-12-18T00:02:23.7265084Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_c7fb5b3cad399bcb_.log 2024-12-18T00:02:23.7278826Z 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 2024-12-18T00:02:23.7290781Z 2024-12-18T00:02:23.7291024Z Running test_cuda_nvml_based_avail 1/1 ... [2024-12-18 00:02:23.726593] 2024-12-18T00:02:23.7291480Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:02:23.7292618Z 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'] ... [2024-12-18 00:02:23.726901] 2024-12-18T00:02:26.8034704Z 2024-12-18T00:02:26.8035934Z 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_461501a81a7d08d4_.log 2024-12-18T00:02:26.8036768Z Running 0 items in this shard: 2024-12-18T00:02:26.8037008Z 2024-12-18T00:02:26.8037530Z Running test_cuda_trace 1/1 ... [2024-12-18 00:02:26.803610] 2024-12-18T00:02:26.8038187Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:02:26.8041527Z 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'] ... [2024-12-18 00:02:26.803902] 2024-12-18T00:02:29.8728546Z 2024-12-18T00:02:29.8729683Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_de3a146ead00a74f_.log 2024-12-18T00:02:29.8730457Z Running 0 items in this shard: 2024-12-18T00:02:29.8730653Z 2024-12-18T00:02:29.8731600Z Running test_cuda_primary_ctx 1/1 ... [2024-12-18 00:02:29.873002] 2024-12-18T00:02:29.8732231Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:02:29.8735151Z 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'] ... [2024-12-18 00:02:29.873284] 2024-12-18T00:02:32.9632596Z 2024-12-18T00:02:32.9633793Z 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_667f834065ebb42e_.log 2024-12-18T00:02:32.9634592Z Running 0 items in this shard: 2024-12-18T00:02:32.9634789Z 2024-12-18T00:02:32.9635536Z Running test_tensor_creation_ops 1/1 ... [2024-12-18 00:02:32.963412] 2024-12-18T00:02:32.9636382Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:02:32.9639447Z 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'] ... [2024-12-18 00:02:32.963692] 2024-12-18T00:02:53.8550549Z 2024-12-18T00:02:53.8551517Z 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_ab80b516525c01de_.log 2024-12-18T00:02:53.8783803Z Running 625 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, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, 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_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_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 2024-12-18T00:02:53.9011333Z 2024-12-18T00:02:53.9011603Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-12-18 00:02:53.855945] 2024-12-18T00:02:56.3270486Z running install 2024-12-18T00:02:56.3271793Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:02:56.3272605Z !! 2024-12-18T00:02:56.3272722Z 2024-12-18T00:02:56.3273135Z ******************************************************************************** 2024-12-18T00:02:56.3273544Z Please avoid running ``setup.py`` directly. 2024-12-18T00:02:56.3274098Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:02:56.3274480Z standards-based tools. 2024-12-18T00:02:56.3274693Z 2024-12-18T00:02:56.3275147Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:02:56.3275699Z ******************************************************************************** 2024-12-18T00:02:56.3275947Z 2024-12-18T00:02:56.3276050Z !! 2024-12-18T00:02:56.3276271Z self.initialize_options() 2024-12-18T00:02:56.3403550Z running build 2024-12-18T00:02:56.3405414Z running build_py 2024-12-18T00:02:56.3478448Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:02:56.3480784Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:02:56.3483925Z running build_ext 2024-12-18T00:02:56.4594097Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:02:56.4594786Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313 2024-12-18T00:02:56.4904295Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:02:56.4905015Z Compiling objects... 2024-12-18T00:02:56.4905356Z Using envvar MAX_JOBS (14) as the number of workers... 2024-12-18T00:02:57.2850644Z [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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:02:57.2949497Z 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 2024-12-18T00:02:57.5550388Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:02:57.5858036Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:02:57.5870310Z Compiling objects... 2024-12-18T00:02:57.5870675Z Using envvar MAX_JOBS (14) as the number of workers... 2024-12-18T00:02:58.3107890Z [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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:02:58.3158780Z 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 2024-12-18T00:02:58.5489183Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:02:58.5799655Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:02:58.5800412Z Compiling objects... 2024-12-18T00:02:58.5800747Z Using envvar MAX_JOBS (14) as the number of workers... 2024-12-18T00:02:59.4485337Z [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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:02:59.4537144Z 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 2024-12-18T00:02:59.7241955Z running install_lib 2024-12-18T00:02:59.7318901Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2024-12-18T00:02:59.7323184Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:02:59.7326011Z 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 2024-12-18T00:02:59.7327634Z 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 2024-12-18T00:02:59.7360543Z 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 2024-12-18T00:02:59.7393771Z 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 2024-12-18T00:02:59.7432617Z 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 2024-12-18T00:02:59.7434992Z running install_egg_info 2024-12-18T00:02:59.7605472Z running egg_info 2024-12-18T00:02:59.7606346Z creating torch_test_cpp_extension.egg-info 2024-12-18T00:02:59.7679273Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:02:59.7682148Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:02:59.7683712Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:02:59.7685281Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:02:59.7686263Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:02:59.7757953Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:02:59.7765033Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:02:59.7766368Z 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 2024-12-18T00:02:59.7771962Z running install_scripts 2024-12-18T00:03:01.8702358Z running install 2024-12-18T00:03:01.8703394Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:03:01.8704200Z !! 2024-12-18T00:03:01.8704319Z 2024-12-18T00:03:01.8704466Z ******************************************************************************** 2024-12-18T00:03:01.8704906Z Please avoid running ``setup.py`` directly. 2024-12-18T00:03:01.8705382Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:03:01.8705780Z standards-based tools. 2024-12-18T00:03:01.8705990Z 2024-12-18T00:03:01.8706299Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:03:01.8706843Z ******************************************************************************** 2024-12-18T00:03:01.8707105Z 2024-12-18T00:03:01.8707201Z !! 2024-12-18T00:03:01.8707436Z self.initialize_options() 2024-12-18T00:03:01.8831858Z running build 2024-12-18T00:03:01.8832094Z running build_ext 2024-12-18T00:03:01.9913785Z building 'no_python_abi_suffix_test' extension 2024-12-18T00:03:01.9915782Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313 2024-12-18T00:03:02.0227571Z 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... 2024-12-18T00:03:02.0228402Z Compiling objects... 2024-12-18T00:03:02.0229039Z Using envvar MAX_JOBS (14) as the number of workers... 2024-12-18T00:03:02.1900855Z [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/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:03:02.1945697Z creating build/lib.linux-x86_64-cpython-313 2024-12-18T00:03:02.1950636Z 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 2024-12-18T00:03:02.2543812Z running install_lib 2024-12-18T00:03:02.2625552Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2024-12-18T00:03:02.2629097Z 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 2024-12-18T00:03:02.2634370Z running install_egg_info 2024-12-18T00:03:02.2803721Z running egg_info 2024-12-18T00:03:02.2804423Z creating no_python_abi_suffix_test.egg-info 2024-12-18T00:03:02.2878401Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-12-18T00:03:02.2881688Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-12-18T00:03:02.2883454Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-12-18T00:03:02.2884467Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:03:02.2955402Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:03:02.2962262Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:03:02.2963578Z 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 2024-12-18T00:03:02.2968376Z running install_scripts 2024-12-18T00:03:02.7503685Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:02.7506038Z 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'] ... [2024-12-18 00:03:02.750390] 2024-12-18T00:03:05.9153341Z 2024-12-18T00:03:05.9154327Z 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_07eaa196b2c20195_.log 2024-12-18T00:03:05.9161834Z Running 18 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_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::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 2024-12-18T00:03:05.9168093Z 2024-12-18T00:03:05.9168306Z Running test_spectral_ops 1/1 ... [2024-12-18 00:03:05.915540] 2024-12-18T00:03:05.9168717Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:05.9169766Z 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'] ... [2024-12-18 00:03:05.915808] 2024-12-18T00:03:15.9433395Z 2024-12-18T00:03:15.9434507Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_f203170fedf0a4e1_.log 2024-12-18T00:03:15.9525946Z Running 280 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, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_ifft_rfft_irfft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_input_modification_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_invalid_dtypes_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_plan_repeatable_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_int8, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_complex_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_float64, 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 2024-12-18T00:03:15.9616245Z 2024-12-18T00:03:15.9616493Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-12-18 00:03:15.943844] 2024-12-18T00:03:18.3828987Z running install 2024-12-18T00:03:18.3830290Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:03:18.3831113Z !! 2024-12-18T00:03:18.3831232Z 2024-12-18T00:03:18.3831374Z ******************************************************************************** 2024-12-18T00:03:18.3831780Z Please avoid running ``setup.py`` directly. 2024-12-18T00:03:18.3832182Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:03:18.3832565Z standards-based tools. 2024-12-18T00:03:18.3832768Z 2024-12-18T00:03:18.3833073Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:03:18.3833624Z ******************************************************************************** 2024-12-18T00:03:18.3833872Z 2024-12-18T00:03:18.3833970Z !! 2024-12-18T00:03:18.3834197Z self.initialize_options() 2024-12-18T00:03:18.3964206Z running build 2024-12-18T00:03:18.3974531Z running build_py 2024-12-18T00:03:18.4039894Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:03:18.4042164Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:03:18.4046181Z running build_ext 2024-12-18T00:03:18.4842203Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:03:18.4842643Z creating build/temp.linux-x86_64-cpython-313 2024-12-18T00:03:18.4848721Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:03:19.5098175Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T00:03:19.5099511Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T00:03:19.5100404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2024-12-18T00:03:19.5101276Z from extension.cpp:1: 2024-12-18T00:03:19.5104288Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T00:03:19.5105155Z extension.cpp:45:53: required from here 2024-12-18T00:03:19.5106879Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T00:03:19.5108320Z 1539 | class class_ : public detail::generic_type { 2024-12-18T00:03:19.5108683Z | ^~~~~~ 2024-12-18T00:03:19.5110343Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T00:03:19.5111746Z extension.cpp:45:53: required from here 2024-12-18T00:03:19.5115014Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2024-12-18T00:03:19.5117713Z 1599 | with_internals([&](internals &internals) { 2024-12-18T00:03:19.5118109Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:03:19.5118743Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T00:03:19.5119320Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:03:19.5119788Z 1601 | : internals.registered_types_cpp; 2024-12-18T00:03:19.5120228Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:03:19.5120661Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T00:03:19.5121078Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:03:19.5121486Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T00:03:19.5121876Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:03:19.5122213Z 1604 | }); 2024-12-18T00:03:19.5122474Z | ~ 2024-12-18T00:03:19.5125708Z 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 2024-12-18T00:03:19.9203202Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:03:19.9207883Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:03:20.9185917Z 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 2024-12-18T00:03:21.2822324Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:03:21.2826516Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:03:22.4567510Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:03:22.4568537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:03:22.4569356Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:03:22.4570265Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:03:22.4570984Z from rng_extension.cpp:6: 2024-12-18T00:03:22.4571814Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:03:22.4572628Z 1123 | # pragma unroll 2024-12-18T00:03:22.4572888Z | 2024-12-18T00:03:22.4573445Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T00:03:22.4574552Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:03:22.4575374Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:03:22.4576266Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:03:22.4577241Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:03:22.4578207Z from rng_extension.cpp:6: 2024-12-18T00:03:22.4579010Z /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] 2024-12-18T00:03:22.4579782Z 59 | #pragma unroll 2024-12-18T00:03:22.4580142Z | 2024-12-18T00:03:22.4580831Z /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] 2024-12-18T00:03:22.4581610Z 72 | #pragma unroll 2024-12-18T00:03:22.4581870Z | 2024-12-18T00:03:22.4582550Z /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] 2024-12-18T00:03:22.4583311Z 87 | #pragma unroll 2024-12-18T00:03:22.4583566Z | 2024-12-18T00:03:22.4584113Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T00:03:22.4585032Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:03:22.4585855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:03:22.4586659Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:03:22.4587566Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:03:22.4588255Z from rng_extension.cpp:6: 2024-12-18T00:03:22.4589066Z /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] 2024-12-18T00:03:22.4589873Z 153 | #pragma unroll 2024-12-18T00:03:22.4590130Z | 2024-12-18T00:03:22.4593298Z 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 2024-12-18T00:03:22.8731526Z running install_lib 2024-12-18T00:03:22.8810687Z 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 2024-12-18T00:03:22.8899875Z 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 2024-12-18T00:03:22.8987632Z 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 2024-12-18T00:03:22.9082321Z running install_egg_info 2024-12-18T00:03:22.9254012Z running egg_info 2024-12-18T00:03:22.9321984Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:03:22.9325339Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:03:22.9334554Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:03:22.9342716Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:03:22.9420116Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:03:22.9428408Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:03:22.9435533Z 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) 2024-12-18T00:03:22.9437239Z 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 2024-12-18T00:03:22.9442702Z running install_scripts 2024-12-18T00:03:25.0422326Z running install 2024-12-18T00:03:25.0423527Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:03:25.0424334Z !! 2024-12-18T00:03:25.0424451Z 2024-12-18T00:03:25.0424595Z ******************************************************************************** 2024-12-18T00:03:25.0424986Z Please avoid running ``setup.py`` directly. 2024-12-18T00:03:25.0425422Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:03:25.0425815Z standards-based tools. 2024-12-18T00:03:25.0426008Z 2024-12-18T00:03:25.0426330Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:03:25.0426885Z ******************************************************************************** 2024-12-18T00:03:25.0427135Z 2024-12-18T00:03:25.0427234Z !! 2024-12-18T00:03:25.0427454Z self.initialize_options() 2024-12-18T00:03:25.0553093Z running build 2024-12-18T00:03:25.0553357Z running build_ext 2024-12-18T00:03:25.1628891Z building 'no_python_abi_suffix_test' extension 2024-12-18T00:03:25.1943631Z 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... 2024-12-18T00:03:25.1944447Z Compiling objects... 2024-12-18T00:03:25.1944772Z Using envvar MAX_JOBS (14) as the number of workers... 2024-12-18T00:03:25.2207465Z ninja: no work to do. 2024-12-18T00:03:25.2250774Z 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 2024-12-18T00:03:25.2840634Z running install_lib 2024-12-18T00:03:25.2917889Z 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 2024-12-18T00:03:25.2922889Z running install_egg_info 2024-12-18T00:03:25.3093743Z running egg_info 2024-12-18T00:03:25.3161884Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-12-18T00:03:25.3165177Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-12-18T00:03:25.3173183Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-12-18T00:03:25.3249964Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:03:25.3257291Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:03:25.3264861Z 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) 2024-12-18T00:03:25.3266516Z 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 2024-12-18T00:03:25.3270997Z running install_scripts 2024-12-18T00:03:25.7403674Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:25.7406334Z 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'] ... [2024-12-18 00:03:25.740385] 2024-12-18T00:03:28.9641374Z 2024-12-18T00:03:28.9642374Z 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_1633b3ac3903e9b5_.log 2024-12-18T00:03:28.9649483Z Running 18 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_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::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 2024-12-18T00:03:28.9655886Z 2024-12-18T00:03:28.9656112Z Running test_ci_sanity_check_fail 1/1 ... [2024-12-18 00:03:28.964351] 2024-12-18T00:03:28.9656547Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:28.9657622Z 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'] ... [2024-12-18 00:03:28.964624] 2024-12-18T00:03:38.7824147Z Running test_show_pickle 1/1 ... [2024-12-18 00:03:38.782101] 2024-12-18T00:03:38.7824687Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:38.7825950Z 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'] ... [2024-12-18 00:03:38.782391] 2024-12-18T00:03:41.9005657Z 2024-12-18T00:03:41.9006571Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_5efcb096d6602fcc_.log 2024-12-18T00:03:41.9007570Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2024-12-18T00:03:41.9007993Z 2024-12-18T00:03:41.9009177Z Running test_namedtuple_return_api 1/1 ... [2024-12-18 00:03:41.900710] 2024-12-18T00:03:41.9009643Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:41.9011608Z 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'] ... [2024-12-18 00:03:41.900979] 2024-12-18T00:03:45.9202978Z 2024-12-18T00:03:45.9203956Z 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_7c5201aa6ccb8658_.log 2024-12-18T00:03:45.9206305Z 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 2024-12-18T00:03:45.9207558Z 2024-12-18T00:03:45.9207755Z Running test_jit_disabled 1/1 ... [2024-12-18 00:03:45.920438] 2024-12-18T00:03:45.9208252Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:45.9209317Z 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'] ... [2024-12-18 00:03:45.920721] 2024-12-18T00:03:49.1890897Z 2024-12-18T00:03:49.1891859Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_4ef75db1347f6da2_.log 2024-12-18T00:03:49.1893549Z 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 2024-12-18T00:03:49.1894576Z 2024-12-18T00:03:49.1894755Z Running test_autocast 1/1 ... [2024-12-18 00:03:49.189217] 2024-12-18T00:03:49.1895168Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:49.1896777Z 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'] ... [2024-12-18 00:03:49.189494] 2024-12-18T00:03:52.9585145Z 2024-12-18T00:03:52.9586047Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_7e7598ee0c67d789_.log 2024-12-18T00:03:52.9593986Z 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 2024-12-18T00:03:52.9600201Z 2024-12-18T00:03:52.9600364Z Running test_torch 1/1 ... [2024-12-18 00:03:52.958629] 2024-12-18T00:03:52.9600753Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:03:52.9601783Z 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'] ... [2024-12-18 00:03:52.958898] 2024-12-18T00:04:09.3972768Z 2024-12-18T00:04:09.3973807Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_9786cb8652e6068b_.log 2024-12-18T00:04:09.4292780Z Running 1022 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_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, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_ressurecting_clear, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, 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_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, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, 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, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_large_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float32, 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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 2024-12-18T00:04:09.4853364Z 2024-12-18T00:04:09.4853723Z Running test_tensorexpr 1/1 ... [2024-12-18 00:04:09.398406] 2024-12-18T00:04:09.4854465Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:04:09.4856437Z 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'] ... [2024-12-18 00:04:09.398687] 2024-12-18T00:04:56.9233543Z 2024-12-18T00:04:56.9234454Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_1199ceea0663b1e1_.log 2024-12-18T00:04:56.9255191Z 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 2024-12-18T00:04:56.9275283Z 2024-12-18T00:04:56.9275472Z Running test_fake_tensor 1/1 ... [2024-12-18 00:04:56.923553] 2024-12-18T00:04:56.9275885Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:04:56.9276932Z 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'] ... [2024-12-18 00:04:56.923828] 2024-12-18T00:05:03.3461611Z 2024-12-18T00:05:03.3462521Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_e9bccdea3070aa2a_.log 2024-12-18T00:05:03.3557904Z Running 238 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, 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_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_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_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_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_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device 2024-12-18T00:05:03.3652125Z 2024-12-18T00:05:03.3652280Z Running test_fx 1/1 ... [2024-12-18 00:05:03.346609] 2024-12-18T00:05:03.3652667Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:05:03.3653681Z 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'] ... [2024-12-18 00:05:03.346912] 2024-12-18T00:08:47.5848165Z 2024-12-18T00:08:47.5850484Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_a8078e4d266d6b56_.log 2024-12-18T00:08:47.6324456Z Running 1250 items in this shard: 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::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_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::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::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::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::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::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::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_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_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_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_det_singular_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_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 2024-12-18T00:08:47.6789910Z 2024-12-18T00:08:47.6790140Z Running test_multiprocessing 1/1 ... [2024-12-18 00:08:47.586426] 2024-12-18T00:08:47.6790594Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:08:47.6791705Z 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'] ... [2024-12-18 00:08:47.586702] 2024-12-18T00:09:44.9268711Z 2024-12-18T00:09:44.9269737Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_63ed9638627b9251_.log 2024-12-18T00:09:44.9285340Z 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 2024-12-18T00:09:44.9299950Z 2024-12-18T00:09:44.9300161Z Running test_native_mha 1/1 ... [2024-12-18 00:09:44.926867] 2024-12-18T00:09:44.9300593Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:09:44.9301742Z 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'] ... [2024-12-18 00:09:44.927173] 2024-12-18T00:09:48.1957530Z 2024-12-18T00:09:48.1958447Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_54fb79e4cc0484c9_.log 2024-12-18T00:09:48.1979967Z 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 2024-12-18T00:09:48.2000589Z 2024-12-18T00:09:48.2000791Z Running test_sort_and_select 1/1 ... [2024-12-18 00:09:48.195914] 2024-12-18T00:09:48.2001216Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:09:48.2002278Z 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'] ... [2024-12-18 00:09:48.196204] 2024-12-18T00:09:53.2170079Z 2024-12-18T00:09:53.2171036Z 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_247b15d303b0508a_.log 2024-12-18T00:09:53.2210274Z Running 112 items in this shard: 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 2024-12-18T00:09:53.2248044Z 2024-12-18T00:09:53.2248240Z Running nn/test_pooling 1/1 ... [2024-12-18 00:09:53.217225] 2024-12-18T00:09:53.2248674Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:09:53.2249729Z 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'] ... [2024-12-18 00:09:53.217516] 2024-12-18T00:10:20.8239094Z 2024-12-18T00:10:20.8240016Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_16d9292bc9cbc7b6_.log 2024-12-18T00:10:20.8278763Z Running 101 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_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_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_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 2024-12-18T00:10:20.8316786Z 2024-12-18T00:10:20.8316991Z Running test_python_dispatch 1/1 ... [2024-12-18 00:10:20.824141] 2024-12-18T00:10:20.8317432Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:10:20.8318557Z 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'] ... [2024-12-18 00:10:20.824445] 2024-12-18T00:10:34.5844862Z 2024-12-18T00:10:34.5846103Z PRINTING LOG FILE of test_python_dispatch 1/1 (test/test-reports/test_python_dispatch_1.1_0033ef2426482c3c_.log) 2024-12-18T00:10:34.5847433Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-b6196ef157b65f12.xml 2024-12-18T00:10:34.5848543Z ============================= test session starts ============================== 2024-12-18T00:10:34.5849368Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:10:34.5850121Z cachedir: .pytest_cache 2024-12-18T00:10:34.5851360Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:10:34.5852556Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:10:34.5853102Z configfile: pytest.ini 2024-12-18T00:10:34.5854314Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:10:34.5855212Z collecting ... collected 119 items 2024-12-18T00:10:34.5855636Z stepcurrent: Cannot find last run test, not skipping 2024-12-18T00:10:34.5928316Z Running 119 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_register_functional_op_error_cases, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional, 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 2024-12-18T00:10:34.5982663Z 2024-12-18T00:10:34.5983058Z test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed PASSED [0.0041s] [ 0%] 2024-12-18T00:10:34.5983868Z test_python_dispatch.py::TestPythonRegistration::test_alias_analysis PASSED [0.0073s] [ 1%] 2024-12-18T00:10:34.5984651Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library PASSED [0.0026s] [ 2%] 2024-12-18T00:10:34.5985539Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing PASSED [0.0020s] [ 3%] 2024-12-18T00:10:34.5986506Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing PASSED [0.0024s] [ 4%] 2024-12-18T00:10:34.5987433Z test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind PASSED [0.0018s] [ 5%] 2024-12-18T00:10:34.5988302Z test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable PASSED [0.0018s] [ 5%] 2024-12-18T00:10:34.5989186Z test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg PASSED [0.0022s] [ 6%] 2024-12-18T00:10:34.5990161Z test_python_dispatch.py::TestPythonRegistration::test_fallback PASSED [0.0073s] [ 7%] 2024-12-18T00:10:34.5990921Z test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough PASSED [0.0060s] [ 8%] 2024-12-18T00:10:34.5991712Z test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset PASSED [0.0052s] [ 9%] 2024-12-18T00:10:34.5992668Z test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls PASSED [0.0020s] [ 10%] 2024-12-18T00:10:34.5993510Z test_python_dispatch.py::TestPythonRegistration::test_finalizer PASSED [0.0020s] [ 10%] 2024-12-18T00:10:34.5995687Z test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142460 for platform(s) asan, linux, rocm, slow, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 11%] 2024-12-18T00:10:34.5999219Z test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142475 for platform(s) asan, linux, mac, macos, rocm, win, windows, slow. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 12%] 2024-12-18T00:10:34.6002464Z test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142495 for platform(s) linux, slow. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 13%] 2024-12-18T00:10:34.6005750Z test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142494 for platform(s) asan, linux, rocm, mac, macos, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 14%] 2024-12-18T00:10:34.6007892Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_error_cases PASSED [0.0036s] [ 15%] 2024-12-18T00:10:34.6010137Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142807 for platform(s) asan, linux, rocm, slow, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 15%] 2024-12-18T00:10:34.6013652Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/117834 for platform(s) asan, dynamo, linux, rocm, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 16%] 2024-12-18T00:10:34.6017248Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/117816 for platform(s) asan, dynamo, linux, rocm, slow, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 17%] 2024-12-18T00:10:34.6020853Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/117871 for platform(s) asan, dynamo, linux, rocm, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 18%] 2024-12-18T00:10:34.6023043Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint ('RERUN', {'yellow': True}) [0.0536s] [ 19%] 2024-12-18T00:10:34.6023975Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint ('RERUN', {'yellow': True}) [0.0132s] [ 19%] 2024-12-18T00:10:34.6024833Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint FAILED [0.0129s] [ 19%] 2024-12-18T00:10:34.6025282Z 2024-12-18T00:10:34.6025417Z ==================================== RERUNS ==================================== 2024-12-18T00:10:34.6025933Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:10:34.6026416Z Traceback (most recent call last): 2024-12-18T00:10:34.6027001Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in test_returning_symint 2024-12-18T00:10:34.6027640Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:10:34.6028025Z ~~~~~~~~~~^^^^^^ 2024-12-18T00:10:34.6028664Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6029393Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6029797Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6030423Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6031048Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6031674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6032307Z return prims._uniform_helper( 2024-12-18T00:10:34.6032617Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6032909Z x.shape, 2024-12-18T00:10:34.6033154Z ^^^^^^^^ 2024-12-18T00:10:34.6033393Z ...<4 lines>... 2024-12-18T00:10:34.6033646Z generator=generator, 2024-12-18T00:10:34.6033935Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6034206Z ) 2024-12-18T00:10:34.6034417Z ^ 2024-12-18T00:10:34.6034852Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6035413Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6035722Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6036349Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6037013Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6037625Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6038229Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6038939Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6039603Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6039960Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6040594Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6041305Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6041694Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6042309Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6042998Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6043609Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6044254Z return prims._uniform_helper( 2024-12-18T00:10:34.6044549Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6044843Z x.shape, 2024-12-18T00:10:34.6045081Z ^^^^^^^^ 2024-12-18T00:10:34.6045375Z ...<4 lines>... 2024-12-18T00:10:34.6045630Z generator=generator, 2024-12-18T00:10:34.6045900Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6046165Z ) 2024-12-18T00:10:34.6046371Z ^ 2024-12-18T00:10:34.6046817Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6047373Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6047668Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6048295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6048960Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6049564Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6050180Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6050797Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6051442Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6051800Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6052435Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6053147Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6053544Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6054148Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6054768Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6055374Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6056016Z return prims._uniform_helper( 2024-12-18T00:10:34.6056320Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6056604Z x.shape, 2024-12-18T00:10:34.6056829Z ^^^^^^^^ 2024-12-18T00:10:34.6057062Z ...<4 lines>... 2024-12-18T00:10:34.6057318Z generator=generator, 2024-12-18T00:10:34.6057596Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6057860Z ) 2024-12-18T00:10:34.6058052Z ^ 2024-12-18T00:10:34.6058498Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6059053Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6059363Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6059986Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6060641Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6061241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6061857Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6062476Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6063132Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6063476Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6064108Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6064814Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6065213Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6065827Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6066516Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6067115Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6067764Z return prims._uniform_helper( 2024-12-18T00:10:34.6068139Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6068431Z x.shape, 2024-12-18T00:10:34.6068670Z ^^^^^^^^ 2024-12-18T00:10:34.6068893Z ...<4 lines>... 2024-12-18T00:10:34.6069149Z generator=generator, 2024-12-18T00:10:34.6069431Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6069696Z ) 2024-12-18T00:10:34.6069904Z ^ 2024-12-18T00:10:34.6070338Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6070896Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6071205Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6071839Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6072508Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6073095Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6073895Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6074526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6075188Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6075551Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6076194Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6076895Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6077305Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6077940Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6078625Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6079243Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6079891Z return prims._uniform_helper( 2024-12-18T00:10:34.6080193Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6080482Z x.shape, 2024-12-18T00:10:34.6080722Z ^^^^^^^^ 2024-12-18T00:10:34.6080961Z ...<4 lines>... 2024-12-18T00:10:34.6081220Z generator=generator, 2024-12-18T00:10:34.6081495Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6081768Z ) 2024-12-18T00:10:34.6081976Z ^ 2024-12-18T00:10:34.6082419Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6082973Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6083276Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6083897Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6084558Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6085158Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6085779Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6086388Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6087048Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6087410Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6088050Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6088764Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6089311Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6089916Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6090535Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6091146Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6091871Z return prims._uniform_helper( 2024-12-18T00:10:34.6092178Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6092452Z x.shape, 2024-12-18T00:10:34.6092689Z ^^^^^^^^ 2024-12-18T00:10:34.6092926Z ...<4 lines>... 2024-12-18T00:10:34.6093187Z generator=generator, 2024-12-18T00:10:34.6093474Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6093730Z ) 2024-12-18T00:10:34.6093947Z ^ 2024-12-18T00:10:34.6094391Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6094946Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6095255Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6095868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6096533Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6097137Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6097754Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6098371Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6099027Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6099367Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6100002Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6100708Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6101109Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6101728Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6102346Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6102949Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6103595Z return prims._uniform_helper( 2024-12-18T00:10:34.6103899Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6104181Z x.shape, 2024-12-18T00:10:34.6104416Z ^^^^^^^^ 2024-12-18T00:10:34.6104640Z ...<4 lines>... 2024-12-18T00:10:34.6104894Z generator=generator, 2024-12-18T00:10:34.6105172Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6105433Z ) 2024-12-18T00:10:34.6105638Z ^ 2024-12-18T00:10:34.6106071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6106632Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6106941Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6107574Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6108234Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6108833Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6109466Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6110083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6110741Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6111097Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6111735Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6112504Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6112911Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6113527Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6114155Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6114827Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6115480Z return prims._uniform_helper( 2024-12-18T00:10:34.6115787Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6116073Z x.shape, 2024-12-18T00:10:34.6116315Z ^^^^^^^^ 2024-12-18T00:10:34.6116553Z ...<4 lines>... 2024-12-18T00:10:34.6116797Z generator=generator, 2024-12-18T00:10:34.6117084Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6117353Z ) 2024-12-18T00:10:34.6117570Z ^ 2024-12-18T00:10:34.6118027Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6118661Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6118976Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6119606Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6120273Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6120882Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6121503Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6122107Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6122767Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6123130Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6123770Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6124490Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6124890Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6125497Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6126115Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6126722Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6127358Z return prims._uniform_helper( 2024-12-18T00:10:34.6127662Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6127932Z x.shape, 2024-12-18T00:10:34.6128165Z ^^^^^^^^ 2024-12-18T00:10:34.6128399Z ...<4 lines>... 2024-12-18T00:10:34.6128653Z generator=generator, 2024-12-18T00:10:34.6128933Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6129186Z ) 2024-12-18T00:10:34.6129401Z ^ 2024-12-18T00:10:34.6129848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6130407Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6130714Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6131327Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6131988Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6132589Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6133203Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6133816Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6134469Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6134814Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6135526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6136236Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6136644Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6137327Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6137935Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6138547Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6139192Z return prims._uniform_helper( 2024-12-18T00:10:34.6139497Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6139781Z x.shape, 2024-12-18T00:10:34.6140018Z ^^^^^^^^ 2024-12-18T00:10:34.6140242Z ...<4 lines>... 2024-12-18T00:10:34.6140495Z generator=generator, 2024-12-18T00:10:34.6140775Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6141043Z ) 2024-12-18T00:10:34.6141236Z ^ 2024-12-18T00:10:34.6141679Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6142235Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6142544Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6143173Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6143834Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6144424Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6145042Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6145659Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6146313Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6146672Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6147292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6147997Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6148392Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6149008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6149622Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6150227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6150855Z return prims._uniform_helper( 2024-12-18T00:10:34.6151160Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6151443Z x.shape, 2024-12-18T00:10:34.6151678Z ^^^^^^^^ 2024-12-18T00:10:34.6151916Z ...<4 lines>... 2024-12-18T00:10:34.6152160Z generator=generator, 2024-12-18T00:10:34.6152449Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6152719Z ) 2024-12-18T00:10:34.6152929Z ^ 2024-12-18T00:10:34.6153376Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6153921Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6154226Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6154850Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6155513Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6156114Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6156721Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6157334Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6157990Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6158515Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6159159Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6159873Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6160319Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6160935Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6161557Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6162175Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6162820Z return prims._uniform_helper( 2024-12-18T00:10:34.6163118Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6217988Z x.shape, 2024-12-18T00:10:34.6218257Z ^^^^^^^^ 2024-12-18T00:10:34.6218527Z ...<4 lines>... 2024-12-18T00:10:34.6218779Z generator=generator, 2024-12-18T00:10:34.6219079Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6219346Z ) 2024-12-18T00:10:34.6219557Z ^ 2024-12-18T00:10:34.6220035Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6220588Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6220912Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6221549Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6222219Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6222841Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6223465Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6224070Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6224739Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6225097Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6225740Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6226450Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6226846Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6227469Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6228087Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6228697Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6229340Z return prims._uniform_helper( 2024-12-18T00:10:34.6229644Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6229914Z x.shape, 2024-12-18T00:10:34.6230158Z ^^^^^^^^ 2024-12-18T00:10:34.6230391Z ...<4 lines>... 2024-12-18T00:10:34.6230648Z generator=generator, 2024-12-18T00:10:34.6230933Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6231183Z ) 2024-12-18T00:10:34.6231394Z ^ 2024-12-18T00:10:34.6231846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6232415Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6232727Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6233345Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6234012Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6234622Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6235241Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6235863Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6236732Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6237091Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6237731Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6238619Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6239032Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6239655Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6240265Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6240882Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6241531Z return prims._uniform_helper( 2024-12-18T00:10:34.6241836Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6242122Z x.shape, 2024-12-18T00:10:34.6242346Z ^^^^^^^^ 2024-12-18T00:10:34.6242583Z ...<4 lines>... 2024-12-18T00:10:34.6242837Z generator=generator, 2024-12-18T00:10:34.6243120Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6243384Z ) 2024-12-18T00:10:34.6243579Z ^ 2024-12-18T00:10:34.6244035Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6244594Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6244903Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6245528Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6246176Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6246778Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6247403Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6248024Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6248679Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6249037Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6249661Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6250367Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6250767Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6251380Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6251999Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6252610Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6253243Z return prims._uniform_helper( 2024-12-18T00:10:34.6253550Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6253833Z x.shape, 2024-12-18T00:10:34.6254074Z ^^^^^^^^ 2024-12-18T00:10:34.6254309Z ...<4 lines>... 2024-12-18T00:10:34.6254552Z generator=generator, 2024-12-18T00:10:34.6254833Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6255098Z ) 2024-12-18T00:10:34.6255310Z ^ 2024-12-18T00:10:34.6255752Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6256314Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6256624Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6257251Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6257915Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6258526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6259207Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6259829Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6260487Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6260849Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6261539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6262255Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6262642Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6263263Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6263882Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6264493Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6265151Z return prims._uniform_helper( 2024-12-18T00:10:34.6265460Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6265732Z x.shape, 2024-12-18T00:10:34.6265970Z ^^^^^^^^ 2024-12-18T00:10:34.6266205Z ...<4 lines>... 2024-12-18T00:10:34.6266460Z generator=generator, 2024-12-18T00:10:34.6266729Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6266992Z ) 2024-12-18T00:10:34.6267209Z ^ 2024-12-18T00:10:34.6267658Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6268216Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6268514Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6269138Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6269802Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6270404Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6271023Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6271634Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6272290Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6272643Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6273267Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6274213Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6274616Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6275234Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6275850Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6276461Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6277094Z return prims._uniform_helper( 2024-12-18T00:10:34.6277397Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6277679Z x.shape, 2024-12-18T00:10:34.6277916Z ^^^^^^^^ 2024-12-18T00:10:34.6278149Z ...<4 lines>... 2024-12-18T00:10:34.6278449Z generator=generator, 2024-12-18T00:10:34.6278738Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6279006Z ) 2024-12-18T00:10:34.6279219Z ^ 2024-12-18T00:10:34.6279668Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6280211Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6280522Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6281146Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6281801Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6282398Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6283147Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6283759Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6284411Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6284832Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6285466Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6286176Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6286561Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6287178Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6287791Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6288402Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6289042Z return prims._uniform_helper( 2024-12-18T00:10:34.6289343Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6289613Z x.shape, 2024-12-18T00:10:34.6289843Z ^^^^^^^^ 2024-12-18T00:10:34.6290072Z ...<4 lines>... 2024-12-18T00:10:34.6290329Z generator=generator, 2024-12-18T00:10:34.6290600Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6290894Z ) 2024-12-18T00:10:34.6291176Z ^ 2024-12-18T00:10:34.6291633Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6292194Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6292497Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6293123Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6293786Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6294402Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6295018Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6295632Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6296278Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6296631Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6297264Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6297973Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6298370Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6298992Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6299602Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6300214Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6300860Z return prims._uniform_helper( 2024-12-18T00:10:34.6301167Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6301451Z x.shape, 2024-12-18T00:10:34.6301676Z ^^^^^^^^ 2024-12-18T00:10:34.6301962Z ...<4 lines>... 2024-12-18T00:10:34.6302214Z generator=generator, 2024-12-18T00:10:34.6302490Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6302754Z ) 2024-12-18T00:10:34.6302958Z ^ 2024-12-18T00:10:34.6303406Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6303961Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6304255Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6304879Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6305700Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6306369Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6306990Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6307666Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6308306Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6308658Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6309286Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6309995Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6310391Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6310994Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6311612Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6312213Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6312847Z return prims._uniform_helper( 2024-12-18T00:10:34.6313142Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6313414Z x.shape, 2024-12-18T00:10:34.6313641Z ^^^^^^^^ 2024-12-18T00:10:34.6313868Z ...<4 lines>... 2024-12-18T00:10:34.6314118Z generator=generator, 2024-12-18T00:10:34.6314390Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6314650Z ) 2024-12-18T00:10:34.6314842Z ^ 2024-12-18T00:10:34.6315280Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6315834Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6316137Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6316757Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6317410Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6318013Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6318726Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6319344Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6320002Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6320342Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6320979Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6321684Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6322074Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6322687Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6323302Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6323900Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6324538Z return prims._uniform_helper( 2024-12-18T00:10:34.6324834Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6325119Z x.shape, 2024-12-18T00:10:34.6325358Z ^^^^^^^^ 2024-12-18T00:10:34.6325578Z ...<4 lines>... 2024-12-18T00:10:34.6325829Z generator=generator, 2024-12-18T00:10:34.6326103Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6326360Z ) 2024-12-18T00:10:34.6326564Z ^ 2024-12-18T00:10:34.6326994Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6327543Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6327843Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6328542Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6329201Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6329794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6330415Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6331083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6331730Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6332080Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6332710Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6333406Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6333805Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6334422Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6335038Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6335643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6336284Z return prims._uniform_helper( 2024-12-18T00:10:34.6336573Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6336852Z x.shape, 2024-12-18T00:10:34.6337081Z ^^^^^^^^ 2024-12-18T00:10:34.6337310Z ...<4 lines>... 2024-12-18T00:10:34.6337560Z generator=generator, 2024-12-18T00:10:34.6337825Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6338083Z ) 2024-12-18T00:10:34.6338288Z ^ 2024-12-18T00:10:34.6338732Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6339285Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6339582Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6340205Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6340866Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6341466Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6342084Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6342683Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6343335Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6343686Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6344319Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6345022Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6345423Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6346026Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6346640Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6347246Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6347891Z return prims._uniform_helper( 2024-12-18T00:10:34.6348195Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6348464Z x.shape, 2024-12-18T00:10:34.6348695Z ^^^^^^^^ 2024-12-18T00:10:34.6348928Z ...<4 lines>... 2024-12-18T00:10:34.6349183Z generator=generator, 2024-12-18T00:10:34.6349458Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6349710Z ) 2024-12-18T00:10:34.6349914Z ^ 2024-12-18T00:10:34.6350362Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6350980Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6351286Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6351898Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6352555Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6353212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6353827Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6354439Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6355089Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6355432Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6356065Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6356764Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6357163Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6357772Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6358469Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6359074Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6359713Z return prims._uniform_helper( 2024-12-18T00:10:34.6360013Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6360292Z x.shape, 2024-12-18T00:10:34.6360530Z ^^^^^^^^ 2024-12-18T00:10:34.6360751Z ...<4 lines>... 2024-12-18T00:10:34.6361004Z generator=generator, 2024-12-18T00:10:34.6361282Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6361543Z ) 2024-12-18T00:10:34.6361751Z ^ 2024-12-18T00:10:34.6362182Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6362744Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6363048Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6363671Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6364328Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6364918Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6365530Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6366138Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6366790Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6367142Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6367774Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6368472Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6368872Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6369485Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6370097Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6370705Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6371345Z return prims._uniform_helper( 2024-12-18T00:10:34.6371632Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6371908Z x.shape, 2024-12-18T00:10:34.6372140Z ^^^^^^^^ 2024-12-18T00:10:34.6372372Z ...<4 lines>... 2024-12-18T00:10:34.6372609Z generator=generator, 2024-12-18T00:10:34.6372888Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6373145Z ) 2024-12-18T00:10:34.6373345Z ^ 2024-12-18T00:10:34.6373994Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6374668Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6374977Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6375602Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6376345Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6376947Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6377559Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6378154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6378802Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6379153Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6379786Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6380493Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6380889Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6381492Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6382114Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6382719Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6383358Z return prims._uniform_helper( 2024-12-18T00:10:34.6383654Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6383921Z x.shape, 2024-12-18T00:10:34.6384153Z ^^^^^^^^ 2024-12-18T00:10:34.6384384Z ...<4 lines>... 2024-12-18T00:10:34.6384633Z generator=generator, 2024-12-18T00:10:34.6384909Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6385155Z ) 2024-12-18T00:10:34.6385365Z ^ 2024-12-18T00:10:34.6385808Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6386361Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6386663Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6387281Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6387953Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6388557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6389178Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6389794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6390453Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6390796Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6391438Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6392145Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6392548Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6393173Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6393784Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6394398Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6395047Z return prims._uniform_helper( 2024-12-18T00:10:34.6395357Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6395641Z x.shape, 2024-12-18T00:10:34.6395866Z ^^^^^^^^ 2024-12-18T00:10:34.6396100Z ...<4 lines>... 2024-12-18T00:10:34.6396359Z generator=generator, 2024-12-18T00:10:34.6396638Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6396986Z ) 2024-12-18T00:10:34.6397179Z ^ 2024-12-18T00:10:34.6397627Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6398184Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6398571Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6399254Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6399916Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6400499Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6401114Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6401722Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6402406Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6402828Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6403448Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6404145Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6404538Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6405156Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6405774Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6406378Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6407010Z return prims._uniform_helper( 2024-12-18T00:10:34.6407313Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6407587Z x.shape, 2024-12-18T00:10:34.6407819Z ^^^^^^^^ 2024-12-18T00:10:34.6408053Z ...<4 lines>... 2024-12-18T00:10:34.6408293Z generator=generator, 2024-12-18T00:10:34.6408567Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6408821Z ) 2024-12-18T00:10:34.6409028Z ^ 2024-12-18T00:10:34.6409473Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6410017Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6410324Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6410951Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6411611Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6412210Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6412812Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6413521Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6414170Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6414524Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6415156Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6415857Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6416244Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6416853Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6417464Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6418067Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6418700Z return prims._uniform_helper( 2024-12-18T00:10:34.6419001Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6419268Z x.shape, 2024-12-18T00:10:34.6419499Z ^^^^^^^^ 2024-12-18T00:10:34.6419813Z ...<4 lines>... 2024-12-18T00:10:34.6420065Z generator=generator, 2024-12-18T00:10:34.6420328Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6420592Z ) 2024-12-18T00:10:34.6420797Z ^ 2024-12-18T00:10:34.6421243Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6421798Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6422165Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6422787Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6423443Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6424043Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6424648Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6425258Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6425904Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6426257Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6426888Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6427593Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6427989Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6428603Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6429205Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6429810Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6430449Z return prims._uniform_helper( 2024-12-18T00:10:34.6430748Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6431029Z x.shape, 2024-12-18T00:10:34.6431253Z ^^^^^^^^ 2024-12-18T00:10:34.6431483Z ...<4 lines>... 2024-12-18T00:10:34.6431733Z generator=generator, 2024-12-18T00:10:34.6432008Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6432267Z ) 2024-12-18T00:10:34.6432463Z ^ 2024-12-18T00:10:34.6432907Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6433470Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6433773Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6434396Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6435047Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6435649Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6436266Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6436880Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6437538Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6437889Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6438599Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6439309Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6439708Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6440319Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6440930Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6441534Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6442161Z return prims._uniform_helper( 2024-12-18T00:10:34.6442460Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6442806Z x.shape, 2024-12-18T00:10:34.6443036Z ^^^^^^^^ 2024-12-18T00:10:34.6443259Z ...<4 lines>... 2024-12-18T00:10:34.6443499Z generator=generator, 2024-12-18T00:10:34.6443775Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6444035Z ) 2024-12-18T00:10:34.6444236Z ^ 2024-12-18T00:10:34.6444720Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6445277Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6445584Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6446211Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6446862Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6447461Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6448062Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6448671Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6449319Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6449669Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6450297Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6451000Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6451384Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6451992Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6452603Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6453206Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6453843Z return prims._uniform_helper( 2024-12-18T00:10:34.6454132Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6454414Z x.shape, 2024-12-18T00:10:34.6454645Z ^^^^^^^^ 2024-12-18T00:10:34.6454872Z ...<4 lines>... 2024-12-18T00:10:34.6455116Z generator=generator, 2024-12-18T00:10:34.6455379Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6455633Z ) 2024-12-18T00:10:34.6455834Z ^ 2024-12-18T00:10:34.6456273Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6456832Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6457125Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6457748Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6458403Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6459005Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6459626Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6460237Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6460878Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6461230Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6461866Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6462570Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6462964Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6463567Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6464182Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6464784Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6465490Z return prims._uniform_helper( 2024-12-18T00:10:34.6465793Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6466076Z x.shape, 2024-12-18T00:10:34.6466298Z ^^^^^^^^ 2024-12-18T00:10:34.6466533Z ...<4 lines>... 2024-12-18T00:10:34.6466788Z generator=generator, 2024-12-18T00:10:34.6467071Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6467320Z ) 2024-12-18T00:10:34.6467577Z ^ 2024-12-18T00:10:34.6468028Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6468583Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6468892Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6469517Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6470167Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6470770Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6471390Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6472006Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6472656Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6472999Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6473850Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6474574Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6474975Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6475592Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6476210Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6476805Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6477457Z return prims._uniform_helper( 2024-12-18T00:10:34.6477765Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6478051Z x.shape, 2024-12-18T00:10:34.6478292Z ^^^^^^^^ 2024-12-18T00:10:34.6478572Z ...<4 lines>... 2024-12-18T00:10:34.6478832Z generator=generator, 2024-12-18T00:10:34.6479118Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6479384Z ) 2024-12-18T00:10:34.6479595Z ^ 2024-12-18T00:10:34.6480033Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6480593Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6480899Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6481520Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6482179Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6482768Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6483390Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6484000Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6484652Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6485006Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6485636Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6486328Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6486725Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6487339Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6487957Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6488567Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6489370Z return prims._uniform_helper( 2024-12-18T00:10:34.6489662Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6489944Z x.shape, 2024-12-18T00:10:34.6490184Z ^^^^^^^^ 2024-12-18T00:10:34.6490421Z ...<4 lines>... 2024-12-18T00:10:34.6490750Z generator=generator, 2024-12-18T00:10:34.6491021Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6491286Z ) 2024-12-18T00:10:34.6491492Z ^ 2024-12-18T00:10:34.6491939Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6492496Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6492791Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6493414Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6494071Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6494676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6495296Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6495896Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6496556Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6496911Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6497544Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6498246Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6498644Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6499249Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6499865Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6500476Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6501118Z return prims._uniform_helper( 2024-12-18T00:10:34.6501419Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6501687Z x.shape, 2024-12-18T00:10:34.6501921Z ^^^^^^^^ 2024-12-18T00:10:34.6502156Z ...<4 lines>... 2024-12-18T00:10:34.6502409Z generator=generator, 2024-12-18T00:10:34.6502688Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6502938Z ) 2024-12-18T00:10:34.6503147Z ^ 2024-12-18T00:10:34.6503590Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6504147Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6504452Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6505062Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6505724Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6506324Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6506939Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6507555Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6508208Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6508550Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6509179Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6509887Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6510284Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6510905Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6511590Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6512188Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6512834Z return prims._uniform_helper( 2024-12-18T00:10:34.6513137Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6513420Z x.shape, 2024-12-18T00:10:34.6513708Z ^^^^^^^^ 2024-12-18T00:10:34.6513931Z ...<4 lines>... 2024-12-18T00:10:34.6514181Z generator=generator, 2024-12-18T00:10:34.6514460Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6514726Z ) 2024-12-18T00:10:34.6514920Z ^ 2024-12-18T00:10:34.6515371Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6515932Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6516247Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6516877Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6517551Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6518145Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6518869Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6519493Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6520149Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6520510Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6521150Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6521848Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6522257Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6522879Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6523510Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6524119Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6524751Z return prims._uniform_helper( 2024-12-18T00:10:34.6525054Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6525345Z x.shape, 2024-12-18T00:10:34.6525581Z ^^^^^^^^ 2024-12-18T00:10:34.6525814Z ...<4 lines>... 2024-12-18T00:10:34.6526051Z generator=generator, 2024-12-18T00:10:34.6526327Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6526587Z ) 2024-12-18T00:10:34.6526791Z ^ 2024-12-18T00:10:34.6527232Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6527773Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6528078Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6528707Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6529367Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6529968Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6530581Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6531187Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6531846Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6532199Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6532835Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6533542Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6533929Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6534619Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6535238Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6535847Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6536489Z return prims._uniform_helper( 2024-12-18T00:10:34.6536845Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6537115Z x.shape, 2024-12-18T00:10:34.6537349Z ^^^^^^^^ 2024-12-18T00:10:34.6537584Z ...<4 lines>... 2024-12-18T00:10:34.6537844Z generator=generator, 2024-12-18T00:10:34.6538129Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6538382Z ) 2024-12-18T00:10:34.6538595Z ^ 2024-12-18T00:10:34.6539041Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6539598Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6539908Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6540519Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6541181Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6541778Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6542395Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6543008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6543664Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6544007Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6544643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6545346Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6545745Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6546359Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6546962Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6547568Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6548216Z return prims._uniform_helper( 2024-12-18T00:10:34.6548323Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6548415Z x.shape, 2024-12-18T00:10:34.6548502Z ^^^^^^^^ 2024-12-18T00:10:34.6548602Z ...<4 lines>... 2024-12-18T00:10:34.6548702Z generator=generator, 2024-12-18T00:10:34.6548807Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6548892Z ) 2024-12-18T00:10:34.6548978Z ^ 2024-12-18T00:10:34.6549309Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6549421Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6549552Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6549974Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6550086Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6550480Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6550592Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6550998Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6551131Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6551249Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6551660Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6551843Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6552014Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6552399Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6552515Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6552995Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6553120Z return prims._uniform_helper( 2024-12-18T00:10:34.6553215Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6553322Z x.shape, 2024-12-18T00:10:34.6553410Z ^^^^^^^^ 2024-12-18T00:10:34.6553504Z ...<4 lines>... 2024-12-18T00:10:34.6553621Z generator=generator, 2024-12-18T00:10:34.6553712Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6553813Z ) 2024-12-18T00:10:34.6553901Z ^ 2024-12-18T00:10:34.6554223Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6554350Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6554445Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6554876Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6554989Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6555383Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6555495Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6555887Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6556030Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6556135Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6556557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6556731Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6556852Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6557235Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6557341Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6557770Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6557882Z return prims._uniform_helper( 2024-12-18T00:10:34.6560272Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6560367Z x.shape, 2024-12-18T00:10:34.6560471Z ^^^^^^^^ 2024-12-18T00:10:34.6560564Z ...<4 lines>... 2024-12-18T00:10:34.6560668Z generator=generator, 2024-12-18T00:10:34.6560776Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6560862Z ) 2024-12-18T00:10:34.6560964Z ^ 2024-12-18T00:10:34.6561295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6561413Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6561522Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6561945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6562078Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6562459Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6562585Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6562983Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6563118Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6563240Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6563653Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6563909Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6564018Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6564419Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6564579Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6564987Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6565110Z return prims._uniform_helper( 2024-12-18T00:10:34.6565205Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6565308Z x.shape, 2024-12-18T00:10:34.6565396Z ^^^^^^^^ 2024-12-18T00:10:34.6565498Z ...<4 lines>... 2024-12-18T00:10:34.6565599Z generator=generator, 2024-12-18T00:10:34.6565690Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6565790Z ) 2024-12-18T00:10:34.6565880Z ^ 2024-12-18T00:10:34.6566212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6566319Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6566420Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6566855Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6566966Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6567356Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6567466Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6567872Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6568004Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6568109Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6568534Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6568704Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6568823Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6569212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6569328Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6569733Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6569843Z return prims._uniform_helper( 2024-12-18T00:10:34.6569947Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6570038Z x.shape, 2024-12-18T00:10:34.6570137Z ^^^^^^^^ 2024-12-18T00:10:34.6570228Z ...<4 lines>... 2024-12-18T00:10:34.6570343Z generator=generator, 2024-12-18T00:10:34.6570434Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6570524Z ) 2024-12-18T00:10:34.6570619Z ^ 2024-12-18T00:10:34.6570938Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6571056Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6571150Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6571574Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6571699Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6572082Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6572206Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6572602Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6572748Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6572852Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6573327Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6573509Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6573803Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6574304Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6574413Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6574829Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6574938Z return prims._uniform_helper( 2024-12-18T00:10:34.6575028Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6575131Z x.shape, 2024-12-18T00:10:34.6575220Z ^^^^^^^^ 2024-12-18T00:10:34.6575323Z ...<4 lines>... 2024-12-18T00:10:34.6575426Z generator=generator, 2024-12-18T00:10:34.6575533Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6575621Z ) 2024-12-18T00:10:34.6575708Z ^ 2024-12-18T00:10:34.6576044Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6576152Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6576259Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6576686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6576799Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6577188Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6577301Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6577707Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6577841Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6577963Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6578374Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6578545Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6578667Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6579056Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6579171Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6579574Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6579695Z return prims._uniform_helper( 2024-12-18T00:10:34.6579787Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6579877Z x.shape, 2024-12-18T00:10:34.6579978Z ^^^^^^^^ 2024-12-18T00:10:34.6580068Z ...<4 lines>... 2024-12-18T00:10:34.6580187Z generator=generator, 2024-12-18T00:10:34.6580280Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6580376Z ) 2024-12-18T00:10:34.6580461Z ^ 2024-12-18T00:10:34.6580780Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6580896Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6580993Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6581426Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6581537Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6581915Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6582036Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6582432Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6582644Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6582751Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6583171Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6583342Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6583529Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6583918Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6584021Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6584435Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6584545Z return prims._uniform_helper( 2024-12-18T00:10:34.6584651Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6584744Z x.shape, 2024-12-18T00:10:34.6584835Z ^^^^^^^^ 2024-12-18T00:10:34.6584939Z ...<4 lines>... 2024-12-18T00:10:34.6585041Z generator=generator, 2024-12-18T00:10:34.6585145Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6585230Z ) 2024-12-18T00:10:34.6585314Z ^ 2024-12-18T00:10:34.6585649Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6585760Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6585865Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6586287Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6586410Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6586790Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6586899Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6587302Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6587439Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6587555Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6587965Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6588151Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6588261Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6588657Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6588762Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6589167Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6589289Z return prims._uniform_helper( 2024-12-18T00:10:34.6589382Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6589487Z x.shape, 2024-12-18T00:10:34.6589576Z ^^^^^^^^ 2024-12-18T00:10:34.6589667Z ...<4 lines>... 2024-12-18T00:10:34.6589782Z generator=generator, 2024-12-18T00:10:34.6589876Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6602040Z ) 2024-12-18T00:10:34.6602181Z ^ 2024-12-18T00:10:34.6602573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6602701Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6602796Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6603248Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6603362Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6603762Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6603875Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6604410Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6604561Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6604670Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6605154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6605327Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6605456Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6605850Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6605956Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6606380Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6606492Z return prims._uniform_helper( 2024-12-18T00:10:34.6606604Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6606698Z x.shape, 2024-12-18T00:10:34.6606787Z ^^^^^^^^ 2024-12-18T00:10:34.6606894Z ...<4 lines>... 2024-12-18T00:10:34.6606999Z generator=generator, 2024-12-18T00:10:34.6607107Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6607195Z ) 2024-12-18T00:10:34.6607295Z ^ 2024-12-18T00:10:34.6607624Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6607735Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6607844Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6608268Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6608396Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6608777Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6608908Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6609305Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6609439Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6609560Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6609974Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6610162Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6610276Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6610674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6610780Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6611185Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6611313Z return prims._uniform_helper( 2024-12-18T00:10:34.6611406Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6611510Z x.shape, 2024-12-18T00:10:34.6611597Z ^^^^^^^^ 2024-12-18T00:10:34.6611689Z ...<4 lines>... 2024-12-18T00:10:34.6611801Z generator=generator, 2024-12-18T00:10:34.6611893Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6611990Z ) 2024-12-18T00:10:34.6612079Z ^ 2024-12-18T00:10:34.6612408Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6612515Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6612609Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6613042Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6613155Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6613546Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6613710Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6614119Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6614253Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6614358Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6614827Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6614997Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6615121Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6615505Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6615623Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6616028Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6616145Z return prims._uniform_helper( 2024-12-18T00:10:34.6616251Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6616343Z x.shape, 2024-12-18T00:10:34.6616449Z ^^^^^^^^ 2024-12-18T00:10:34.6616541Z ...<4 lines>... 2024-12-18T00:10:34.6616646Z generator=generator, 2024-12-18T00:10:34.6616754Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6616841Z ) 2024-12-18T00:10:34.6616940Z ^ 2024-12-18T00:10:34.6617261Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6617386Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6617481Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6617900Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6618025Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6618401Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6618530Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6618926Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6619072Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6619183Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6619593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6619773Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6619882Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6620277Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6620382Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6620803Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6620920Z return prims._uniform_helper( 2024-12-18T00:10:34.6621013Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6621119Z x.shape, 2024-12-18T00:10:34.6621207Z ^^^^^^^^ 2024-12-18T00:10:34.6621315Z ...<4 lines>... 2024-12-18T00:10:34.6621420Z generator=generator, 2024-12-18T00:10:34.6621513Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6621613Z ) 2024-12-18T00:10:34.6621701Z ^ 2024-12-18T00:10:34.6622036Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6622144Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6622250Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6622672Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6622785Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6623229Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6623340Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6623749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6623932Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6624054Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6624467Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6624638Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6624761Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6625149Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6625267Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6625677Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6625805Z return prims._uniform_helper( 2024-12-18T00:10:34.6625898Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6625989Z x.shape, 2024-12-18T00:10:34.6626093Z ^^^^^^^^ 2024-12-18T00:10:34.6626188Z ...<4 lines>... 2024-12-18T00:10:34.6626307Z generator=generator, 2024-12-18T00:10:34.6626398Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6626486Z ) 2024-12-18T00:10:34.6626588Z ^ 2024-12-18T00:10:34.6626911Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6627030Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6627127Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6627560Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6627678Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6628057Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6628181Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6628576Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6628721Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6628825Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6629248Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6629419Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6629529Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6629923Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6630032Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6630447Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6630557Z return prims._uniform_helper( 2024-12-18T00:10:34.6630661Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6630752Z x.shape, 2024-12-18T00:10:34.6630844Z ^^^^^^^^ 2024-12-18T00:10:34.6630952Z ...<4 lines>... 2024-12-18T00:10:34.6631058Z generator=generator, 2024-12-18T00:10:34.6631163Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6631248Z ) 2024-12-18T00:10:34.6631338Z ^ 2024-12-18T00:10:34.6631672Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6631781Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6631889Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6632310Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6632503Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6632881Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6632993Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6633446Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6633581Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6633701Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6634110Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6634296Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6634408Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6634794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6634917Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6635323Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6635447Z return prims._uniform_helper( 2024-12-18T00:10:34.6635539Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6635648Z x.shape, 2024-12-18T00:10:34.6635738Z ^^^^^^^^ 2024-12-18T00:10:34.6635829Z ...<4 lines>... 2024-12-18T00:10:34.6635946Z generator=generator, 2024-12-18T00:10:34.6636037Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6636138Z ) 2024-12-18T00:10:34.6636222Z ^ 2024-12-18T00:10:34.6636543Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6636663Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6636756Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6637196Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6637311Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6637703Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6637815Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6638212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6638478Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6638589Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6639018Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6639191Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6639317Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6639706Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6639811Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6640232Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6640343Z return prims._uniform_helper( 2024-12-18T00:10:34.6640460Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6640552Z x.shape, 2024-12-18T00:10:34.6640655Z ^^^^^^^^ 2024-12-18T00:10:34.6640748Z ...<4 lines>... 2024-12-18T00:10:34.6640851Z generator=generator, 2024-12-18T00:10:34.6640959Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6641047Z ) 2024-12-18T00:10:34.6641149Z ^ 2024-12-18T00:10:34.6641472Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6641582Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6641749Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6642176Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6642306Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6642686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6642865Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6643260Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6643394Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6643515Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6643927Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6644116Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6644231Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6644628Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6644733Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6645154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6645270Z return prims._uniform_helper( 2024-12-18T00:10:34.6645365Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6645472Z x.shape, 2024-12-18T00:10:34.6645566Z ^^^^^^^^ 2024-12-18T00:10:34.6645661Z ...<4 lines>... 2024-12-18T00:10:34.6645764Z generator=generator, 2024-12-18T00:10:34.6645868Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6645952Z ) 2024-12-18T00:10:34.6646049Z ^ 2024-12-18T00:10:34.6646368Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6646477Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6646585Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6647006Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6647130Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6647511Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6647632Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6648027Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6648159Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6648280Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6648686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6648866Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6648980Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6649373Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6649476Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6649883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6650005Z return prims._uniform_helper( 2024-12-18T00:10:34.6650100Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6650201Z x.shape, 2024-12-18T00:10:34.6650289Z ^^^^^^^^ 2024-12-18T00:10:34.6650392Z ...<4 lines>... 2024-12-18T00:10:34.6650491Z generator=generator, 2024-12-18T00:10:34.6650583Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6650680Z ) 2024-12-18T00:10:34.6650766Z ^ 2024-12-18T00:10:34.6651096Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6651252Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6651346Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6651781Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6651893Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6652326Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6652436Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6652839Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6652970Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6653076Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6653496Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6653669Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6653790Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6654173Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6654289Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6654696Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6654803Z return prims._uniform_helper( 2024-12-18T00:10:34.6654906Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6654997Z x.shape, 2024-12-18T00:10:34.6655096Z ^^^^^^^^ 2024-12-18T00:10:34.6655186Z ...<4 lines>... 2024-12-18T00:10:34.6655298Z generator=generator, 2024-12-18T00:10:34.6655388Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6655474Z ) 2024-12-18T00:10:34.6655569Z ^ 2024-12-18T00:10:34.6655889Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6656011Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6656103Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6656520Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6656648Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6657025Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6657146Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6657539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6657680Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6657785Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6658192Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6658376Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6658486Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6658879Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6658986Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6659399Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6659507Z return prims._uniform_helper( 2024-12-18T00:10:34.6659599Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6659699Z x.shape, 2024-12-18T00:10:34.6659790Z ^^^^^^^^ 2024-12-18T00:10:34.6659894Z ...<4 lines>... 2024-12-18T00:10:34.6659997Z generator=generator, 2024-12-18T00:10:34.6660103Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6660187Z ) 2024-12-18T00:10:34.6660322Z ^ 2024-12-18T00:10:34.6660650Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6660757Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6660861Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6661329Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6661441Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6661831Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6661942Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6662343Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6662478Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6662595Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6663006Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6663176Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6663300Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6663686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6663803Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6664205Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6664325Z return prims._uniform_helper( 2024-12-18T00:10:34.6664415Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6664506Z x.shape, 2024-12-18T00:10:34.6664605Z ^^^^^^^^ 2024-12-18T00:10:34.6664697Z ...<4 lines>... 2024-12-18T00:10:34.6664811Z generator=generator, 2024-12-18T00:10:34.6664901Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6664990Z ) 2024-12-18T00:10:34.6665084Z ^ 2024-12-18T00:10:34.6665407Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6665524Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6665616Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6666053Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6666163Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6666540Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6666662Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6667055Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6667198Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6667303Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6667728Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6667895Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6668006Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6668408Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6668511Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6668928Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6669035Z return prims._uniform_helper( 2024-12-18T00:10:34.6669140Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6669225Z x.shape, 2024-12-18T00:10:34.6669311Z ^^^^^^^^ 2024-12-18T00:10:34.6669412Z ...<4 lines>... 2024-12-18T00:10:34.6669512Z generator=generator, 2024-12-18T00:10:34.6669680Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6669767Z ) 2024-12-18T00:10:34.6669854Z ^ 2024-12-18T00:10:34.6670192Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6670297Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6670388Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6670869Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6670979Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6671363Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6671470Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6671868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6671999Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6672107Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6672521Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6672688Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6672807Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6673192Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6673304Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6673933Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6674052Z return prims._uniform_helper( 2024-12-18T00:10:34.6674149Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6674239Z x.shape, 2024-12-18T00:10:34.6674334Z ^^^^^^^^ 2024-12-18T00:10:34.6674423Z ...<4 lines>... 2024-12-18T00:10:34.6674531Z generator=generator, 2024-12-18T00:10:34.6674630Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6674713Z ) 2024-12-18T00:10:34.6674806Z ^ 2024-12-18T00:10:34.6675126Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6675242Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6675336Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6675756Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6675878Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6676254Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6676372Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6676764Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6676907Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6677010Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6677418Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6677598Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6677712Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6678106Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6678205Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6678687Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6678795Z return prims._uniform_helper( 2024-12-18T00:10:34.6678889Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6678988Z x.shape, 2024-12-18T00:10:34.6679188Z ^^^^^^^^ 2024-12-18T00:10:34.6679291Z ...<4 lines>... 2024-12-18T00:10:34.6679387Z generator=generator, 2024-12-18T00:10:34.6679475Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6679570Z ) 2024-12-18T00:10:34.6679650Z ^ 2024-12-18T00:10:34.6679980Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6680145Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6680247Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6680668Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6680777Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6681161Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6681268Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6681671Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6681803Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6681918Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6682327Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6682494Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6682613Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6682991Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6683104Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6683508Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6683623Z return prims._uniform_helper( 2024-12-18T00:10:34.6683717Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6683806Z x.shape, 2024-12-18T00:10:34.6683904Z ^^^^^^^^ 2024-12-18T00:10:34.6683994Z ...<4 lines>... 2024-12-18T00:10:34.6684104Z generator=generator, 2024-12-18T00:10:34.6684191Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6684271Z ) 2024-12-18T00:10:34.6684367Z ^ 2024-12-18T00:10:34.6684688Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6684799Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6684890Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6685317Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6685425Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6685801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6685916Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6686309Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6686446Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6686547Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6686962Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6687134Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6687243Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6687630Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6687733Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6688146Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6688254Z return prims._uniform_helper( 2024-12-18T00:10:34.6688407Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6688494Z x.shape, 2024-12-18T00:10:34.6688579Z ^^^^^^^^ 2024-12-18T00:10:34.6688677Z ...<4 lines>... 2024-12-18T00:10:34.6688778Z generator=generator, 2024-12-18T00:10:34.6688878Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6688962Z ) 2024-12-18T00:10:34.6689045Z ^ 2024-12-18T00:10:34.6689416Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6689524Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6689623Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6690045Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6690165Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6690539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6690652Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6691048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6691176Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6691288Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6691698Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6691871Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6691978Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6692357Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6692465Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6692869Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6692988Z return prims._uniform_helper( 2024-12-18T00:10:34.6693076Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6693175Z x.shape, 2024-12-18T00:10:34.6693263Z ^^^^^^^^ 2024-12-18T00:10:34.6693350Z ...<4 lines>... 2024-12-18T00:10:34.6693461Z generator=generator, 2024-12-18T00:10:34.6693548Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6693644Z ) 2024-12-18T00:10:34.6693726Z ^ 2024-12-18T00:10:34.6694044Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6694158Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6694249Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6694676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6694782Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6695168Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6695282Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6695674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6695813Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6695913Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6696333Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6696498Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6696620Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6697003Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6697101Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6697515Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6697670Z return prims._uniform_helper( 2024-12-18T00:10:34.6697776Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6697868Z x.shape, 2024-12-18T00:10:34.6697961Z ^^^^^^^^ 2024-12-18T00:10:34.6698051Z ...<4 lines>... 2024-12-18T00:10:34.6698147Z generator=generator, 2024-12-18T00:10:34.6698314Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6698397Z ) 2024-12-18T00:10:34.6698494Z ^ 2024-12-18T00:10:34.6698813Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6698915Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6699017Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6699432Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6699551Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6699927Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6700047Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6700438Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6700567Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6700682Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6701086Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6701264Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6701373Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6701762Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6701863Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6702273Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6702385Z return prims._uniform_helper( 2024-12-18T00:10:34.6702476Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6702567Z x.shape, 2024-12-18T00:10:34.6702656Z ^^^^^^^^ 2024-12-18T00:10:34.6702754Z ...<4 lines>... 2024-12-18T00:10:34.6702857Z generator=generator, 2024-12-18T00:10:34.6702948Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6703038Z ) 2024-12-18T00:10:34.6703123Z ^ 2024-12-18T00:10:34.6703452Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6703557Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6703649Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6704073Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6704183Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6704573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6704682Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6705079Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6705214Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6705318Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6705737Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6705906Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6706025Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6706407Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6706517Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6706969Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6707089Z return prims._uniform_helper( 2024-12-18T00:10:34.6707177Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6707269Z x.shape, 2024-12-18T00:10:34.6707366Z ^^^^^^^^ 2024-12-18T00:10:34.6707560Z ...<4 lines>... 2024-12-18T00:10:34.6707676Z generator=generator, 2024-12-18T00:10:34.6707763Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6707847Z ) 2024-12-18T00:10:34.6707944Z ^ 2024-12-18T00:10:34.6708261Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6708378Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6708467Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6708887Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6709011Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6709392Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6709516Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6709914Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6710059Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6710159Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6710582Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6710749Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6710859Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6711249Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6711354Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6711771Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6711879Z return prims._uniform_helper( 2024-12-18T00:10:34.6711978Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6712068Z x.shape, 2024-12-18T00:10:34.6712157Z ^^^^^^^^ 2024-12-18T00:10:34.6712259Z ...<4 lines>... 2024-12-18T00:10:34.6712357Z generator=generator, 2024-12-18T00:10:34.6712458Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6712541Z ) 2024-12-18T00:10:34.6712624Z ^ 2024-12-18T00:10:34.6712951Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6713056Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6713155Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6713573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6713685Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6714073Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6714180Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6714582Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6714715Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6714830Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6715236Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6715412Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6715520Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6715903Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6716065Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6716468Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6716580Z return prims._uniform_helper( 2024-12-18T00:10:34.6716670Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6716813Z x.shape, 2024-12-18T00:10:34.6716900Z ^^^^^^^^ 2024-12-18T00:10:34.6716988Z ...<4 lines>... 2024-12-18T00:10:34.6717098Z generator=generator, 2024-12-18T00:10:34.6717191Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6717283Z ) 2024-12-18T00:10:34.6717367Z ^ 2024-12-18T00:10:34.6717685Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6717798Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6717890Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6718414Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6718527Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6718912Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6719021Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6719414Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6719552Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6719658Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6720074Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6720241Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6720359Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6720745Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6720846Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6721259Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6721368Z return prims._uniform_helper( 2024-12-18T00:10:34.6721469Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6721555Z x.shape, 2024-12-18T00:10:34.6721653Z ^^^^^^^^ 2024-12-18T00:10:34.6721739Z ...<4 lines>... 2024-12-18T00:10:34.6721840Z generator=generator, 2024-12-18T00:10:34.6721937Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6722021Z ) 2024-12-18T00:10:34.6722117Z ^ 2024-12-18T00:10:34.6722433Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6722540Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6722641Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6723062Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6723182Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6723554Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6723678Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6724066Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6724195Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6724311Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6724716Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6724894Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6725052Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6725443Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6725545Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6725945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6726111Z return prims._uniform_helper( 2024-12-18T00:10:34.6726200Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6726303Z x.shape, 2024-12-18T00:10:34.6726387Z ^^^^^^^^ 2024-12-18T00:10:34.6726476Z ...<4 lines>... 2024-12-18T00:10:34.6726593Z generator=generator, 2024-12-18T00:10:34.6726680Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6726777Z ) 2024-12-18T00:10:34.6726859Z ^ 2024-12-18T00:10:34.6727188Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6727291Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6727385Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6727812Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6727921Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6728312Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6728420Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6728826Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6728957Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6729060Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6729479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6729652Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6729781Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6730165Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6730276Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6730682Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6730787Z return prims._uniform_helper( 2024-12-18T00:10:34.6730894Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6730982Z x.shape, 2024-12-18T00:10:34.6731077Z ^^^^^^^^ 2024-12-18T00:10:34.6731166Z ...<4 lines>... 2024-12-18T00:10:34.6731263Z generator=generator, 2024-12-18T00:10:34.6731364Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6731446Z ) 2024-12-18T00:10:34.6731539Z ^ 2024-12-18T00:10:34.6731856Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6731974Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6732067Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6732483Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6732607Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6732981Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6733102Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6733492Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6733632Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6733743Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6734150Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6734378Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6734486Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6734881Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6734984Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6735454Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6735563Z return prims._uniform_helper( 2024-12-18T00:10:34.6735651Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6735749Z x.shape, 2024-12-18T00:10:34.6735835Z ^^^^^^^^ 2024-12-18T00:10:34.6735934Z ...<4 lines>... 2024-12-18T00:10:34.6736033Z generator=generator, 2024-12-18T00:10:34.6736120Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6736214Z ) 2024-12-18T00:10:34.6736292Z ^ 2024-12-18T00:10:34.6736621Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6736731Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6736837Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6737259Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6737370Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6737762Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6737867Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6738269Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6738398Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6738522Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6738934Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6739107Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6739231Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6739615Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6739733Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6740140Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6740259Z return prims._uniform_helper( 2024-12-18T00:10:34.6740354Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6740445Z x.shape, 2024-12-18T00:10:34.6740547Z ^^^^^^^^ 2024-12-18T00:10:34.6740638Z ...<4 lines>... 2024-12-18T00:10:34.6740754Z generator=generator, 2024-12-18T00:10:34.6740847Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6740934Z ) 2024-12-18T00:10:34.6741038Z ^ 2024-12-18T00:10:34.6741357Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6741481Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6741576Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6742005Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6742123Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6742497Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6742621Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6743014Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6743161Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6743269Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6743743Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6743913Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6744022Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6744470Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6744579Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6744992Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6745102Z return prims._uniform_helper( 2024-12-18T00:10:34.6745208Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6745301Z x.shape, 2024-12-18T00:10:34.6745388Z ^^^^^^^^ 2024-12-18T00:10:34.6745489Z ...<4 lines>... 2024-12-18T00:10:34.6745591Z generator=generator, 2024-12-18T00:10:34.6745693Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6745781Z ) 2024-12-18T00:10:34.6745866Z ^ 2024-12-18T00:10:34.6746193Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6746303Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6746408Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6746833Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6746960Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6747336Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6747444Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6747846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6747976Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6748091Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6748500Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6748678Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6748785Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6749170Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6749284Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6749686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6749805Z return prims._uniform_helper( 2024-12-18T00:10:34.6749901Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6750035Z x.shape, 2024-12-18T00:10:34.6750123Z ^^^^^^^^ 2024-12-18T00:10:34.6750217Z ...<4 lines>... 2024-12-18T00:10:34.6750329Z generator=generator, 2024-12-18T00:10:34.6750419Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6750517Z ) 2024-12-18T00:10:34.6750605Z ^ 2024-12-18T00:10:34.6750926Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6751046Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6751136Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6751571Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6751683Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6752071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6752183Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6752574Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6752718Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6752872Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6753291Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6753461Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6753580Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6754011Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6754116Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6754534Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6754643Z return prims._uniform_helper( 2024-12-18T00:10:34.6754747Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6754837Z x.shape, 2024-12-18T00:10:34.6754934Z ^^^^^^^^ 2024-12-18T00:10:34.6755023Z ...<4 lines>... 2024-12-18T00:10:34.6755128Z generator=generator, 2024-12-18T00:10:34.6755229Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6755316Z ) 2024-12-18T00:10:34.6755413Z ^ 2024-12-18T00:10:34.6755733Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6755840Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6755944Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6756364Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6756487Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6756864Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6756986Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6757377Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6757512Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6757627Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6758035Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6758213Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6758409Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6758810Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6758912Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6759330Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6759437Z return prims._uniform_helper( 2024-12-18T00:10:34.6759528Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6759630Z x.shape, 2024-12-18T00:10:34.6759720Z ^^^^^^^^ 2024-12-18T00:10:34.6759822Z ...<4 lines>... 2024-12-18T00:10:34.6759922Z generator=generator, 2024-12-18T00:10:34.6760011Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6760108Z ) 2024-12-18T00:10:34.6760190Z ^ 2024-12-18T00:10:34.6760523Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6760632Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6760725Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6761157Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6761268Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6761651Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6761759Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6762158Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6762347Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6762451Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6762868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6763086Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6763209Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6763595Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6763713Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6764116Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6764234Z return prims._uniform_helper( 2024-12-18T00:10:34.6764329Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6764421Z x.shape, 2024-12-18T00:10:34.6764521Z ^^^^^^^^ 2024-12-18T00:10:34.6764610Z ...<4 lines>... 2024-12-18T00:10:34.6764721Z generator=generator, 2024-12-18T00:10:34.6764811Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6764894Z ) 2024-12-18T00:10:34.6764989Z ^ 2024-12-18T00:10:34.6765308Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6765428Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6765520Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6765937Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6766061Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6766437Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6766558Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6766953Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6767092Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6767200Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6767616Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6767791Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6767899Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6768291Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6768395Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6768810Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6768919Z return prims._uniform_helper( 2024-12-18T00:10:34.6769026Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6769116Z x.shape, 2024-12-18T00:10:34.6769204Z ^^^^^^^^ 2024-12-18T00:10:34.6769301Z ...<4 lines>... 2024-12-18T00:10:34.6769404Z generator=generator, 2024-12-18T00:10:34.6769506Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6769590Z ) 2024-12-18T00:10:34.6769675Z ^ 2024-12-18T00:10:34.6770008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6770114Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6770221Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6770640Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6770760Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6771136Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6771312Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6771716Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6771849Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6771965Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6772424Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6772608Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6772718Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6773105Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6773227Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6773794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6773922Z return prims._uniform_helper( 2024-12-18T00:10:34.6774014Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6774119Z x.shape, 2024-12-18T00:10:34.6774206Z ^^^^^^^^ 2024-12-18T00:10:34.6774298Z ...<4 lines>... 2024-12-18T00:10:34.6774420Z generator=generator, 2024-12-18T00:10:34.6774511Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6774612Z ) 2024-12-18T00:10:34.6774700Z ^ 2024-12-18T00:10:34.6775024Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6775147Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6775240Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6775674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6775786Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6776176Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6776290Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6776685Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6776832Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6776942Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6777367Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6777537Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6777662Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6778047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6778153Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6778574Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6778689Z return prims._uniform_helper( 2024-12-18T00:10:34.6778796Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6778885Z x.shape, 2024-12-18T00:10:34.6778987Z ^^^^^^^^ 2024-12-18T00:10:34.6779082Z ...<4 lines>... 2024-12-18T00:10:34.6779185Z generator=generator, 2024-12-18T00:10:34.6779286Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6779376Z ) 2024-12-18T00:10:34.6779474Z ^ 2024-12-18T00:10:34.6779790Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6779900Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6780001Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6780420Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6780542Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6780916Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6781139Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6781531Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6781661Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6781846Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6782257Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6782439Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6782549Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6782945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6783047Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6783446Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6783570Z return prims._uniform_helper( 2024-12-18T00:10:34.6783663Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6783765Z x.shape, 2024-12-18T00:10:34.6783852Z ^^^^^^^^ 2024-12-18T00:10:34.6783942Z ...<4 lines>... 2024-12-18T00:10:34.6784060Z generator=generator, 2024-12-18T00:10:34.6784150Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6784246Z ) 2024-12-18T00:10:34.6784330Z ^ 2024-12-18T00:10:34.6784659Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6784768Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6784861Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6785296Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6785407Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6785795Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6785905Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6786312Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6786449Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6786556Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6786976Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6787144Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6787264Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6787648Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6787763Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6788170Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6788278Z return prims._uniform_helper( 2024-12-18T00:10:34.6788382Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6788471Z x.shape, 2024-12-18T00:10:34.6788566Z ^^^^^^^^ 2024-12-18T00:10:34.6788659Z ...<4 lines>... 2024-12-18T00:10:34.6788763Z generator=generator, 2024-12-18T00:10:34.6788861Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6788947Z ) 2024-12-18T00:10:34.6789040Z ^ 2024-12-18T00:10:34.6789358Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6789475Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6789569Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6789987Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6790157Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6790536Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6790658Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6791049Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6791237Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6791345Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6791756Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6791940Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6792052Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6792447Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6792551Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6792966Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6793075Z return prims._uniform_helper( 2024-12-18T00:10:34.6793167Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6793267Z x.shape, 2024-12-18T00:10:34.6793355Z ^^^^^^^^ 2024-12-18T00:10:34.6793456Z ...<4 lines>... 2024-12-18T00:10:34.6793554Z generator=generator, 2024-12-18T00:10:34.6793643Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6793736Z ) 2024-12-18T00:10:34.6793819Z ^ 2024-12-18T00:10:34.6794147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6794252Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6794355Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6794775Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6794888Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6795274Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6795385Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6795790Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6795922Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6796037Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6796447Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6796619Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6796740Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6797123Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6797243Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6797643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6797763Z return prims._uniform_helper( 2024-12-18T00:10:34.6797857Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6797948Z x.shape, 2024-12-18T00:10:34.6798046Z ^^^^^^^^ 2024-12-18T00:10:34.6798140Z ...<4 lines>... 2024-12-18T00:10:34.6798253Z generator=generator, 2024-12-18T00:10:34.6798433Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6798520Z ) 2024-12-18T00:10:34.6798618Z ^ 2024-12-18T00:10:34.6798937Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6799055Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6799146Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6799643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6799755Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6800131Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6800253Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6800695Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6800839Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6800947Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6801368Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6801541Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6801651Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6802048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6802153Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6802565Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6802677Z return prims._uniform_helper( 2024-12-18T00:10:34.6802787Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6802877Z x.shape, 2024-12-18T00:10:34.6802966Z ^^^^^^^^ 2024-12-18T00:10:34.6803066Z ...<4 lines>... 2024-12-18T00:10:34.6803169Z generator=generator, 2024-12-18T00:10:34.6803276Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6803361Z ) 2024-12-18T00:10:34.6803447Z ^ 2024-12-18T00:10:34.6803778Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6803887Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6803990Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6804414Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6804534Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6804909Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6805024Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6805423Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6805553Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6805666Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6806074Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6806257Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6806364Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6806753Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6806870Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6807273Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6807394Z return prims._uniform_helper( 2024-12-18T00:10:34.6807496Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6807598Z x.shape, 2024-12-18T00:10:34.6807687Z ^^^^^^^^ 2024-12-18T00:10:34.6807779Z ...<4 lines>... 2024-12-18T00:10:34.6807896Z generator=generator, 2024-12-18T00:10:34.6807988Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6808088Z ) 2024-12-18T00:10:34.6808176Z ^ 2024-12-18T00:10:34.6808496Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6808619Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6808777Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6809212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6809325Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6809763Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6809872Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6810268Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6810417Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6810525Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6810951Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6811123Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6811253Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6811638Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6811757Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6812167Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6812279Z return prims._uniform_helper( 2024-12-18T00:10:34.6812387Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6812480Z x.shape, 2024-12-18T00:10:34.6812584Z ^^^^^^^^ 2024-12-18T00:10:34.6812678Z ...<4 lines>... 2024-12-18T00:10:34.6812782Z generator=generator, 2024-12-18T00:10:34.6812886Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6812976Z ) 2024-12-18T00:10:34.6813074Z ^ 2024-12-18T00:10:34.6813393Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6813506Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6813612Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6814034Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6814158Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6814540Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6814663Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6815054Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6815187Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6815306Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6815715Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6815898Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6816008Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6816399Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6816503Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6816924Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6817032Z return prims._uniform_helper( 2024-12-18T00:10:34.6817125Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6817227Z x.shape, 2024-12-18T00:10:34.6817315Z ^^^^^^^^ 2024-12-18T00:10:34.6817417Z ...<4 lines>... 2024-12-18T00:10:34.6817519Z generator=generator, 2024-12-18T00:10:34.6817610Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6817707Z ) 2024-12-18T00:10:34.6817791Z ^ 2024-12-18T00:10:34.6818122Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6818281Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6818373Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6818808Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6818967Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6819360Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6819474Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6819883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6820016Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6820125Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6820548Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6820722Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6820845Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6821230Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6821353Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6821755Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6821877Z return prims._uniform_helper( 2024-12-18T00:10:34.6821973Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6822065Z x.shape, 2024-12-18T00:10:34.6822165Z ^^^^^^^^ 2024-12-18T00:10:34.6822257Z ...<4 lines>... 2024-12-18T00:10:34.6822370Z generator=generator, 2024-12-18T00:10:34.6822460Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6822546Z ) 2024-12-18T00:10:34.6822645Z ^ 2024-12-18T00:10:34.6822963Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6823083Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6823175Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6823593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6823722Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6824097Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6824220Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6824613Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6824759Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6824864Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6825289Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6825459Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6825570Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6825968Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6826071Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6826487Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6826596Z return prims._uniform_helper( 2024-12-18T00:10:34.6826703Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6826793Z x.shape, 2024-12-18T00:10:34.6826882Z ^^^^^^^^ 2024-12-18T00:10:34.6826983Z ...<4 lines>... 2024-12-18T00:10:34.6827085Z generator=generator, 2024-12-18T00:10:34.6827188Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6827325Z ) 2024-12-18T00:10:34.6827410Z ^ 2024-12-18T00:10:34.6827741Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6827849Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6827952Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6828420Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6828547Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6828923Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6829034Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6829439Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6829571Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6829691Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6830104Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6830283Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6830392Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6830781Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6830897Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6831304Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6831425Z return prims._uniform_helper( 2024-12-18T00:10:34.6831517Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6831620Z x.shape, 2024-12-18T00:10:34.6831709Z ^^^^^^^^ 2024-12-18T00:10:34.6831800Z ...<4 lines>... 2024-12-18T00:10:34.6831916Z generator=generator, 2024-12-18T00:10:34.6832010Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6832107Z ) 2024-12-18T00:10:34.6832190Z ^ 2024-12-18T00:10:34.6832508Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6832629Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6832721Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6833154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6833265Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6833652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6833762Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6834154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6834296Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6834405Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6834826Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6834994Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6835115Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6835500Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6835605Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6836022Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6836130Z return prims._uniform_helper( 2024-12-18T00:10:34.6836235Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6836325Z x.shape, 2024-12-18T00:10:34.6836425Z ^^^^^^^^ 2024-12-18T00:10:34.6836516Z ...<4 lines>... 2024-12-18T00:10:34.6836673Z generator=generator, 2024-12-18T00:10:34.6836776Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6836860Z ) 2024-12-18T00:10:34.6836960Z ^ 2024-12-18T00:10:34.6837281Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6837391Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6837559Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6837983Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6838109Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6838557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6838685Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6839081Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6839216Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6839386Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6839796Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6839979Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6840093Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6840491Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6840593Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6840996Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6841117Z return prims._uniform_helper( 2024-12-18T00:10:34.6841210Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6841312Z x.shape, 2024-12-18T00:10:34.6841404Z ^^^^^^^^ 2024-12-18T00:10:34.6841507Z ...<4 lines>... 2024-12-18T00:10:34.6841610Z generator=generator, 2024-12-18T00:10:34.6841700Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6841797Z ) 2024-12-18T00:10:34.6841885Z ^ 2024-12-18T00:10:34.6842218Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6842335Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6842430Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6842867Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6842982Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6843375Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6843487Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6843895Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6844031Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6844141Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6844568Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6844745Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6844870Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6845257Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6845376Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6845782Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6845896Z return prims._uniform_helper( 2024-12-18T00:10:34.6846006Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6846155Z x.shape, 2024-12-18T00:10:34.6846258Z ^^^^^^^^ 2024-12-18T00:10:34.6846354Z ...<4 lines>... 2024-12-18T00:10:34.6846459Z generator=generator, 2024-12-18T00:10:34.6846569Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6846656Z ) 2024-12-18T00:10:34.6846757Z ^ 2024-12-18T00:10:34.6847881Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6848010Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6848104Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6848532Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6848661Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6849041Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6849166Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6849565Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6849712Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6849819Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6850232Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6850415Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6850529Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6850932Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6851036Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6851452Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6851563Z return prims._uniform_helper( 2024-12-18T00:10:34.6851659Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6851761Z x.shape, 2024-12-18T00:10:34.6851852Z ^^^^^^^^ 2024-12-18T00:10:34.6851953Z ...<4 lines>... 2024-12-18T00:10:34.6852053Z generator=generator, 2024-12-18T00:10:34.6852142Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6852238Z ) 2024-12-18T00:10:34.6852321Z ^ 2024-12-18T00:10:34.6852658Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6852764Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6852873Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6853295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6853406Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6853795Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6853906Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6854317Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6854454Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6854574Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6854987Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6855158Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6855284Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6855669Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6855785Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6856189Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6856373Z return prims._uniform_helper( 2024-12-18T00:10:34.6856467Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6856559Z x.shape, 2024-12-18T00:10:34.6856659Z ^^^^^^^^ 2024-12-18T00:10:34.6856750Z ...<4 lines>... 2024-12-18T00:10:34.6856864Z generator=generator, 2024-12-18T00:10:34.6856959Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6857044Z ) 2024-12-18T00:10:34.6857252Z ^ 2024-12-18T00:10:34.6857575Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6857696Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6857790Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6858222Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6858334Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6858712Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6858839Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6859236Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6859380Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6859488Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6859916Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6860090Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6860201Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6860597Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6860705Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6861123Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6861238Z return prims._uniform_helper( 2024-12-18T00:10:34.6861343Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6861433Z x.shape, 2024-12-18T00:10:34.6861522Z ^^^^^^^^ 2024-12-18T00:10:34.6861625Z ...<4 lines>... 2024-12-18T00:10:34.6861731Z generator=generator, 2024-12-18T00:10:34.6861836Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6861926Z ) 2024-12-18T00:10:34.6862011Z ^ 2024-12-18T00:10:34.6862344Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6862452Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6862557Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6862978Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6863101Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6863479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6863595Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6863999Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6864129Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6864250Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6864662Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6864844Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6864956Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6865339Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6865454Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6865855Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6866028Z return prims._uniform_helper( 2024-12-18T00:10:34.6866120Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6866223Z x.shape, 2024-12-18T00:10:34.6866309Z ^^^^^^^^ 2024-12-18T00:10:34.6866400Z ...<4 lines>... 2024-12-18T00:10:34.6866512Z generator=generator, 2024-12-18T00:10:34.6866650Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6866751Z ) 2024-12-18T00:10:34.6866836Z ^ 2024-12-18T00:10:34.6867157Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6867274Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6867365Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6867803Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6867913Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6868303Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6868412Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6868803Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6868949Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6869055Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6869476Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6869647Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6869768Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6870152Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6870267Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6870675Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6870784Z return prims._uniform_helper( 2024-12-18T00:10:34.6870888Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6870980Z x.shape, 2024-12-18T00:10:34.6871081Z ^^^^^^^^ 2024-12-18T00:10:34.6871175Z ...<4 lines>... 2024-12-18T00:10:34.6871282Z generator=generator, 2024-12-18T00:10:34.6871387Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6871471Z ) 2024-12-18T00:10:34.6871568Z ^ 2024-12-18T00:10:34.6871890Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6871997Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6872104Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6872525Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6872655Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6873037Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6873167Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6873712Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6873851Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6873970Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6874382Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6874566Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6874677Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6875069Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6875271Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6875689Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6875799Z return prims._uniform_helper( 2024-12-18T00:10:34.6875893Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6875995Z x.shape, 2024-12-18T00:10:34.6876164Z ^^^^^^^^ 2024-12-18T00:10:34.6876268Z ...<4 lines>... 2024-12-18T00:10:34.6876371Z generator=generator, 2024-12-18T00:10:34.6876461Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6876559Z ) 2024-12-18T00:10:34.6876646Z ^ 2024-12-18T00:10:34.6876985Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6877094Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6877189Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6877621Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6877739Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6878126Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6878237Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6878716Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6878853Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6878957Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6879380Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6879549Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6879671Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6880058Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6880181Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6880588Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6880711Z return prims._uniform_helper( 2024-12-18T00:10:34.6880807Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6880901Z x.shape, 2024-12-18T00:10:34.6881004Z ^^^^^^^^ 2024-12-18T00:10:34.6881098Z ...<4 lines>... 2024-12-18T00:10:34.6881213Z generator=generator, 2024-12-18T00:10:34.6881306Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6881391Z ) 2024-12-18T00:10:34.6881492Z ^ 2024-12-18T00:10:34.6881812Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6881933Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6882027Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6882449Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6882580Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6882955Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6883080Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6883480Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6883626Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6883734Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6884156Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6884328Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6884440Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6884891Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6884997Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6885420Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6885530Z return prims._uniform_helper( 2024-12-18T00:10:34.6885681Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6885771Z x.shape, 2024-12-18T00:10:34.6885858Z ^^^^^^^^ 2024-12-18T00:10:34.6885959Z ...<4 lines>... 2024-12-18T00:10:34.6886059Z generator=generator, 2024-12-18T00:10:34.6886159Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6886243Z ) 2024-12-18T00:10:34.6886327Z ^ 2024-12-18T00:10:34.6886655Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6886762Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6886867Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6887293Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6887416Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6887792Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6887902Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6888306Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6888439Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6888554Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6888964Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6889145Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6889255Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6889640Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6889755Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6890160Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6890285Z return prims._uniform_helper( 2024-12-18T00:10:34.6890380Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6890480Z x.shape, 2024-12-18T00:10:34.6890568Z ^^^^^^^^ 2024-12-18T00:10:34.6890656Z ...<4 lines>... 2024-12-18T00:10:34.6890771Z generator=generator, 2024-12-18T00:10:34.6890863Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6890959Z ) 2024-12-18T00:10:34.6891045Z ^ 2024-12-18T00:10:34.6891363Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6891481Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6891578Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6892014Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6892126Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6892516Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6892627Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6893019Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6893162Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6893267Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6893685Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6893854Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6894028Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6894413Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6894516Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6894980Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6895092Z return prims._uniform_helper( 2024-12-18T00:10:34.6895196Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6895285Z x.shape, 2024-12-18T00:10:34.6895383Z ^^^^^^^^ 2024-12-18T00:10:34.6895475Z ...<4 lines>... 2024-12-18T00:10:34.6895579Z generator=generator, 2024-12-18T00:10:34.6895681Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6895766Z ) 2024-12-18T00:10:34.6895864Z ^ 2024-12-18T00:10:34.6896184Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6896293Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6896395Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6896817Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6896937Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6897316Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6897440Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6897834Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6897966Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6898081Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6898488Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6898673Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6898782Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6899173Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6899278Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6899685Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6899805Z return prims._uniform_helper( 2024-12-18T00:10:34.6899896Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6899997Z x.shape, 2024-12-18T00:10:34.6900085Z ^^^^^^^^ 2024-12-18T00:10:34.6900185Z ...<4 lines>... 2024-12-18T00:10:34.6900288Z generator=generator, 2024-12-18T00:10:34.6900379Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6900476Z ) 2024-12-18T00:10:34.6900562Z ^ 2024-12-18T00:10:34.6900891Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6901000Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6901091Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6901522Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6901634Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6902026Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6902136Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6902536Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6902667Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6902767Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6903185Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6903408Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6903528Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6904007Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6904228Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6904641Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6904749Z return prims._uniform_helper( 2024-12-18T00:10:34.6904854Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6904946Z x.shape, 2024-12-18T00:10:34.6905047Z ^^^^^^^^ 2024-12-18T00:10:34.6905138Z ...<4 lines>... 2024-12-18T00:10:34.6905239Z generator=generator, 2024-12-18T00:10:34.6905344Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6905428Z ) 2024-12-18T00:10:34.6905532Z ^ 2024-12-18T00:10:34.6905856Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6905973Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6906068Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6906485Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6906612Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6906990Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6907116Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6907512Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6907654Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6907759Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6908169Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6908355Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6908466Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6908863Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6908968Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6909385Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6909494Z return prims._uniform_helper( 2024-12-18T00:10:34.6909585Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6909685Z x.shape, 2024-12-18T00:10:34.6909774Z ^^^^^^^^ 2024-12-18T00:10:34.6909876Z ...<4 lines>... 2024-12-18T00:10:34.6909978Z generator=generator, 2024-12-18T00:10:34.6910069Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6910170Z ) 2024-12-18T00:10:34.6936768Z ^ 2024-12-18T00:10:34.6937207Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6937310Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6937393Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6937835Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6937953Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6938350Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6938463Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6938873Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6939008Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6939129Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6939657Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6939830Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6939956Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6940405Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6940522Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6940933Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6941053Z return prims._uniform_helper( 2024-12-18T00:10:34.6941147Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6941238Z x.shape, 2024-12-18T00:10:34.6941338Z ^^^^^^^^ 2024-12-18T00:10:34.6941431Z ...<4 lines>... 2024-12-18T00:10:34.6941546Z generator=generator, 2024-12-18T00:10:34.6941642Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6941726Z ) 2024-12-18T00:10:34.6941822Z ^ 2024-12-18T00:10:34.6942145Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6942265Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6942357Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6942781Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6942908Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6943287Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6943404Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6943798Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6943943Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6944050Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6944474Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6944645Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6944755Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6945158Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6945261Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6945680Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6945789Z return prims._uniform_helper( 2024-12-18T00:10:34.6945893Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6945982Z x.shape, 2024-12-18T00:10:34.6946066Z ^^^^^^^^ 2024-12-18T00:10:34.6946164Z ...<4 lines>... 2024-12-18T00:10:34.6946268Z generator=generator, 2024-12-18T00:10:34.6946370Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6946454Z ) 2024-12-18T00:10:34.6946538Z ^ 2024-12-18T00:10:34.6946869Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6946975Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6947082Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6947505Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6947630Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6948008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6948116Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6948519Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6948717Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6948832Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6949245Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6949426Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6949584Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6949968Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6950084Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6950489Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6950610Z return prims._uniform_helper( 2024-12-18T00:10:34.6950701Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6950804Z x.shape, 2024-12-18T00:10:34.6950890Z ^^^^^^^^ 2024-12-18T00:10:34.6950983Z ...<4 lines>... 2024-12-18T00:10:34.6951096Z generator=generator, 2024-12-18T00:10:34.6951186Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6951281Z ) 2024-12-18T00:10:34.6951365Z ^ 2024-12-18T00:10:34.6951687Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6951810Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6951904Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6952339Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6952450Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6952840Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6952949Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6953342Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6953492Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6953595Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6954015Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6954188Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6954309Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6954697Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6954800Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6955216Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6955326Z return prims._uniform_helper( 2024-12-18T00:10:34.6955428Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6955519Z x.shape, 2024-12-18T00:10:34.6955617Z ^^^^^^^^ 2024-12-18T00:10:34.6955706Z ...<4 lines>... 2024-12-18T00:10:34.6955807Z generator=generator, 2024-12-18T00:10:34.6955911Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6955996Z ) 2024-12-18T00:10:34.6956093Z ^ 2024-12-18T00:10:34.6956415Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6956523Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6956628Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6957048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6957172Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6957550Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6957669Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6958113Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6958246Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6958460Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6958873Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6959112Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6959226Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6959623Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6959726Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6960132Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6960251Z return prims._uniform_helper( 2024-12-18T00:10:34.6960346Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6960445Z x.shape, 2024-12-18T00:10:34.6960532Z ^^^^^^^^ 2024-12-18T00:10:34.6960635Z ...<4 lines>... 2024-12-18T00:10:34.6960735Z generator=generator, 2024-12-18T00:10:34.6960826Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6960921Z ) 2024-12-18T00:10:34.6961005Z ^ 2024-12-18T00:10:34.6961338Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6961446Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6961539Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6961971Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6962083Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6962474Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6962584Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6962994Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6963125Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6963229Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6963652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6963824Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6963946Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6964329Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6964443Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6964847Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6964961Z return prims._uniform_helper( 2024-12-18T00:10:34.6965064Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6965153Z x.shape, 2024-12-18T00:10:34.6965252Z ^^^^^^^^ 2024-12-18T00:10:34.6965342Z ...<4 lines>... 2024-12-18T00:10:34.6965453Z generator=generator, 2024-12-18T00:10:34.6965543Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6965628Z ) 2024-12-18T00:10:34.6965726Z ^ 2024-12-18T00:10:34.6966048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6966165Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6966257Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6966678Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6966800Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6967179Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6967353Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6967745Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6967891Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6967997Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6968456Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6968639Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6968754Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6969152Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6969255Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6969673Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6969786Z return prims._uniform_helper( 2024-12-18T00:10:34.6969880Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6969982Z x.shape, 2024-12-18T00:10:34.6970070Z ^^^^^^^^ 2024-12-18T00:10:34.6970176Z ...<4 lines>... 2024-12-18T00:10:34.6970277Z generator=generator, 2024-12-18T00:10:34.6970367Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6970471Z ) 2024-12-18T00:10:34.6970556Z ^ 2024-12-18T00:10:34.6970886Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6970992Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6971100Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6971522Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6971637Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6972023Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6972140Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6972544Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6972677Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6972797Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6973209Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6973380Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6973503Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6974047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6974171Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6974576Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6974705Z return prims._uniform_helper( 2024-12-18T00:10:34.6974799Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6974888Z x.shape, 2024-12-18T00:10:34.6974989Z ^^^^^^^^ 2024-12-18T00:10:34.6975079Z ...<4 lines>... 2024-12-18T00:10:34.6975192Z generator=generator, 2024-12-18T00:10:34.6975288Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6975382Z ) 2024-12-18T00:10:34.6975482Z ^ 2024-12-18T00:10:34.6975803Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6975921Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6976015Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6976445Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6976555Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6977042Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6977165Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6977559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6977795Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6977901Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6978321Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6978491Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6978602Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6978999Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6979105Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6979526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6979635Z return prims._uniform_helper( 2024-12-18T00:10:34.6979738Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6979827Z x.shape, 2024-12-18T00:10:34.6979914Z ^^^^^^^^ 2024-12-18T00:10:34.6980018Z ...<4 lines>... 2024-12-18T00:10:34.6980121Z generator=generator, 2024-12-18T00:10:34.6980226Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6980309Z ) 2024-12-18T00:10:34.6980392Z ^ 2024-12-18T00:10:34.6980715Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6980840Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6980936Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6981373Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6981487Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6981867Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6981989Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6982380Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6982524Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6982628Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6983048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6983217Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6983326Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6983720Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6983826Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6984241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6984348Z return prims._uniform_helper( 2024-12-18T00:10:34.6984451Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6984540Z x.shape, 2024-12-18T00:10:34.6984631Z ^^^^^^^^ 2024-12-18T00:10:34.6984730Z ...<4 lines>... 2024-12-18T00:10:34.6984831Z generator=generator, 2024-12-18T00:10:34.6984934Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6985019Z ) 2024-12-18T00:10:34.6985103Z ^ 2024-12-18T00:10:34.6985434Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6985541Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6985645Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6986065Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6986272Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6986647Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6986757Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6987205Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6987338Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6987453Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6987862Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6988040Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6988148Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6988528Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6988643Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6989045Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6989162Z return prims._uniform_helper( 2024-12-18T00:10:34.6989252Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6989358Z x.shape, 2024-12-18T00:10:34.6989444Z ^^^^^^^^ 2024-12-18T00:10:34.6989533Z ...<4 lines>... 2024-12-18T00:10:34.6989642Z generator=generator, 2024-12-18T00:10:34.6989731Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6989826Z ) 2024-12-18T00:10:34.6989909Z ^ 2024-12-18T00:10:34.6990226Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6990341Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6990431Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6990861Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6990975Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6991364Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6991472Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6991867Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6992006Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6992108Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6992523Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6992690Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6992811Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6993196Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6993296Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6993709Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6993816Z return prims._uniform_helper( 2024-12-18T00:10:34.6993921Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6994009Z x.shape, 2024-12-18T00:10:34.6994105Z ^^^^^^^^ 2024-12-18T00:10:34.6994194Z ...<4 lines>... 2024-12-18T00:10:34.6994294Z generator=generator, 2024-12-18T00:10:34.6994392Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6994476Z ) 2024-12-18T00:10:34.6994570Z ^ 2024-12-18T00:10:34.6994888Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6994994Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6995094Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6995567Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.6995688Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.6996064Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.6996232Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.6996628Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.6996759Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.6996873Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6997282Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.6997461Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.6997570Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6997969Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.6998072Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.6998578Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.6998693Z return prims._uniform_helper( 2024-12-18T00:10:34.6998783Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.6998884Z x.shape, 2024-12-18T00:10:34.6998971Z ^^^^^^^^ 2024-12-18T00:10:34.6999072Z ...<4 lines>... 2024-12-18T00:10:34.6999172Z generator=generator, 2024-12-18T00:10:34.6999262Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.6999358Z ) 2024-12-18T00:10:34.6999440Z ^ 2024-12-18T00:10:34.6999773Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.6999879Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.6999976Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7000409Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7000519Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7000910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7001020Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7001422Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7001553Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7001660Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7002078Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7002248Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7002372Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7002755Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7002869Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7003274Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7003394Z return prims._uniform_helper( 2024-12-18T00:10:34.7003487Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7003576Z x.shape, 2024-12-18T00:10:34.7003673Z ^^^^^^^^ 2024-12-18T00:10:34.7003763Z ...<4 lines>... 2024-12-18T00:10:34.7003876Z generator=generator, 2024-12-18T00:10:34.7003966Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7004051Z ) 2024-12-18T00:10:34.7004146Z ^ 2024-12-18T00:10:34.7004465Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7004642Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7004734Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7005155Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7005277Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7005704Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7005820Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7006229Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7006356Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7006471Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7006880Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7007065Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7007174Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7007554Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7007667Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7008072Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7008190Z return prims._uniform_helper( 2024-12-18T00:10:34.7008282Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7008381Z x.shape, 2024-12-18T00:10:34.7008467Z ^^^^^^^^ 2024-12-18T00:10:34.7008551Z ...<4 lines>... 2024-12-18T00:10:34.7008663Z generator=generator, 2024-12-18T00:10:34.7008751Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7008846Z ) 2024-12-18T00:10:34.7008929Z ^ 2024-12-18T00:10:34.7009243Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7009366Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7009453Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7009889Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7009998Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7010392Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7010499Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7010891Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7011032Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7011133Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7011552Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7011721Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7011838Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7012222Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7012325Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7012740Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7012845Z return prims._uniform_helper( 2024-12-18T00:10:34.7012946Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7013033Z x.shape, 2024-12-18T00:10:34.7013126Z ^^^^^^^^ 2024-12-18T00:10:34.7013214Z ...<4 lines>... 2024-12-18T00:10:34.7013308Z generator=generator, 2024-12-18T00:10:34.7013409Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7013490Z ) 2024-12-18T00:10:34.7013590Z ^ 2024-12-18T00:10:34.7014021Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7014127Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7014230Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7014644Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7014829Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7015201Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7015320Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7015708Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7015835Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7015951Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7016357Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7016545Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7016651Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7017041Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7017144Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7017546Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7017666Z return prims._uniform_helper( 2024-12-18T00:10:34.7017753Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7017853Z x.shape, 2024-12-18T00:10:34.7017940Z ^^^^^^^^ 2024-12-18T00:10:34.7018037Z ...<4 lines>... 2024-12-18T00:10:34.7018137Z generator=generator, 2024-12-18T00:10:34.7018223Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7018323Z ) 2024-12-18T00:10:34.7018402Z ^ 2024-12-18T00:10:34.7018733Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7018838Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7018926Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7019359Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7019464Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7019849Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7019958Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7020358Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7020488Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7020589Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7021012Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7021178Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7021295Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7021680Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7021789Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7022193Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7022296Z return prims._uniform_helper( 2024-12-18T00:10:34.7022397Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7022483Z x.shape, 2024-12-18T00:10:34.7022574Z ^^^^^^^^ 2024-12-18T00:10:34.7022663Z ...<4 lines>... 2024-12-18T00:10:34.7022772Z generator=generator, 2024-12-18T00:10:34.7022909Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7022990Z ) 2024-12-18T00:10:34.7023084Z ^ 2024-12-18T00:10:34.7023399Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7023516Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7023606Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7024071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7024191Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7024566Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7024685Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7025075Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7025214Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7025322Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7025725Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7025903Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7026006Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7026401Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7026501Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7026910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7027015Z return prims._uniform_helper( 2024-12-18T00:10:34.7027102Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7027199Z x.shape, 2024-12-18T00:10:34.7027281Z ^^^^^^^^ 2024-12-18T00:10:34.7027379Z ...<4 lines>... 2024-12-18T00:10:34.7027482Z generator=generator, 2024-12-18T00:10:34.7027583Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7027664Z ) 2024-12-18T00:10:34.7027745Z ^ 2024-12-18T00:10:34.7028076Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7028177Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7028284Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7028701Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7028807Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7029196Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7029300Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7029702Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7029834Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7029946Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7030353Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7030515Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7030638Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7031020Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7031130Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7031532Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7031649Z return prims._uniform_helper( 2024-12-18T00:10:34.7031739Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7031821Z x.shape, 2024-12-18T00:10:34.7031918Z ^^^^^^^^ 2024-12-18T00:10:34.7032049Z ...<4 lines>... 2024-12-18T00:10:34.7032160Z generator=generator, 2024-12-18T00:10:34.7032246Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7032340Z ) 2024-12-18T00:10:34.7032422Z ^ 2024-12-18T00:10:34.7032738Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7032901Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7032991Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7033417Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7033526Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7033902Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7034027Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7034417Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7034563Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7034664Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7035081Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7035253Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7035372Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7035754Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7035851Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7036265Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7036374Z return prims._uniform_helper( 2024-12-18T00:10:34.7036471Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7036564Z x.shape, 2024-12-18T00:10:34.7036644Z ^^^^^^^^ 2024-12-18T00:10:34.7036741Z ...<4 lines>... 2024-12-18T00:10:34.7036838Z generator=generator, 2024-12-18T00:10:34.7036939Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7037018Z ) 2024-12-18T00:10:34.7037098Z ^ 2024-12-18T00:10:34.7037429Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7037531Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7037631Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7038044Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7038160Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7038623Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7038734Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7039138Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7039271Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7039389Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7039798Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7039981Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7040089Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7040485Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7040588Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7040990Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7041107Z return prims._uniform_helper( 2024-12-18T00:10:34.7041255Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7041353Z x.shape, 2024-12-18T00:10:34.7041441Z ^^^^^^^^ 2024-12-18T00:10:34.7041526Z ...<4 lines>... 2024-12-18T00:10:34.7041639Z generator=generator, 2024-12-18T00:10:34.7041725Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7041821Z ) 2024-12-18T00:10:34.7041904Z ^ 2024-12-18T00:10:34.7042272Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7042393Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7042482Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7042917Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7043024Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7043412Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7043522Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7043928Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7044059Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7044165Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7044592Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7044758Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7044878Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7045265Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7045376Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7045780Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7045892Z return prims._uniform_helper( 2024-12-18T00:10:34.7045989Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7046075Z x.shape, 2024-12-18T00:10:34.7046174Z ^^^^^^^^ 2024-12-18T00:10:34.7046263Z ...<4 lines>... 2024-12-18T00:10:34.7046361Z generator=generator, 2024-12-18T00:10:34.7046463Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7046544Z ) 2024-12-18T00:10:34.7046637Z ^ 2024-12-18T00:10:34.7046956Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7047066Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7047159Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7047573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7047695Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7048067Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7048191Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7048581Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7048718Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7048822Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7049230Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7049409Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7049516Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7049911Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7050012Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7050426Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7050605Z return prims._uniform_helper( 2024-12-18T00:10:34.7050696Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7050797Z x.shape, 2024-12-18T00:10:34.7050879Z ^^^^^^^^ 2024-12-18T00:10:34.7050981Z ...<4 lines>... 2024-12-18T00:10:34.7051081Z generator=generator, 2024-12-18T00:10:34.7051165Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7051312Z ) 2024-12-18T00:10:34.7051396Z ^ 2024-12-18T00:10:34.7051726Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7051829Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7051936Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7052355Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7052462Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7052848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7052962Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7053365Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7053495Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7053611Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7054018Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7054185Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7054305Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7054684Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7054794Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7055199Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7055317Z return prims._uniform_helper( 2024-12-18T00:10:34.7055405Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7055489Z x.shape, 2024-12-18T00:10:34.7055581Z ^^^^^^^^ 2024-12-18T00:10:34.7055667Z ...<4 lines>... 2024-12-18T00:10:34.7055776Z generator=generator, 2024-12-18T00:10:34.7055868Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7055948Z ) 2024-12-18T00:10:34.7056043Z ^ 2024-12-18T00:10:34.7056355Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7056473Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7056561Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7056992Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7057103Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7057481Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7057601Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7057991Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7058138Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7058241Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7058659Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7058824Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7058930Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7059323Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7059421Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7059887Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7059994Z return prims._uniform_helper( 2024-12-18T00:10:34.7060096Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7060181Z x.shape, 2024-12-18T00:10:34.7060262Z ^^^^^^^^ 2024-12-18T00:10:34.7060360Z ...<4 lines>... 2024-12-18T00:10:34.7060503Z generator=generator, 2024-12-18T00:10:34.7060603Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7060686Z ) 2024-12-18T00:10:34.7060767Z ^ 2024-12-18T00:10:34.7061099Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7061199Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7061301Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7061716Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7061839Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7062213Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 286, in _prim_impl 2024-12-18T00:10:34.7062306Z meta(*args, **kwargs) 2024-12-18T00:10:34.7062408Z ~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7062801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2741, in _uniform_meta 2024-12-18T00:10:34.7062964Z strides = utils.make_contiguous_strides_for(shape) 2024-12-18T00:10:34.7063441Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py", line 1622, in make_contiguous_strides_for 2024-12-18T00:10:34.7063551Z validate_shape(shape) 2024-12-18T00:10:34.7063645Z ~~~~~~~~~~~~~~^^^^^^^ 2024-12-18T00:10:34.7063793Z RecursionError: maximum recursion depth exceeded 2024-12-18T00:10:34.7063802Z 2024-12-18T00:10:34.7064007Z To execute this test, run the following from the base repo dir: 2024-12-18T00:10:34.7064287Z python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:10:34.7064293Z 2024-12-18T00:10:34.7064540Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:10:34.7064782Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:10:34.7064902Z Traceback (most recent call last): 2024-12-18T00:10:34.7065270Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in test_returning_symint 2024-12-18T00:10:34.7065411Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:10:34.7065532Z ~~~~~~~~~~^^^^^^ 2024-12-18T00:10:34.7065940Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7066121Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7066228Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7066625Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7066725Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7067138Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7067246Z return prims._uniform_helper( 2024-12-18T00:10:34.7067337Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7067437Z x.shape, 2024-12-18T00:10:34.7067521Z ^^^^^^^^ 2024-12-18T00:10:34.7067624Z ...<4 lines>... 2024-12-18T00:10:34.7067725Z generator=generator, 2024-12-18T00:10:34.7067812Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7067908Z ) 2024-12-18T00:10:34.7067991Z ^ 2024-12-18T00:10:34.7068321Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7068426Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7068567Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7069008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7069113Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7069500Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7069662Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7070063Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7070192Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7070295Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7070717Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7070882Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7071004Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7071384Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7071493Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7071893Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7072008Z return prims._uniform_helper( 2024-12-18T00:10:34.7072098Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7072184Z x.shape, 2024-12-18T00:10:34.7072278Z ^^^^^^^^ 2024-12-18T00:10:34.7072365Z ...<4 lines>... 2024-12-18T00:10:34.7072472Z generator=generator, 2024-12-18T00:10:34.7072562Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7072642Z ) 2024-12-18T00:10:34.7072733Z ^ 2024-12-18T00:10:34.7073048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7073166Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7073256Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7073893Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7074020Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7074402Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7074527Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7074921Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7075069Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7075177Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7075599Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7075768Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7075884Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7076281Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7076384Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7076809Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7076918Z return prims._uniform_helper( 2024-12-18T00:10:34.7077022Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7077111Z x.shape, 2024-12-18T00:10:34.7077198Z ^^^^^^^^ 2024-12-18T00:10:34.7077301Z ...<4 lines>... 2024-12-18T00:10:34.7077403Z generator=generator, 2024-12-18T00:10:34.7077502Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7077584Z ) 2024-12-18T00:10:34.7077666Z ^ 2024-12-18T00:10:34.7077998Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7078202Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7078373Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7078803Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7078924Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7079374Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7079484Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7079892Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7080021Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7080139Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7080548Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7080736Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7080845Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7081229Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7081342Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7081750Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7081867Z return prims._uniform_helper( 2024-12-18T00:10:34.7081958Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7082057Z x.shape, 2024-12-18T00:10:34.7082141Z ^^^^^^^^ 2024-12-18T00:10:34.7082234Z ...<4 lines>... 2024-12-18T00:10:34.7082349Z generator=generator, 2024-12-18T00:10:34.7082437Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7082533Z ) 2024-12-18T00:10:34.7082617Z ^ 2024-12-18T00:10:34.7082934Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7083055Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7083144Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7083569Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7083686Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7084070Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7084176Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7084570Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7084710Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7084814Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7085230Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7085407Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7085530Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7085914Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7086019Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7086433Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7086541Z return prims._uniform_helper( 2024-12-18T00:10:34.7086645Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7086736Z x.shape, 2024-12-18T00:10:34.7086835Z ^^^^^^^^ 2024-12-18T00:10:34.7086923Z ...<4 lines>... 2024-12-18T00:10:34.7087027Z generator=generator, 2024-12-18T00:10:34.7087123Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7087208Z ) 2024-12-18T00:10:34.7087373Z ^ 2024-12-18T00:10:34.7087691Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7087798Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7087900Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7088369Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7088492Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7088870Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7088991Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7089383Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7089513Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7089629Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7090042Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7090223Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7090330Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7090729Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7090828Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7091232Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7091351Z return prims._uniform_helper( 2024-12-18T00:10:34.7091442Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7091542Z x.shape, 2024-12-18T00:10:34.7091628Z ^^^^^^^^ 2024-12-18T00:10:34.7091718Z ...<4 lines>... 2024-12-18T00:10:34.7091829Z generator=generator, 2024-12-18T00:10:34.7091920Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7092020Z ) 2024-12-18T00:10:34.7092106Z ^ 2024-12-18T00:10:34.7092436Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7092543Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7092634Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7093066Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7093175Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7093559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7093666Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7094067Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7094198Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7094306Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7094722Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7094889Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7095006Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7095392Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7095505Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7095907Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7096013Z return prims._uniform_helper( 2024-12-18T00:10:34.7096115Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7096202Z x.shape, 2024-12-18T00:10:34.7096300Z ^^^^^^^^ 2024-12-18T00:10:34.7096387Z ...<4 lines>... 2024-12-18T00:10:34.7096488Z generator=generator, 2024-12-18T00:10:34.7096637Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7096720Z ) 2024-12-18T00:10:34.7096813Z ^ 2024-12-18T00:10:34.7097129Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7097241Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7097331Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7097798Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7097918Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7098296Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7098413Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7098805Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7098947Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7099054Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7099461Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7099640Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7099752Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7100144Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7100245Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7100658Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7100764Z return prims._uniform_helper( 2024-12-18T00:10:34.7100853Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7100950Z x.shape, 2024-12-18T00:10:34.7101034Z ^^^^^^^^ 2024-12-18T00:10:34.7101134Z ...<4 lines>... 2024-12-18T00:10:34.7101233Z generator=generator, 2024-12-18T00:10:34.7101323Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7101413Z ) 2024-12-18T00:10:34.7101496Z ^ 2024-12-18T00:10:34.7101823Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7101930Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7102035Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7102454Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7102565Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7102953Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7103059Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7103457Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7103594Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7103709Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7104117Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7104286Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7104410Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7104789Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7104900Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7105303Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7105422Z return prims._uniform_helper( 2024-12-18T00:10:34.7105510Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7105599Z x.shape, 2024-12-18T00:10:34.7105744Z ^^^^^^^^ 2024-12-18T00:10:34.7105833Z ...<4 lines>... 2024-12-18T00:10:34.7105945Z generator=generator, 2024-12-18T00:10:34.7106032Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7106116Z ) 2024-12-18T00:10:34.7106209Z ^ 2024-12-18T00:10:34.7106526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7106692Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7106787Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7107219Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7107328Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7107705Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7107822Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7108214Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7108361Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7108463Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7108883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7109055Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7109166Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7109564Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7109667Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7110084Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7110192Z return prims._uniform_helper( 2024-12-18T00:10:34.7110297Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7110389Z x.shape, 2024-12-18T00:10:34.7110476Z ^^^^^^^^ 2024-12-18T00:10:34.7110583Z ...<4 lines>... 2024-12-18T00:10:34.7110686Z generator=generator, 2024-12-18T00:10:34.7110789Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7110875Z ) 2024-12-18T00:10:34.7110961Z ^ 2024-12-18T00:10:34.7111295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7111403Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7111511Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7111930Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7112056Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7112433Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7112544Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7112954Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7113083Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7113199Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7113611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7113794Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7113902Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7114287Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7114405Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7114811Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7114936Z return prims._uniform_helper( 2024-12-18T00:10:34.7115079Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7115175Z x.shape, 2024-12-18T00:10:34.7115260Z ^^^^^^^^ 2024-12-18T00:10:34.7115347Z ...<4 lines>... 2024-12-18T00:10:34.7115453Z generator=generator, 2024-12-18T00:10:34.7115540Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7115634Z ) 2024-12-18T00:10:34.7115716Z ^ 2024-12-18T00:10:34.7116097Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7116214Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7116307Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7116737Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7116846Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7117229Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7117344Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7117737Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7117874Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7117974Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7118475Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7118646Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7118767Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7119151Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7119266Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7119668Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7119779Z return prims._uniform_helper( 2024-12-18T00:10:34.7119880Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7119966Z x.shape, 2024-12-18T00:10:34.7120063Z ^^^^^^^^ 2024-12-18T00:10:34.7120152Z ...<4 lines>... 2024-12-18T00:10:34.7120253Z generator=generator, 2024-12-18T00:10:34.7120352Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7120437Z ) 2024-12-18T00:10:34.7120533Z ^ 2024-12-18T00:10:34.7120848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7120954Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7121058Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7121479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7121598Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7121975Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7122095Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7122485Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7122617Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7122731Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7123143Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7123322Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7123432Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7123822Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7123922Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7124334Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7124495Z return prims._uniform_helper( 2024-12-18T00:10:34.7124587Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7124683Z x.shape, 2024-12-18T00:10:34.7124769Z ^^^^^^^^ 2024-12-18T00:10:34.7124868Z ...<4 lines>... 2024-12-18T00:10:34.7124968Z generator=generator, 2024-12-18T00:10:34.7125103Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7125198Z ) 2024-12-18T00:10:34.7125281Z ^ 2024-12-18T00:10:34.7125617Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7125723Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7125817Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7126247Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7126357Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7126743Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7126856Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7127259Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7127388Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7127495Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7127913Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7128083Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7128202Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7128583Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7128696Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7129097Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7129218Z return prims._uniform_helper( 2024-12-18T00:10:34.7129310Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7129399Z x.shape, 2024-12-18T00:10:34.7129494Z ^^^^^^^^ 2024-12-18T00:10:34.7129583Z ...<4 lines>... 2024-12-18T00:10:34.7129695Z generator=generator, 2024-12-18T00:10:34.7129782Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7129866Z ) 2024-12-18T00:10:34.7129960Z ^ 2024-12-18T00:10:34.7130276Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7130388Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7130482Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7130900Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7131018Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7131397Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7131517Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7131906Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7132048Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7132154Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7132569Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7132736Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7132845Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7133237Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7133338Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7133800Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7133907Z return prims._uniform_helper( 2024-12-18T00:10:34.7134011Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7134098Z x.shape, 2024-12-18T00:10:34.7134184Z ^^^^^^^^ 2024-12-18T00:10:34.7134349Z ...<4 lines>... 2024-12-18T00:10:34.7134452Z generator=generator, 2024-12-18T00:10:34.7134552Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7134634Z ) 2024-12-18T00:10:34.7134716Z ^ 2024-12-18T00:10:34.7135047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7135151Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7135251Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7135668Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7135795Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7136170Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7136282Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7136689Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7136820Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7136936Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7137340Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7137519Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7137626Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7138009Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7138125Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7138527Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7138649Z return prims._uniform_helper( 2024-12-18T00:10:34.7138740Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7138838Z x.shape, 2024-12-18T00:10:34.7138926Z ^^^^^^^^ 2024-12-18T00:10:34.7139017Z ...<4 lines>... 2024-12-18T00:10:34.7139129Z generator=generator, 2024-12-18T00:10:34.7139219Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7139312Z ) 2024-12-18T00:10:34.7139393Z ^ 2024-12-18T00:10:34.7139713Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7139825Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7139917Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7140346Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7140459Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7140843Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7140949Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7141344Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7141485Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7141589Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7142006Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7142182Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7142304Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7142686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7142847Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7143266Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7143377Z return prims._uniform_helper( 2024-12-18T00:10:34.7143482Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7143621Z x.shape, 2024-12-18T00:10:34.7143722Z ^^^^^^^^ 2024-12-18T00:10:34.7143813Z ...<4 lines>... 2024-12-18T00:10:34.7143915Z generator=generator, 2024-12-18T00:10:34.7144019Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7144104Z ) 2024-12-18T00:10:34.7144202Z ^ 2024-12-18T00:10:34.7144525Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7144633Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7144742Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7145168Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7145294Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7145669Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7145794Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7146195Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7146329Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7146451Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7146862Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7147045Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7147155Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7147557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7147661Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7148067Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7148188Z return prims._uniform_helper( 2024-12-18T00:10:34.7148282Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7148387Z x.shape, 2024-12-18T00:10:34.7148475Z ^^^^^^^^ 2024-12-18T00:10:34.7148577Z ...<4 lines>... 2024-12-18T00:10:34.7148677Z generator=generator, 2024-12-18T00:10:34.7148766Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7148862Z ) 2024-12-18T00:10:34.7148944Z ^ 2024-12-18T00:10:34.7149272Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7149376Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7149470Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7149900Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7150006Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7150396Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7150508Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7150910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7151041Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7151144Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7151561Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7151729Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7151896Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7152277Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7152390Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7152794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7153020Z return prims._uniform_helper( 2024-12-18T00:10:34.7153124Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7153212Z x.shape, 2024-12-18T00:10:34.7153312Z ^^^^^^^^ 2024-12-18T00:10:34.7153399Z ...<4 lines>... 2024-12-18T00:10:34.7153498Z generator=generator, 2024-12-18T00:10:34.7153602Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7153685Z ) 2024-12-18T00:10:34.7153787Z ^ 2024-12-18T00:10:34.7154106Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7154230Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7154322Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7154742Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7154867Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7155246Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7155373Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7155769Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7155911Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7156014Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7156421Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7156603Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7156719Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7157110Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7157211Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7157628Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7157734Z return prims._uniform_helper( 2024-12-18T00:10:34.7157826Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7157927Z x.shape, 2024-12-18T00:10:34.7158013Z ^^^^^^^^ 2024-12-18T00:10:34.7158114Z ...<4 lines>... 2024-12-18T00:10:34.7158215Z generator=generator, 2024-12-18T00:10:34.7158393Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7158503Z ) 2024-12-18T00:10:34.7158588Z ^ 2024-12-18T00:10:34.7158923Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7159035Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7159142Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7159569Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7159681Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7160075Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7160187Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7160593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7160725Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7160844Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7161252Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7161483Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7161607Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7161990Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7162104Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7162562Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7162684Z return prims._uniform_helper( 2024-12-18T00:10:34.7162776Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7162866Z x.shape, 2024-12-18T00:10:34.7162962Z ^^^^^^^^ 2024-12-18T00:10:34.7163050Z ...<4 lines>... 2024-12-18T00:10:34.7163163Z generator=generator, 2024-12-18T00:10:34.7163252Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7163336Z ) 2024-12-18T00:10:34.7163430Z ^ 2024-12-18T00:10:34.7163748Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7163869Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7163960Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7164390Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7164500Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7164875Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7164993Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7165384Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7165527Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7165629Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7166047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7166222Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7166331Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7166724Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7166829Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7167244Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7167349Z return prims._uniform_helper( 2024-12-18T00:10:34.7167449Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7167537Z x.shape, 2024-12-18T00:10:34.7167622Z ^^^^^^^^ 2024-12-18T00:10:34.7167721Z ...<4 lines>... 2024-12-18T00:10:34.7167820Z generator=generator, 2024-12-18T00:10:34.7167921Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7168005Z ) 2024-12-18T00:10:34.7168092Z ^ 2024-12-18T00:10:34.7168421Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7168526Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7168627Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7169049Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7169172Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7169546Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7169655Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7170060Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7170188Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7170305Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7170769Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7170951Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7171061Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7171490Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7171605Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7172010Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7172132Z return prims._uniform_helper( 2024-12-18T00:10:34.7172221Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7172325Z x.shape, 2024-12-18T00:10:34.7172412Z ^^^^^^^^ 2024-12-18T00:10:34.7172500Z ...<4 lines>... 2024-12-18T00:10:34.7172613Z generator=generator, 2024-12-18T00:10:34.7172703Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7172804Z ) 2024-12-18T00:10:34.7172888Z ^ 2024-12-18T00:10:34.7173207Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7173325Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7173416Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7174009Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7174120Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7174509Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7174618Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7175012Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7175156Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7175261Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7175687Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7175855Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7175979Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7176366Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7176482Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7176885Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7176993Z return prims._uniform_helper( 2024-12-18T00:10:34.7177100Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7177188Z x.shape, 2024-12-18T00:10:34.7177290Z ^^^^^^^^ 2024-12-18T00:10:34.7177381Z ...<4 lines>... 2024-12-18T00:10:34.7177482Z generator=generator, 2024-12-18T00:10:34.7177590Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7177676Z ) 2024-12-18T00:10:34.7177776Z ^ 2024-12-18T00:10:34.7178094Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7178201Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7178308Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7178731Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7178859Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7179236Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7179360Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7179756Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7179889Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7180103Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7180516Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7180701Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7180812Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7181277Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7181380Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7181797Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7181908Z return prims._uniform_helper( 2024-12-18T00:10:34.7182004Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7182109Z x.shape, 2024-12-18T00:10:34.7182209Z ^^^^^^^^ 2024-12-18T00:10:34.7182312Z ...<4 lines>... 2024-12-18T00:10:34.7182417Z generator=generator, 2024-12-18T00:10:34.7182507Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7182603Z ) 2024-12-18T00:10:34.7182685Z ^ 2024-12-18T00:10:34.7183019Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7183124Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7183221Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7183652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7183760Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7184152Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7184261Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7184667Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7184800Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7184914Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7185321Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7185488Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7185610Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7185992Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7186104Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7186505Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7186624Z return prims._uniform_helper( 2024-12-18T00:10:34.7186714Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7186802Z x.shape, 2024-12-18T00:10:34.7186905Z ^^^^^^^^ 2024-12-18T00:10:34.7187041Z ...<4 lines>... 2024-12-18T00:10:34.7187153Z generator=generator, 2024-12-18T00:10:34.7187242Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7187328Z ) 2024-12-18T00:10:34.7187423Z ^ 2024-12-18T00:10:34.7187739Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7187866Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7187960Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7188382Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7188504Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7188883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7189004Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7189397Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7189606Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7189709Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7190135Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7190355Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7190466Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7190862Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7190964Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7191380Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7191487Z return prims._uniform_helper( 2024-12-18T00:10:34.7191589Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7191680Z x.shape, 2024-12-18T00:10:34.7191765Z ^^^^^^^^ 2024-12-18T00:10:34.7191866Z ...<4 lines>... 2024-12-18T00:10:34.7191964Z generator=generator, 2024-12-18T00:10:34.7192067Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7192150Z ) 2024-12-18T00:10:34.7192233Z ^ 2024-12-18T00:10:34.7192569Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7192675Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7192778Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7193195Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7193317Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7193696Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7193807Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7194214Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7194344Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7194460Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7194874Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7195054Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7195161Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7195545Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7195659Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7196061Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7196181Z return prims._uniform_helper( 2024-12-18T00:10:34.7196276Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7196376Z x.shape, 2024-12-18T00:10:34.7196462Z ^^^^^^^^ 2024-12-18T00:10:34.7196550Z ...<4 lines>... 2024-12-18T00:10:34.7196662Z generator=generator, 2024-12-18T00:10:34.7196751Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7196847Z ) 2024-12-18T00:10:34.7196930Z ^ 2024-12-18T00:10:34.7197252Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7197368Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7197458Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7197887Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7197996Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7198463Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7198637Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7199032Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7199176Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7199279Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7199750Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7199921Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7200043Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7200426Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7200528Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7200942Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7201061Z return prims._uniform_helper( 2024-12-18T00:10:34.7201165Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7201253Z x.shape, 2024-12-18T00:10:34.7201350Z ^^^^^^^^ 2024-12-18T00:10:34.7201438Z ...<4 lines>... 2024-12-18T00:10:34.7201537Z generator=generator, 2024-12-18T00:10:34.7201642Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7201726Z ) 2024-12-18T00:10:34.7201822Z ^ 2024-12-18T00:10:34.7202141Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7202247Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7202349Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7202768Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7202893Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7203266Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7203390Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7203781Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7203913Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7204029Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7204439Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7204621Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7204730Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7205124Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7205225Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7205632Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7205755Z return prims._uniform_helper( 2024-12-18T00:10:34.7205845Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7205944Z x.shape, 2024-12-18T00:10:34.7206030Z ^^^^^^^^ 2024-12-18T00:10:34.7206134Z ...<4 lines>... 2024-12-18T00:10:34.7206236Z generator=generator, 2024-12-18T00:10:34.7206329Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7206428Z ) 2024-12-18T00:10:34.7206513Z ^ 2024-12-18T00:10:34.7206846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7206951Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7207044Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7207479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7207589Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7207977Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7208154Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7208559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7208689Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7208845Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7209266Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7209436Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7209560Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7209942Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7210057Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7210461Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7210574Z return prims._uniform_helper( 2024-12-18T00:10:34.7210681Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7210771Z x.shape, 2024-12-18T00:10:34.7210872Z ^^^^^^^^ 2024-12-18T00:10:34.7210962Z ...<4 lines>... 2024-12-18T00:10:34.7211069Z generator=generator, 2024-12-18T00:10:34.7211168Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7211257Z ) 2024-12-18T00:10:34.7211350Z ^ 2024-12-18T00:10:34.7211670Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7211787Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7211880Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7212300Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7212422Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7212807Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7212927Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7213320Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7213466Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7213572Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7213983Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7214162Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7214273Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7214671Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7214773Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7215198Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7215306Z return prims._uniform_helper( 2024-12-18T00:10:34.7215399Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7215500Z x.shape, 2024-12-18T00:10:34.7215589Z ^^^^^^^^ 2024-12-18T00:10:34.7215695Z ...<4 lines>... 2024-12-18T00:10:34.7215795Z generator=generator, 2024-12-18T00:10:34.7215885Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7215983Z ) 2024-12-18T00:10:34.7216068Z ^ 2024-12-18T00:10:34.7216396Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7216502Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7216606Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7217029Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7221310Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7221724Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7221834Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7222361Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7222491Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7222594Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7223021Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7223187Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7223306Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7223690Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7223835Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7224251Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7224360Z return prims._uniform_helper( 2024-12-18T00:10:34.7224461Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7224551Z x.shape, 2024-12-18T00:10:34.7224644Z ^^^^^^^^ 2024-12-18T00:10:34.7224746Z ...<4 lines>... 2024-12-18T00:10:34.7224848Z generator=generator, 2024-12-18T00:10:34.7224949Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7225034Z ) 2024-12-18T00:10:34.7225119Z ^ 2024-12-18T00:10:34.7225452Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7225560Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7225665Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7226086Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7226212Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7226590Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7226700Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7227109Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7227238Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7227352Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7227761Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7227942Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7228050Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7228431Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7228546Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7228948Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7229065Z return prims._uniform_helper( 2024-12-18T00:10:34.7229155Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7229260Z x.shape, 2024-12-18T00:10:34.7229347Z ^^^^^^^^ 2024-12-18T00:10:34.7229434Z ...<4 lines>... 2024-12-18T00:10:34.7229551Z generator=generator, 2024-12-18T00:10:34.7229640Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7229736Z ) 2024-12-18T00:10:34.7229819Z ^ 2024-12-18T00:10:34.7230139Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7230256Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7230348Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7230811Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7230981Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7231370Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7231478Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7231922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7232068Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7232171Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7232591Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7232758Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7232875Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7233259Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7233366Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7233779Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7233886Z return prims._uniform_helper( 2024-12-18T00:10:34.7233989Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7234079Z x.shape, 2024-12-18T00:10:34.7234172Z ^^^^^^^^ 2024-12-18T00:10:34.7234261Z ...<4 lines>... 2024-12-18T00:10:34.7234361Z generator=generator, 2024-12-18T00:10:34.7234464Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7234549Z ) 2024-12-18T00:10:34.7234642Z ^ 2024-12-18T00:10:34.7234961Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7235066Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7235173Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7235594Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7235716Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7236091Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7236211Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7236602Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7236735Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7236851Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7237258Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7237435Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7237548Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7237938Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7238040Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7238572Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7238698Z return prims._uniform_helper( 2024-12-18T00:10:34.7238789Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7238887Z x.shape, 2024-12-18T00:10:34.7238972Z ^^^^^^^^ 2024-12-18T00:10:34.7239074Z ...<4 lines>... 2024-12-18T00:10:34.7239175Z generator=generator, 2024-12-18T00:10:34.7239264Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7239361Z ) 2024-12-18T00:10:34.7239445Z ^ 2024-12-18T00:10:34.7239773Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7239878Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7240050Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7240481Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7240593Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7241034Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7241145Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7241550Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7241683Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7241787Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7242206Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7242376Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7242499Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7242883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7242996Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7243401Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7243509Z return prims._uniform_helper( 2024-12-18T00:10:34.7243608Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7243696Z x.shape, 2024-12-18T00:10:34.7243794Z ^^^^^^^^ 2024-12-18T00:10:34.7243885Z ...<4 lines>... 2024-12-18T00:10:34.7243993Z generator=generator, 2024-12-18T00:10:34.7244081Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7244165Z ) 2024-12-18T00:10:34.7244259Z ^ 2024-12-18T00:10:34.7244577Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7244695Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7244786Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7245202Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7245322Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7245702Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7245822Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7246215Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7246358Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7246460Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7246868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7252521Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7252692Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7253117Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7253222Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7253652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7253765Z return prims._uniform_helper( 2024-12-18T00:10:34.7253866Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7253955Z x.shape, 2024-12-18T00:10:34.7254042Z ^^^^^^^^ 2024-12-18T00:10:34.7254140Z ...<4 lines>... 2024-12-18T00:10:34.7254243Z generator=generator, 2024-12-18T00:10:34.7254339Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7254425Z ) 2024-12-18T00:10:34.7254510Z ^ 2024-12-18T00:10:34.7254848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7255085Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7255187Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7255611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7255794Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7256178Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7256287Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7256689Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7256824Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7256939Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7257348Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7257533Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7257645Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7258029Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7258145Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7258549Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7258669Z return prims._uniform_helper( 2024-12-18T00:10:34.7258760Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7258859Z x.shape, 2024-12-18T00:10:34.7258947Z ^^^^^^^^ 2024-12-18T00:10:34.7259038Z ...<4 lines>... 2024-12-18T00:10:34.7259149Z generator=generator, 2024-12-18T00:10:34.7259240Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7259333Z ) 2024-12-18T00:10:34.7259418Z ^ 2024-12-18T00:10:34.7259741Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7259860Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7259950Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7260389Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7260500Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7260884Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7260994Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7261386Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7261524Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7261631Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7262050Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7262222Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7262339Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7262725Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7262829Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7263239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7263351Z return prims._uniform_helper( 2024-12-18T00:10:34.7263453Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7263542Z x.shape, 2024-12-18T00:10:34.7263636Z ^^^^^^^^ 2024-12-18T00:10:34.7263726Z ...<4 lines>... 2024-12-18T00:10:34.7263827Z generator=generator, 2024-12-18T00:10:34.7263928Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7264041Z ) 2024-12-18T00:10:34.7264166Z ^ 2024-12-18T00:10:34.7264488Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7264598Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7264705Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7265189Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7265315Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7265695Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7265815Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7266207Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7266339Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7266454Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7266868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7267047Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7267157Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7267555Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7267658Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7268063Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7268182Z return prims._uniform_helper( 2024-12-18T00:10:34.7268275Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7268374Z x.shape, 2024-12-18T00:10:34.7268461Z ^^^^^^^^ 2024-12-18T00:10:34.7268562Z ...<4 lines>... 2024-12-18T00:10:34.7268662Z generator=generator, 2024-12-18T00:10:34.7268756Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7268849Z ) 2024-12-18T00:10:34.7268932Z ^ 2024-12-18T00:10:34.7269260Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7269365Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7269454Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7269889Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7269999Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7270384Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7270493Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7270895Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7271027Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7271135Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7271557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7271727Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7271846Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7272232Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7272349Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7272751Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7272870Z return prims._uniform_helper( 2024-12-18T00:10:34.7272961Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7273046Z x.shape, 2024-12-18T00:10:34.7273138Z ^^^^^^^^ 2024-12-18T00:10:34.7273229Z ...<4 lines>... 2024-12-18T00:10:34.7273390Z generator=generator, 2024-12-18T00:10:34.7273480Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7273719Z ) 2024-12-18T00:10:34.7273816Z ^ 2024-12-18T00:10:34.7274139Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7274257Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7274444Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7274865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7274987Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7275362Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7275485Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7276017Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7276167Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7276275Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7276683Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7276867Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7276983Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7277378Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7277480Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7277895Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7278006Z return prims._uniform_helper( 2024-12-18T00:10:34.7278111Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7278199Z x.shape, 2024-12-18T00:10:34.7278288Z ^^^^^^^^ 2024-12-18T00:10:34.7278454Z ...<4 lines>... 2024-12-18T00:10:34.7278558Z generator=generator, 2024-12-18T00:10:34.7278658Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7278743Z ) 2024-12-18T00:10:34.7278825Z ^ 2024-12-18T00:10:34.7279158Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7279268Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7279370Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7279791Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7279901Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7280292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7280400Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7280803Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7280940Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7281053Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7281462Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7281646Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7281756Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7282136Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7282246Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7282653Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7282772Z return prims._uniform_helper( 2024-12-18T00:10:34.7282863Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7283011Z x.shape, 2024-12-18T00:10:34.7283155Z ^^^^^^^^ 2024-12-18T00:10:34.7283245Z ...<4 lines>... 2024-12-18T00:10:34.7283354Z generator=generator, 2024-12-18T00:10:34.7283445Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7283540Z ) 2024-12-18T00:10:34.7283625Z ^ 2024-12-18T00:10:34.7283996Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7284116Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7284208Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7284638Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7284750Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7285125Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7285247Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7285643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7285790Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7285896Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7286318Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7286531Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7286687Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7287182Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7287287Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7287703Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7287811Z return prims._uniform_helper( 2024-12-18T00:10:34.7287912Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7288006Z x.shape, 2024-12-18T00:10:34.7288091Z ^^^^^^^^ 2024-12-18T00:10:34.7288191Z ...<4 lines>... 2024-12-18T00:10:34.7288295Z generator=generator, 2024-12-18T00:10:34.7288392Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7288477Z ) 2024-12-18T00:10:34.7288572Z ^ 2024-12-18T00:10:34.7288897Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7289002Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7289103Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7289526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7289642Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7290017Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7290139Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7290536Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7290666Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7290778Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7291191Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7291371Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7291482Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7291875Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7291978Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7292379Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7292534Z return prims._uniform_helper( 2024-12-18T00:10:34.7292659Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7292761Z x.shape, 2024-12-18T00:10:34.7292847Z ^^^^^^^^ 2024-12-18T00:10:34.7292937Z ...<4 lines>... 2024-12-18T00:10:34.7293049Z generator=generator, 2024-12-18T00:10:34.7293139Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7293236Z ) 2024-12-18T00:10:34.7293371Z ^ 2024-12-18T00:10:34.7293705Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7293811Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7293905Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7294335Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7294445Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7294832Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7294948Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7295347Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7295479Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7295583Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7296003Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7296173Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7296283Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7296678Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7296781Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7297186Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7297304Z return prims._uniform_helper( 2024-12-18T00:10:34.7297404Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7297545Z x.shape, 2024-12-18T00:10:34.7297669Z ^^^^^^^^ 2024-12-18T00:10:34.7297786Z ...<4 lines>... 2024-12-18T00:10:34.7297921Z generator=generator, 2024-12-18T00:10:34.7298016Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7298107Z ) 2024-12-18T00:10:34.7298190Z ^ 2024-12-18T00:10:34.7298521Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7298626Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7298716Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7299146Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7299257Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7299639Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7299750Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7300150Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7300283Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7300390Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7300813Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7300983Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7301103Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7301483Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7301595Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7302001Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7302193Z return prims._uniform_helper( 2024-12-18T00:10:34.7302296Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7302387Z x.shape, 2024-12-18T00:10:34.7302482Z ^^^^^^^^ 2024-12-18T00:10:34.7302571Z ...<4 lines>... 2024-12-18T00:10:34.7302725Z generator=generator, 2024-12-18T00:10:34.7302826Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7302912Z ) 2024-12-18T00:10:34.7303006Z ^ 2024-12-18T00:10:34.7303325Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7303437Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7303528Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7303946Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7304067Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7304445Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7304567Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7304959Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7305102Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7305207Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7305614Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7305790Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7305898Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7306292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7306394Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7306809Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7306916Z return prims._uniform_helper( 2024-12-18T00:10:34.7307006Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7307102Z x.shape, 2024-12-18T00:10:34.7307188Z ^^^^^^^^ 2024-12-18T00:10:34.7307288Z ...<4 lines>... 2024-12-18T00:10:34.7307389Z generator=generator, 2024-12-18T00:10:34.7307475Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7307571Z ) 2024-12-18T00:10:34.7307653Z ^ 2024-12-18T00:10:34.7307979Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7308084Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7308182Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7308603Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7308713Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7309098Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7309207Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7309613Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7309741Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7309853Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7310261Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7310427Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7310541Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7310923Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7311145Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7311550Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7311667Z return prims._uniform_helper( 2024-12-18T00:10:34.7311757Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7311848Z x.shape, 2024-12-18T00:10:34.7311990Z ^^^^^^^^ 2024-12-18T00:10:34.7312082Z ...<4 lines>... 2024-12-18T00:10:34.7312191Z generator=generator, 2024-12-18T00:10:34.7312279Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7312363Z ) 2024-12-18T00:10:34.7312456Z ^ 2024-12-18T00:10:34.7312777Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7312891Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7312984Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7313409Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7313521Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7313896Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7314012Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7314407Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7314546Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7314650Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7315066Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7315233Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7315342Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7315735Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7315839Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7316251Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7316359Z return prims._uniform_helper( 2024-12-18T00:10:34.7316457Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7316548Z x.shape, 2024-12-18T00:10:34.7316634Z ^^^^^^^^ 2024-12-18T00:10:34.7316733Z ...<4 lines>... 2024-12-18T00:10:34.7316834Z generator=generator, 2024-12-18T00:10:34.7316927Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7317013Z ) 2024-12-18T00:10:34.7317096Z ^ 2024-12-18T00:10:34.7317425Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7317531Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7317630Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7318051Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7318173Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7318635Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7318746Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7319158Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7319291Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7319406Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7319814Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7319994Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7320104Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7320518Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7320654Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7321057Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7321170Z return prims._uniform_helper( 2024-12-18T00:10:34.7321305Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7321398Z x.shape, 2024-12-18T00:10:34.7321501Z ^^^^^^^^ 2024-12-18T00:10:34.7321586Z ...<4 lines>... 2024-12-18T00:10:34.7321685Z generator=generator, 2024-12-18T00:10:34.7321780Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7321861Z ) 2024-12-18T00:10:34.7321955Z ^ 2024-12-18T00:10:34.7322270Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7322373Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7322475Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7322893Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7323010Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7323384Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7323506Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7323892Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7324032Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7324135Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7324541Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7324721Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7324829Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7325225Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7325324Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7325737Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7325844Z return prims._uniform_helper( 2024-12-18T00:10:34.7325933Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7326034Z x.shape, 2024-12-18T00:10:34.7326117Z ^^^^^^^^ 2024-12-18T00:10:34.7326214Z ...<4 lines>... 2024-12-18T00:10:34.7326311Z generator=generator, 2024-12-18T00:10:34.7326399Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7326496Z ) 2024-12-18T00:10:34.7326578Z ^ 2024-12-18T00:10:34.7326910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7327013Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7327109Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7327533Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7327642Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7328028Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7328133Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7328532Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7328658Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7328770Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7329179Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7329345Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7329514Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7329894Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7330008Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7330454Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7330572Z return prims._uniform_helper( 2024-12-18T00:10:34.7330659Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7330744Z x.shape, 2024-12-18T00:10:34.7330845Z ^^^^^^^^ 2024-12-18T00:10:34.7330928Z ...<4 lines>... 2024-12-18T00:10:34.7331041Z generator=generator, 2024-12-18T00:10:34.7331125Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7331207Z ) 2024-12-18T00:10:34.7331297Z ^ 2024-12-18T00:10:34.7331613Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7331735Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7331826Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7332241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7332357Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7332735Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7332852Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7333239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7333377Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7333478Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7333896Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7334062Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7334170Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7334559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7334659Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7335079Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7335184Z return prims._uniform_helper( 2024-12-18T00:10:34.7335283Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7335369Z x.shape, 2024-12-18T00:10:34.7335450Z ^^^^^^^^ 2024-12-18T00:10:34.7335543Z ...<4 lines>... 2024-12-18T00:10:34.7335641Z generator=generator, 2024-12-18T00:10:34.7335737Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7335818Z ) 2024-12-18T00:10:34.7335896Z ^ 2024-12-18T00:10:34.7336218Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7336324Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7336425Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7336842Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7336964Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7337336Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7337440Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7337838Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7337967Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7338080Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7338484Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7338728Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7338833Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7339212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7339369Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7339775Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7339895Z return prims._uniform_helper( 2024-12-18T00:10:34.7339983Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7340081Z x.shape, 2024-12-18T00:10:34.7340163Z ^^^^^^^^ 2024-12-18T00:10:34.7340250Z ...<4 lines>... 2024-12-18T00:10:34.7340357Z generator=generator, 2024-12-18T00:10:34.7340443Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7340536Z ) 2024-12-18T00:10:34.7340618Z ^ 2024-12-18T00:10:34.7340936Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7341050Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7341139Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7341568Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7341673Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7342055Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7342160Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7342548Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7342684Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7342783Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7343202Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7343369Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7343486Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7343868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7343965Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7344377Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7344479Z return prims._uniform_helper( 2024-12-18T00:10:34.7344576Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7344660Z x.shape, 2024-12-18T00:10:34.7344753Z ^^^^^^^^ 2024-12-18T00:10:34.7344835Z ...<4 lines>... 2024-12-18T00:10:34.7344936Z generator=generator, 2024-12-18T00:10:34.7345032Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7345113Z ) 2024-12-18T00:10:34.7345209Z ^ 2024-12-18T00:10:34.7345523Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7345626Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7345727Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7346147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7346267Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7346639Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7346759Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7347148Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7347275Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7347388Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7347858Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7348035Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7348137Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7348578Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7348677Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7349077Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7349194Z return prims._uniform_helper( 2024-12-18T00:10:34.7349280Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7349377Z x.shape, 2024-12-18T00:10:34.7349459Z ^^^^^^^^ 2024-12-18T00:10:34.7349554Z ...<4 lines>... 2024-12-18T00:10:34.7349651Z generator=generator, 2024-12-18T00:10:34.7349738Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7349831Z ) 2024-12-18T00:10:34.7349910Z ^ 2024-12-18T00:10:34.7350236Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7350337Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7350425Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7350861Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7350967Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7351355Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7351461Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7351859Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7351986Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7352090Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7352508Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7352672Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7352787Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7353168Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7353276Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7353674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7353779Z return prims._uniform_helper( 2024-12-18T00:10:34.7353876Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7353960Z x.shape, 2024-12-18T00:10:34.7354051Z ^^^^^^^^ 2024-12-18T00:10:34.7354138Z ...<4 lines>... 2024-12-18T00:10:34.7354236Z generator=generator, 2024-12-18T00:10:34.7354332Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7354414Z ) 2024-12-18T00:10:34.7354507Z ^ 2024-12-18T00:10:34.7354820Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7354939Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7355030Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7355449Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7355568Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7355941Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7356061Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7356448Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7356614Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7356742Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7357147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7357324Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7357479Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7357871Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7357969Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7358481Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7358625Z return prims._uniform_helper( 2024-12-18T00:10:34.7358715Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7358812Z x.shape, 2024-12-18T00:10:34.7358899Z ^^^^^^^^ 2024-12-18T00:10:34.7359001Z ...<4 lines>... 2024-12-18T00:10:34.7359096Z generator=generator, 2024-12-18T00:10:34.7359182Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7359277Z ) 2024-12-18T00:10:34.7359359Z ^ 2024-12-18T00:10:34.7359690Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7359797Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7359898Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7360313Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7360425Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7360811Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7360919Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7361319Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7361451Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7361565Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7361966Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7362137Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7362253Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7362631Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7362742Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7363140Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7363254Z return prims._uniform_helper( 2024-12-18T00:10:34.7363342Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7363428Z x.shape, 2024-12-18T00:10:34.7363528Z ^^^^^^^^ 2024-12-18T00:10:34.7363611Z ...<4 lines>... 2024-12-18T00:10:34.7363723Z generator=generator, 2024-12-18T00:10:34.7363806Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7363887Z ) 2024-12-18T00:10:34.7363976Z ^ 2024-12-18T00:10:34.7364295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7364408Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7364497Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7364924Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7365030Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7365403Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7365518Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7365944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7366111Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7366209Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7366624Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7366852Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7366959Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7367352Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7367449Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7367865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7367967Z return prims._uniform_helper( 2024-12-18T00:10:34.7368066Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7368151Z x.shape, 2024-12-18T00:10:34.7368231Z ^^^^^^^^ 2024-12-18T00:10:34.7368327Z ...<4 lines>... 2024-12-18T00:10:34.7368425Z generator=generator, 2024-12-18T00:10:34.7368516Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7368595Z ) 2024-12-18T00:10:34.7368675Z ^ 2024-12-18T00:10:34.7369009Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7369158Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7369297Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7369785Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7369904Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7370277Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7370384Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7370785Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7370911Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7371024Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7371432Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7371610Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7371715Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7372100Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7372213Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7372616Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7372733Z return prims._uniform_helper( 2024-12-18T00:10:34.7372823Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7372918Z x.shape, 2024-12-18T00:10:34.7372999Z ^^^^^^^^ 2024-12-18T00:10:34.7373085Z ...<4 lines>... 2024-12-18T00:10:34.7373195Z generator=generator, 2024-12-18T00:10:34.7373281Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7373374Z ) 2024-12-18T00:10:34.7373457Z ^ 2024-12-18T00:10:34.7373914Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7374030Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7374118Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7374550Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7374658Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7375046Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7375274Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7375664Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7375805Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7375905Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7376395Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7376562Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7376679Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7377056Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7377165Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7377565Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7377673Z return prims._uniform_helper( 2024-12-18T00:10:34.7377775Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7377857Z x.shape, 2024-12-18T00:10:34.7377952Z ^^^^^^^^ 2024-12-18T00:10:34.7378037Z ...<4 lines>... 2024-12-18T00:10:34.7378134Z generator=generator, 2024-12-18T00:10:34.7378232Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7378316Z ) 2024-12-18T00:10:34.7378412Z ^ 2024-12-18T00:10:34.7378726Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7378829Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7378924Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7379341Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7379458Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7379828Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7379950Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7380337Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7380475Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7380579Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7380983Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7381160Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7381262Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7381653Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7381749Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7382162Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7382271Z return prims._uniform_helper( 2024-12-18T00:10:34.7382358Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7382454Z x.shape, 2024-12-18T00:10:34.7382535Z ^^^^^^^^ 2024-12-18T00:10:34.7382631Z ...<4 lines>... 2024-12-18T00:10:34.7382729Z generator=generator, 2024-12-18T00:10:34.7382815Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7382906Z ) 2024-12-18T00:10:34.7382986Z ^ 2024-12-18T00:10:34.7383317Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7383422Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7383512Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7383937Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7384044Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7384458Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7384607Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7385011Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7385187Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7385301Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7385703Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7385869Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7385985Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7386362Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7386471Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7386874Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7386987Z return prims._uniform_helper( 2024-12-18T00:10:34.7387072Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7387156Z x.shape, 2024-12-18T00:10:34.7387250Z ^^^^^^^^ 2024-12-18T00:10:34.7387338Z ...<4 lines>... 2024-12-18T00:10:34.7387448Z generator=generator, 2024-12-18T00:10:34.7387532Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7387613Z ) 2024-12-18T00:10:34.7387703Z ^ 2024-12-18T00:10:34.7388020Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7388132Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7388232Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7388658Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7388769Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7389147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7389269Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7389661Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7389805Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7389910Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7390327Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7390496Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7390605Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7391000Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7391106Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7391517Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7391626Z return prims._uniform_helper( 2024-12-18T00:10:34.7391725Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7391814Z x.shape, 2024-12-18T00:10:34.7391905Z ^^^^^^^^ 2024-12-18T00:10:34.7392008Z ...<4 lines>... 2024-12-18T00:10:34.7392109Z generator=generator, 2024-12-18T00:10:34.7392207Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7392291Z ) 2024-12-18T00:10:34.7392372Z ^ 2024-12-18T00:10:34.7392700Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7392808Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7392911Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7393330Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7393498Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7393875Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7393981Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7394430Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7394563Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7394676Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7395084Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7395266Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7395377Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7395758Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7395875Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7396279Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7396398Z return prims._uniform_helper( 2024-12-18T00:10:34.7396490Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7396587Z x.shape, 2024-12-18T00:10:34.7396673Z ^^^^^^^^ 2024-12-18T00:10:34.7396760Z ...<4 lines>... 2024-12-18T00:10:34.7396869Z generator=generator, 2024-12-18T00:10:34.7396957Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7397051Z ) 2024-12-18T00:10:34.7397132Z ^ 2024-12-18T00:10:34.7397449Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7397564Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7397653Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7398078Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7398191Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7398652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7398761Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7399154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7399296Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7399397Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7399818Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7399988Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7400105Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7400488Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7400590Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7401002Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7401109Z return prims._uniform_helper( 2024-12-18T00:10:34.7401213Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7401301Z x.shape, 2024-12-18T00:10:34.7401396Z ^^^^^^^^ 2024-12-18T00:10:34.7401488Z ...<4 lines>... 2024-12-18T00:10:34.7401589Z generator=generator, 2024-12-18T00:10:34.7401693Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7401777Z ) 2024-12-18T00:10:34.7401865Z ^ 2024-12-18T00:10:34.7402184Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7402289Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7402390Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7402870Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7402987Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7403363Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7403538Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7403932Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7404061Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7404174Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7404585Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7404763Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7404873Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7405263Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7405365Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7405765Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7405890Z return prims._uniform_helper( 2024-12-18T00:10:34.7405982Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7406083Z x.shape, 2024-12-18T00:10:34.7406172Z ^^^^^^^^ 2024-12-18T00:10:34.7406267Z ...<4 lines>... 2024-12-18T00:10:34.7406371Z generator=generator, 2024-12-18T00:10:34.7406460Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7406556Z ) 2024-12-18T00:10:34.7406640Z ^ 2024-12-18T00:10:34.7406966Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7407072Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7407165Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7407593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7407700Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7408087Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7408195Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7408598Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7408727Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7408825Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7409241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7409407Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7409527Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7409910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7410017Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7410421Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7410526Z return prims._uniform_helper( 2024-12-18T00:10:34.7410625Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7410711Z x.shape, 2024-12-18T00:10:34.7410804Z ^^^^^^^^ 2024-12-18T00:10:34.7410892Z ...<4 lines>... 2024-12-18T00:10:34.7410996Z generator=generator, 2024-12-18T00:10:34.7411083Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7411164Z ) 2024-12-18T00:10:34.7411255Z ^ 2024-12-18T00:10:34.7411571Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7411734Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7411826Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7412241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7412361Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7412786Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7412903Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7413296Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7413438Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7413540Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7413944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7414123Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7414232Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7414622Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7414723Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7415141Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7415246Z return prims._uniform_helper( 2024-12-18T00:10:34.7415335Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7415436Z x.shape, 2024-12-18T00:10:34.7415526Z ^^^^^^^^ 2024-12-18T00:10:34.7415626Z ...<4 lines>... 2024-12-18T00:10:34.7415725Z generator=generator, 2024-12-18T00:10:34.7415809Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7415903Z ) 2024-12-18T00:10:34.7415985Z ^ 2024-12-18T00:10:34.7416313Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7416422Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7416524Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7416943Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7417055Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7417440Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7417549Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7417944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7418074Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7418183Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7418590Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7418758Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7418876Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7419254Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7419367Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7419770Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7419889Z return prims._uniform_helper( 2024-12-18T00:10:34.7419978Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7420063Z x.shape, 2024-12-18T00:10:34.7420160Z ^^^^^^^^ 2024-12-18T00:10:34.7420249Z ...<4 lines>... 2024-12-18T00:10:34.7420358Z generator=generator, 2024-12-18T00:10:34.7420470Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7420591Z ) 2024-12-18T00:10:34.7420737Z ^ 2024-12-18T00:10:34.7421088Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7421202Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7421294Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7421730Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7421888Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7422267Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7422388Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7422778Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7422922Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7423027Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7423446Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7423619Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7423728Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7424122Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7424223Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7424638Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7424747Z return prims._uniform_helper( 2024-12-18T00:10:34.7424848Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7424939Z x.shape, 2024-12-18T00:10:34.7425025Z ^^^^^^^^ 2024-12-18T00:10:34.7425128Z ...<4 lines>... 2024-12-18T00:10:34.7425230Z generator=generator, 2024-12-18T00:10:34.7425328Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7425415Z ) 2024-12-18T00:10:34.7425498Z ^ 2024-12-18T00:10:34.7425832Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7425940Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7426043Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7426467Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7426591Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7426969Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7427073Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7427475Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7427606Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7427721Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7428138Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7428319Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7428429Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7428826Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7428927Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7429328Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7429446Z return prims._uniform_helper( 2024-12-18T00:10:34.7429538Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7429634Z x.shape, 2024-12-18T00:10:34.7429719Z ^^^^^^^^ 2024-12-18T00:10:34.7429805Z ...<4 lines>... 2024-12-18T00:10:34.7429912Z generator=generator, 2024-12-18T00:10:34.7430027Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7430148Z ) 2024-12-18T00:10:34.7430231Z ^ 2024-12-18T00:10:34.7430549Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7430666Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7430756Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7431239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7431353Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7431736Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7431845Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7432231Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7432368Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7432470Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7432889Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7433057Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7433174Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7433559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7433667Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7434071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7434180Z return prims._uniform_helper( 2024-12-18T00:10:34.7434283Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7434374Z x.shape, 2024-12-18T00:10:34.7434466Z ^^^^^^^^ 2024-12-18T00:10:34.7434556Z ...<4 lines>... 2024-12-18T00:10:34.7434661Z generator=generator, 2024-12-18T00:10:34.7434761Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7434844Z ) 2024-12-18T00:10:34.7434932Z ^ 2024-12-18T00:10:34.7435249Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7435353Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7435457Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7435875Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7435993Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7436371Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7436484Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7436877Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7437017Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7437123Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7437529Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7437708Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7437821Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7438207Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7438387Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7438802Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7438909Z return prims._uniform_helper( 2024-12-18T00:10:34.7438998Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7439097Z x.shape, 2024-12-18T00:10:34.7439221Z ^^^^^^^^ 2024-12-18T00:10:34.7439359Z ...<4 lines>... 2024-12-18T00:10:34.7439458Z generator=generator, 2024-12-18T00:10:34.7439545Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7439642Z ) 2024-12-18T00:10:34.7439727Z ^ 2024-12-18T00:10:34.7440059Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7440217Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7440308Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7440742Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7440850Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7441236Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7441345Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7441745Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7441883Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7441994Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7442403Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7442577Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7442696Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7443079Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7443193Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7443596Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7443711Z return prims._uniform_helper( 2024-12-18T00:10:34.7443801Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7443890Z x.shape, 2024-12-18T00:10:34.7443984Z ^^^^^^^^ 2024-12-18T00:10:34.7444071Z ...<4 lines>... 2024-12-18T00:10:34.7444178Z generator=generator, 2024-12-18T00:10:34.7444267Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7444349Z ) 2024-12-18T00:10:34.7444438Z ^ 2024-12-18T00:10:34.7444758Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7444873Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7444962Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7445391Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7445501Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7445875Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7445991Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7446384Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7446527Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7446631Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7447048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7447218Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7447323Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7447719Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7447819Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7448231Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7448338Z return prims._uniform_helper( 2024-12-18T00:10:34.7448466Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7448579Z x.shape, 2024-12-18T00:10:34.7448664Z ^^^^^^^^ 2024-12-18T00:10:34.7448760Z ...<4 lines>... 2024-12-18T00:10:34.7448860Z generator=generator, 2024-12-18T00:10:34.7448954Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7449040Z ) 2024-12-18T00:10:34.7449125Z ^ 2024-12-18T00:10:34.7449499Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7449606Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7449708Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7450127Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7450245Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7450625Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7450734Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7451141Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7451272Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7451387Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7451802Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7451981Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7452092Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7452476Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7452587Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7452990Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7453114Z return prims._uniform_helper( 2024-12-18T00:10:34.7453208Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7453304Z x.shape, 2024-12-18T00:10:34.7453391Z ^^^^^^^^ 2024-12-18T00:10:34.7453480Z ...<4 lines>... 2024-12-18T00:10:34.7453590Z generator=generator, 2024-12-18T00:10:34.7453678Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7453765Z ) 2024-12-18T00:10:34.7453852Z ^ 2024-12-18T00:10:34.7454167Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7454280Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7454370Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7454794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7454905Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7455289Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7455401Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7455793Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7455935Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7456037Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7456463Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7456634Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7456755Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7457138Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7457240Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7457660Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7457883Z return prims._uniform_helper( 2024-12-18T00:10:34.7457986Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7458074Z x.shape, 2024-12-18T00:10:34.7458174Z ^^^^^^^^ 2024-12-18T00:10:34.7458265Z ...<4 lines>... 2024-12-18T00:10:34.7458366Z generator=generator, 2024-12-18T00:10:34.7458468Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7458602Z ) 2024-12-18T00:10:34.7458706Z ^ 2024-12-18T00:10:34.7459026Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7459134Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7459241Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7459666Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7459790Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7460168Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7460293Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7460688Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7460821Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7460942Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7461352Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7461537Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7461648Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7462044Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7462145Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7462553Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7462679Z return prims._uniform_helper( 2024-12-18T00:10:34.7462773Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7462872Z x.shape, 2024-12-18T00:10:34.7462957Z ^^^^^^^^ 2024-12-18T00:10:34.7463058Z ...<4 lines>... 2024-12-18T00:10:34.7463156Z generator=generator, 2024-12-18T00:10:34.7463248Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7463342Z ) 2024-12-18T00:10:34.7463428Z ^ 2024-12-18T00:10:34.7463752Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7463855Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7463946Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7464375Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7464487Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7464875Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7464985Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7465389Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7465522Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7465624Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7466041Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7466210Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7466328Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7466711Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7466823Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7467276Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7467386Z return prims._uniform_helper( 2024-12-18T00:10:34.7467485Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7467572Z x.shape, 2024-12-18T00:10:34.7467672Z ^^^^^^^^ 2024-12-18T00:10:34.7467759Z ...<4 lines>... 2024-12-18T00:10:34.7467916Z generator=generator, 2024-12-18T00:10:34.7468006Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7468089Z ) 2024-12-18T00:10:34.7468184Z ^ 2024-12-18T00:10:34.7468505Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7468625Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7468717Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7469136Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7469259Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7469637Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7469754Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7470149Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7470296Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7470402Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7470811Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7470990Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7471099Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7471493Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7471595Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7472010Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7472116Z return prims._uniform_helper( 2024-12-18T00:10:34.7472205Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7472302Z x.shape, 2024-12-18T00:10:34.7472391Z ^^^^^^^^ 2024-12-18T00:10:34.7472489Z ...<4 lines>... 2024-12-18T00:10:34.7472588Z generator=generator, 2024-12-18T00:10:34.7472689Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7472770Z ) 2024-12-18T00:10:34.7472854Z ^ 2024-12-18T00:10:34.7473181Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7473286Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7473389Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7473994Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7474110Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7474504Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7474614Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7475025Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7475155Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7475271Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7475677Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7475847Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7475971Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7476354Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7476577Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7476983Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7477104Z return prims._uniform_helper( 2024-12-18T00:10:34.7477193Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7477354Z x.shape, 2024-12-18T00:10:34.7477453Z ^^^^^^^^ 2024-12-18T00:10:34.7477542Z ...<4 lines>... 2024-12-18T00:10:34.7477653Z generator=generator, 2024-12-18T00:10:34.7477742Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7477826Z ) 2024-12-18T00:10:34.7477922Z ^ 2024-12-18T00:10:34.7478241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7478427Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7478520Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7478954Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7479068Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7479442Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7479561Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7479955Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7480096Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7480199Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7480619Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7480785Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7480893Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7481287Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7481389Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7481801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7481907Z return prims._uniform_helper( 2024-12-18T00:10:34.7482011Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7482095Z x.shape, 2024-12-18T00:10:34.7482181Z ^^^^^^^^ 2024-12-18T00:10:34.7482278Z ...<4 lines>... 2024-12-18T00:10:34.7482382Z generator=generator, 2024-12-18T00:10:34.7482479Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7482563Z ) 2024-12-18T00:10:34.7482644Z ^ 2024-12-18T00:10:34.7482968Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7483072Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7483174Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7483597Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7483717Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7484088Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7484201Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7484603Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7484733Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7484849Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7485254Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7485434Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7485545Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7486001Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7486105Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7486512Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7486683Z return prims._uniform_helper( 2024-12-18T00:10:34.7486774Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7486874Z x.shape, 2024-12-18T00:10:34.7486961Z ^^^^^^^^ 2024-12-18T00:10:34.7487050Z ...<4 lines>... 2024-12-18T00:10:34.7487162Z generator=generator, 2024-12-18T00:10:34.7487251Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7487347Z ) 2024-12-18T00:10:34.7487429Z ^ 2024-12-18T00:10:34.7487749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7487868Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7487963Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7488394Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7488502Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7488895Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7489003Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7489393Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7489533Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7489636Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7490054Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7490223Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7490349Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7490729Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7490844Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7491248Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7491355Z return prims._uniform_helper( 2024-12-18T00:10:34.7491459Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7491548Z x.shape, 2024-12-18T00:10:34.7491647Z ^^^^^^^^ 2024-12-18T00:10:34.7491737Z ...<4 lines>... 2024-12-18T00:10:34.7491839Z generator=generator, 2024-12-18T00:10:34.7491941Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7492029Z ) 2024-12-18T00:10:34.7492125Z ^ 2024-12-18T00:10:34.7492445Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7492558Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7492664Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7493083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7493209Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7493591Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7493718Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7494110Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7494259Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7494369Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7494779Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7494998Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7495139Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7495537Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7495641Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7496114Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7496224Z return prims._uniform_helper( 2024-12-18T00:10:34.7496316Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7496416Z x.shape, 2024-12-18T00:10:34.7496504Z ^^^^^^^^ 2024-12-18T00:10:34.7496606Z ...<4 lines>... 2024-12-18T00:10:34.7496709Z generator=generator, 2024-12-18T00:10:34.7496800Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7496898Z ) 2024-12-18T00:10:34.7496982Z ^ 2024-12-18T00:10:34.7497319Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7497429Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7497521Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7497949Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7498063Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7498454Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7498562Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7498963Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7499091Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7499204Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7499611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7499781Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7499898Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7500279Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7500395Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7500797Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7500916Z return prims._uniform_helper( 2024-12-18T00:10:34.7501005Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7501100Z x.shape, 2024-12-18T00:10:34.7501195Z ^^^^^^^^ 2024-12-18T00:10:34.7501283Z ...<4 lines>... 2024-12-18T00:10:34.7501395Z generator=generator, 2024-12-18T00:10:34.7501484Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7501568Z ) 2024-12-18T00:10:34.7501665Z ^ 2024-12-18T00:10:34.7501985Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7502104Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7502196Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7502626Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7502740Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7503115Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7503237Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7503630Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7503773Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7503879Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7504299Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7504523Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7504632Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7505087Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7505191Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7505608Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7505715Z return prims._uniform_helper( 2024-12-18T00:10:34.7505817Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7505905Z x.shape, 2024-12-18T00:10:34.7505990Z ^^^^^^^^ 2024-12-18T00:10:34.7506089Z ...<4 lines>... 2024-12-18T00:10:34.7506190Z generator=generator, 2024-12-18T00:10:34.7506291Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7506375Z ) 2024-12-18T00:10:34.7506462Z ^ 2024-12-18T00:10:34.7506791Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7506897Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7506998Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7507420Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7507542Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7507917Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7508026Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7508433Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7508567Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7508683Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7509097Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7509278Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7509384Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7509769Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7509882Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7510287Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7510408Z return prims._uniform_helper( 2024-12-18T00:10:34.7510499Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7510600Z x.shape, 2024-12-18T00:10:34.7510685Z ^^^^^^^^ 2024-12-18T00:10:34.7510775Z ...<4 lines>... 2024-12-18T00:10:34.7510885Z generator=generator, 2024-12-18T00:10:34.7510974Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7511073Z ) 2024-12-18T00:10:34.7511158Z ^ 2024-12-18T00:10:34.7511474Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7511589Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7511680Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7512113Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7512221Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7512608Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7512718Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7513110Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7513247Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7513402Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7513820Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7513988Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7514109Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7514542Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7514643Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7515059Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7515167Z return prims._uniform_helper( 2024-12-18T00:10:34.7515268Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7515355Z x.shape, 2024-12-18T00:10:34.7515448Z ^^^^^^^^ 2024-12-18T00:10:34.7515537Z ...<4 lines>... 2024-12-18T00:10:34.7515638Z generator=generator, 2024-12-18T00:10:34.7515737Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7515820Z ) 2024-12-18T00:10:34.7515916Z ^ 2024-12-18T00:10:34.7516235Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7516339Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7516447Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7516862Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7516983Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7517358Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7517476Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7517868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7518000Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7518116Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7518602Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7518785Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7518896Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7519295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7519397Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7519801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7519920Z return prims._uniform_helper( 2024-12-18T00:10:34.7520011Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7520114Z x.shape, 2024-12-18T00:10:34.7520202Z ^^^^^^^^ 2024-12-18T00:10:34.7520306Z ...<4 lines>... 2024-12-18T00:10:34.7520408Z generator=generator, 2024-12-18T00:10:34.7520504Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7520599Z ) 2024-12-18T00:10:34.7520683Z ^ 2024-12-18T00:10:34.7521011Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7521119Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7521211Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7521642Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7521752Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7522141Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7522250Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7522655Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7522842Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7522946Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7523366Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7523581Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7523700Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7524084Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7524199Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7524604Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7524711Z return prims._uniform_helper( 2024-12-18T00:10:34.7524816Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7524906Z x.shape, 2024-12-18T00:10:34.7525007Z ^^^^^^^^ 2024-12-18T00:10:34.7525096Z ...<4 lines>... 2024-12-18T00:10:34.7525205Z generator=generator, 2024-12-18T00:10:34.7525294Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7525379Z ) 2024-12-18T00:10:34.7525475Z ^ 2024-12-18T00:10:34.7525794Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7525907Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7526000Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7526416Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7526538Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7526914Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7527035Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7527429Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7527570Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7527674Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7528079Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7528263Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7528373Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7528766Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7528868Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7529286Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7529394Z return prims._uniform_helper( 2024-12-18T00:10:34.7529485Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7529589Z x.shape, 2024-12-18T00:10:34.7529675Z ^^^^^^^^ 2024-12-18T00:10:34.7529775Z ...<4 lines>... 2024-12-18T00:10:34.7529876Z generator=generator, 2024-12-18T00:10:34.7529979Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7530063Z ) 2024-12-18T00:10:34.7530147Z ^ 2024-12-18T00:10:34.7530482Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7530585Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7530687Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7531106Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7531210Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7531599Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7531706Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7532174Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7532300Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7532413Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7532868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7533038Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7533156Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7533536Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7533645Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7534047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7534165Z return prims._uniform_helper( 2024-12-18T00:10:34.7534257Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7534345Z x.shape, 2024-12-18T00:10:34.7534437Z ^^^^^^^^ 2024-12-18T00:10:34.7534526Z ...<4 lines>... 2024-12-18T00:10:34.7534633Z generator=generator, 2024-12-18T00:10:34.7534721Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7534804Z ) 2024-12-18T00:10:34.7534897Z ^ 2024-12-18T00:10:34.7535215Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7535331Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7535421Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7535851Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7535960Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7536335Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7536454Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7536847Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7536987Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7537090Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7537510Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7537678Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7537785Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7538174Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7538277Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7538687Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7538799Z return prims._uniform_helper( 2024-12-18T00:10:34.7538897Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7538985Z x.shape, 2024-12-18T00:10:34.7539069Z ^^^^^^^^ 2024-12-18T00:10:34.7539165Z ...<4 lines>... 2024-12-18T00:10:34.7539264Z generator=generator, 2024-12-18T00:10:34.7539362Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7539447Z ) 2024-12-18T00:10:34.7539528Z ^ 2024-12-18T00:10:34.7539856Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7539960Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7540061Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7540479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7540600Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7540975Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7541132Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7541531Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7541660Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7541832Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7542244Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7542425Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7542533Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7542929Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7543029Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7543434Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7543557Z return prims._uniform_helper( 2024-12-18T00:10:34.7543654Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7543755Z x.shape, 2024-12-18T00:10:34.7543841Z ^^^^^^^^ 2024-12-18T00:10:34.7543927Z ...<4 lines>... 2024-12-18T00:10:34.7544041Z generator=generator, 2024-12-18T00:10:34.7544134Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7544229Z ) 2024-12-18T00:10:34.7544312Z ^ 2024-12-18T00:10:34.7544631Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7544743Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7544833Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7545267Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7545374Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7545763Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7545872Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7546261Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7546404Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7546508Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7546928Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7547097Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7547214Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7547596Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7547703Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7548108Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7548217Z return prims._uniform_helper( 2024-12-18T00:10:34.7548313Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7548400Z x.shape, 2024-12-18T00:10:34.7548493Z ^^^^^^^^ 2024-12-18T00:10:34.7548582Z ...<4 lines>... 2024-12-18T00:10:34.7548684Z generator=generator, 2024-12-18T00:10:34.7548780Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7548863Z ) 2024-12-18T00:10:34.7548956Z ^ 2024-12-18T00:10:34.7549271Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7549376Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7549474Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7549890Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7550040Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7550441Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7550555Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7550947Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7551133Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7551239Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7551646Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7551821Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7551929Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7552318Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7552422Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7552835Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7552940Z return prims._uniform_helper( 2024-12-18T00:10:34.7553031Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7553126Z x.shape, 2024-12-18T00:10:34.7553216Z ^^^^^^^^ 2024-12-18T00:10:34.7553316Z ...<4 lines>... 2024-12-18T00:10:34.7553415Z generator=generator, 2024-12-18T00:10:34.7553502Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7553597Z ) 2024-12-18T00:10:34.7553684Z ^ 2024-12-18T00:10:34.7554011Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7554115Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7554204Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7554634Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7554747Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7555134Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7555241Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7555643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7555773Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7555887Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7556292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7556459Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7560712Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7561169Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7561291Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7561705Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7561814Z return prims._uniform_helper( 2024-12-18T00:10:34.7561916Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7562013Z x.shape, 2024-12-18T00:10:34.7562106Z ^^^^^^^^ 2024-12-18T00:10:34.7562196Z ...<4 lines>... 2024-12-18T00:10:34.7562296Z generator=generator, 2024-12-18T00:10:34.7562399Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7562483Z ) 2024-12-18T00:10:34.7562576Z ^ 2024-12-18T00:10:34.7562897Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7563009Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7563101Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7563521Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7563754Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7564134Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7564253Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7564707Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7564848Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7564951Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7565361Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7565539Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7565648Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7566045Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7566150Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7566557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7566664Z return prims._uniform_helper( 2024-12-18T00:10:34.7566755Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7566855Z x.shape, 2024-12-18T00:10:34.7566941Z ^^^^^^^^ 2024-12-18T00:10:34.7567038Z ...<4 lines>... 2024-12-18T00:10:34.7567138Z generator=generator, 2024-12-18T00:10:34.7567226Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7567319Z ) 2024-12-18T00:10:34.7567403Z ^ 2024-12-18T00:10:34.7567729Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7567836Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7567936Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7568357Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7568467Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7568852Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7568962Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7569361Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7569491Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7569600Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7570009Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7570174Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7570289Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7570676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7570784Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7571190Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7571307Z return prims._uniform_helper( 2024-12-18T00:10:34.7571397Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7571484Z x.shape, 2024-12-18T00:10:34.7571577Z ^^^^^^^^ 2024-12-18T00:10:34.7571664Z ...<4 lines>... 2024-12-18T00:10:34.7571774Z generator=generator, 2024-12-18T00:10:34.7571863Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7571945Z ) 2024-12-18T00:10:34.7572038Z ^ 2024-12-18T00:10:34.7572356Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7572469Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7572617Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7573045Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7573154Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7573764Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7573885Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7574276Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7574418Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7574521Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7574945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7575115Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7575227Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7575621Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7575727Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7576141Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7576248Z return prims._uniform_helper( 2024-12-18T00:10:34.7576345Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7576436Z x.shape, 2024-12-18T00:10:34.7576519Z ^^^^^^^^ 2024-12-18T00:10:34.7576618Z ...<4 lines>... 2024-12-18T00:10:34.7576717Z generator=generator, 2024-12-18T00:10:34.7576816Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7576900Z ) 2024-12-18T00:10:34.7576980Z ^ 2024-12-18T00:10:34.7577306Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7577415Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7577516Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7577935Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7578051Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7578431Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7578538Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7578938Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7579066Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7579180Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7579589Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7579768Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7579877Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7580256Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7580368Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7580777Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7580891Z return prims._uniform_helper( 2024-12-18T00:10:34.7580981Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7581076Z x.shape, 2024-12-18T00:10:34.7581160Z ^^^^^^^^ 2024-12-18T00:10:34.7581248Z ...<4 lines>... 2024-12-18T00:10:34.7581356Z generator=generator, 2024-12-18T00:10:34.7581443Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7581531Z ) 2024-12-18T00:10:34.7581613Z ^ 2024-12-18T00:10:34.7581928Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7582130Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7582221Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7582647Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7582756Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7583196Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7583305Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7583693Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7583832Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7583937Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7584356Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7584526Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7584641Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7585024Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7585135Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7585539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7585647Z return prims._uniform_helper( 2024-12-18T00:10:34.7585745Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7585832Z x.shape, 2024-12-18T00:10:34.7585927Z ^^^^^^^^ 2024-12-18T00:10:34.7586015Z ...<4 lines>... 2024-12-18T00:10:34.7586115Z generator=generator, 2024-12-18T00:10:34.7586213Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7586294Z ) 2024-12-18T00:10:34.7586385Z ^ 2024-12-18T00:10:34.7586702Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7586807Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7586906Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7587324Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7587444Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7587823Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7587939Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7588328Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7588455Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7588564Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7588970Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7589149Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7589257Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7589650Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7589751Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7590160Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7590267Z return prims._uniform_helper( 2024-12-18T00:10:34.7590360Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7590456Z x.shape, 2024-12-18T00:10:34.7590542Z ^^^^^^^^ 2024-12-18T00:10:34.7590639Z ...<4 lines>... 2024-12-18T00:10:34.7590742Z generator=generator, 2024-12-18T00:10:34.7590831Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7590950Z ) 2024-12-18T00:10:34.7591060Z ^ 2024-12-18T00:10:34.7591386Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7591492Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7591581Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7592061Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7592174Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7592559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7592668Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7593066Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7593197Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7593311Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7593720Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7593890Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7594003Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7594389Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7594495Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7594899Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7595012Z return prims._uniform_helper( 2024-12-18T00:10:34.7595102Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7595191Z x.shape, 2024-12-18T00:10:34.7595284Z ^^^^^^^^ 2024-12-18T00:10:34.7595372Z ...<4 lines>... 2024-12-18T00:10:34.7595479Z generator=generator, 2024-12-18T00:10:34.7595568Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7595651Z ) 2024-12-18T00:10:34.7595744Z ^ 2024-12-18T00:10:34.7596063Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7596176Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7596265Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7596687Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7596804Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7597179Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7597292Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7597681Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7597823Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7597927Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7598427Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7598599Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7598714Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7599109Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7599208Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7599616Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7599723Z return prims._uniform_helper( 2024-12-18T00:10:34.7599822Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7599911Z x.shape, 2024-12-18T00:10:34.7599997Z ^^^^^^^^ 2024-12-18T00:10:34.7600094Z ...<4 lines>... 2024-12-18T00:10:34.7600228Z generator=generator, 2024-12-18T00:10:34.7600352Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7600435Z ) 2024-12-18T00:10:34.7600520Z ^ 2024-12-18T00:10:34.7600848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7600953Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7601169Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7601589Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7601710Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7602085Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7602195Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7602593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7602726Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7602840Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7603249Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7603430Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7603543Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7603922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7604027Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7604445Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7604554Z return prims._uniform_helper( 2024-12-18T00:10:34.7604652Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7604741Z x.shape, 2024-12-18T00:10:34.7604828Z ^^^^^^^^ 2024-12-18T00:10:34.7604928Z ...<4 lines>... 2024-12-18T00:10:34.7605031Z generator=generator, 2024-12-18T00:10:34.7605147Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7605230Z ) 2024-12-18T00:10:34.7605312Z ^ 2024-12-18T00:10:34.7605636Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7605743Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7605839Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7606256Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7606364Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7606752Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7606860Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7607259Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7607391Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7607501Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7607910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7608095Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7608203Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7608586Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7608695Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7609099Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7609215Z return prims._uniform_helper( 2024-12-18T00:10:34.7609304Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7609627Z x.shape, 2024-12-18T00:10:34.7609743Z ^^^^^^^^ 2024-12-18T00:10:34.7609832Z ...<4 lines>... 2024-12-18T00:10:34.7609942Z generator=generator, 2024-12-18T00:10:34.7610032Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7610127Z ) 2024-12-18T00:10:34.7610212Z ^ 2024-12-18T00:10:34.7610583Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7610700Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7610789Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7611213Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7611322Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7611709Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7611817Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7612206Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7612350Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7612454Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7612868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7613037Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7613153Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7613531Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7613631Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7614041Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7614147Z return prims._uniform_helper( 2024-12-18T00:10:34.7614245Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7614337Z x.shape, 2024-12-18T00:10:34.7614420Z ^^^^^^^^ 2024-12-18T00:10:34.7614514Z ...<4 lines>... 2024-12-18T00:10:34.7614612Z generator=generator, 2024-12-18T00:10:34.7614708Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7614793Z ) 2024-12-18T00:10:34.7614884Z ^ 2024-12-18T00:10:34.7615204Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7615307Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7615405Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7615824Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7615941Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7616312Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7616430Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7616821Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7616951Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7617059Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7617468Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7617642Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7617749Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7618140Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7618242Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7618646Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7618801Z return prims._uniform_helper( 2024-12-18T00:10:34.7618918Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7619013Z x.shape, 2024-12-18T00:10:34.7619103Z ^^^^^^^^ 2024-12-18T00:10:34.7619188Z ...<4 lines>... 2024-12-18T00:10:34.7619294Z generator=generator, 2024-12-18T00:10:34.7619382Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7619472Z ) 2024-12-18T00:10:34.7619556Z ^ 2024-12-18T00:10:34.7619934Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7620042Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7620131Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7620561Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7620669Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7621047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7621160Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7621557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7621685Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7621790Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7622210Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7622378Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7622492Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7622872Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7622980Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7623383Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7623495Z return prims._uniform_helper( 2024-12-18T00:10:34.7623594Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7623683Z x.shape, 2024-12-18T00:10:34.7623775Z ^^^^^^^^ 2024-12-18T00:10:34.7623865Z ...<4 lines>... 2024-12-18T00:10:34.7623962Z generator=generator, 2024-12-18T00:10:34.7624063Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7624150Z ) 2024-12-18T00:10:34.7624241Z ^ 2024-12-18T00:10:34.7624559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7624670Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7624762Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7625183Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7625301Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7625676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7625797Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7626190Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7626329Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7626435Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7626844Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7627020Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7627129Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7627522Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7627622Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7628029Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7628185Z return prims._uniform_helper( 2024-12-18T00:10:34.7628271Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7628371Z x.shape, 2024-12-18T00:10:34.7628455Z ^^^^^^^^ 2024-12-18T00:10:34.7628549Z ...<4 lines>... 2024-12-18T00:10:34.7628659Z generator=generator, 2024-12-18T00:10:34.7628794Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7628877Z ) 2024-12-18T00:10:34.7628964Z ^ 2024-12-18T00:10:34.7629280Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7629392Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7629482Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7629906Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7630017Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7630390Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7630509Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7630901Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7631040Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7631139Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7631555Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7631720Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7631826Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7632216Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7632312Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7632725Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7632833Z return prims._uniform_helper( 2024-12-18T00:10:34.7632933Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7633018Z x.shape, 2024-12-18T00:10:34.7633099Z ^^^^^^^^ 2024-12-18T00:10:34.7633197Z ...<4 lines>... 2024-12-18T00:10:34.7633298Z generator=generator, 2024-12-18T00:10:34.7633398Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7633476Z ) 2024-12-18T00:10:34.7633559Z ^ 2024-12-18T00:10:34.7633887Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7633988Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7634092Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7634506Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7634629Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7635001Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7635102Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7635499Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7635628Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7635737Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7636139Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7636319Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7636424Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7636810Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7636936Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7637363Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7637476Z return prims._uniform_helper( 2024-12-18T00:10:34.7637563Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7637656Z x.shape, 2024-12-18T00:10:34.7637787Z ^^^^^^^^ 2024-12-18T00:10:34.7637872Z ...<4 lines>... 2024-12-18T00:10:34.7637980Z generator=generator, 2024-12-18T00:10:34.7638065Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7638160Z ) 2024-12-18T00:10:34.7638239Z ^ 2024-12-18T00:10:34.7638632Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7638749Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7638838Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7639260Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7639372Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7639754Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7639857Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7640251Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7640393Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7640494Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7640911Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7641074Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7641188Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7641567Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7641679Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7642082Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7642186Z return prims._uniform_helper( 2024-12-18T00:10:34.7642285Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7642371Z x.shape, 2024-12-18T00:10:34.7642468Z ^^^^^^^^ 2024-12-18T00:10:34.7642552Z ...<4 lines>... 2024-12-18T00:10:34.7642652Z generator=generator, 2024-12-18T00:10:34.7642748Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7642828Z ) 2024-12-18T00:10:34.7642917Z ^ 2024-12-18T00:10:34.7643229Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7643332Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7643427Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7643839Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7643959Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7644330Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7644445Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7644839Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7644976Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7645075Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7645479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7645654Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7645757Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7646177Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7646305Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7646714Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7646818Z return prims._uniform_helper( 2024-12-18T00:10:34.7646963Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7647059Z x.shape, 2024-12-18T00:10:34.7647141Z ^^^^^^^^ 2024-12-18T00:10:34.7647236Z ...<4 lines>... 2024-12-18T00:10:34.7647332Z generator=generator, 2024-12-18T00:10:34.7647418Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7647514Z ) 2024-12-18T00:10:34.7647594Z ^ 2024-12-18T00:10:34.7647914Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7648015Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7648101Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7648531Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7648637Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7649018Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7649126Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7649528Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7649653Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7649761Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7650165Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7650328Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7650444Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7650824Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7650930Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7651328Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7651443Z return prims._uniform_helper( 2024-12-18T00:10:34.7651533Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7651614Z x.shape, 2024-12-18T00:10:34.7651700Z ^^^^^^^^ 2024-12-18T00:10:34.7651785Z ...<4 lines>... 2024-12-18T00:10:34.7651889Z generator=generator, 2024-12-18T00:10:34.7651973Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7652054Z ) 2024-12-18T00:10:34.7652144Z ^ 2024-12-18T00:10:34.7652458Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7652574Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7652662Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7653087Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7653196Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7653570Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7653686Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7654073Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7654211Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7654309Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7654727Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7654891Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7655043Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7655430Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7655526Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7655980Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7656086Z return prims._uniform_helper( 2024-12-18T00:10:34.7656182Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7656268Z x.shape, 2024-12-18T00:10:34.7656348Z ^^^^^^^^ 2024-12-18T00:10:34.7656444Z ...<4 lines>... 2024-12-18T00:10:34.7656539Z generator=generator, 2024-12-18T00:10:34.7656632Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7656711Z ) 2024-12-18T00:10:34.7656788Z ^ 2024-12-18T00:10:34.7657116Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7657217Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7657315Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7657731Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7657848Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7658227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7658338Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7658738Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7658865Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7658973Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7659379Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7659554Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7659661Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7660037Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7660145Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7660546Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7660657Z return prims._uniform_helper( 2024-12-18T00:10:34.7660742Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7660832Z x.shape, 2024-12-18T00:10:34.7660916Z ^^^^^^^^ 2024-12-18T00:10:34.7660998Z ...<4 lines>... 2024-12-18T00:10:34.7661105Z generator=generator, 2024-12-18T00:10:34.7661193Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7661284Z ) 2024-12-18T00:10:34.7661363Z ^ 2024-12-18T00:10:34.7661676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7661795Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7661885Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7662310Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7662416Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7662804Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7662911Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7663299Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7663439Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7663538Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7663955Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7664173Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7664290Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7664671Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7664769Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7665231Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7665334Z return prims._uniform_helper( 2024-12-18T00:10:34.7665430Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7665517Z x.shape, 2024-12-18T00:10:34.7665610Z ^^^^^^^^ 2024-12-18T00:10:34.7665696Z ...<4 lines>... 2024-12-18T00:10:34.7665793Z generator=generator, 2024-12-18T00:10:34.7665895Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7665978Z ) 2024-12-18T00:10:34.7666071Z ^ 2024-12-18T00:10:34.7666387Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7666494Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7666595Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7667008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7667126Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7667499Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7667610Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7668003Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7668133Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7668245Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7668650Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7668831Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7668934Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7669318Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7669418Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7669819Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7669936Z return prims._uniform_helper( 2024-12-18T00:10:34.7670021Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7670115Z x.shape, 2024-12-18T00:10:34.7670198Z ^^^^^^^^ 2024-12-18T00:10:34.7670294Z ...<4 lines>... 2024-12-18T00:10:34.7670392Z generator=generator, 2024-12-18T00:10:34.7670479Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7670572Z ) 2024-12-18T00:10:34.7670654Z ^ 2024-12-18T00:10:34.7670981Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7671080Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7671169Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7671602Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7671709Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7672096Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7672200Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7672598Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7672728Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7672827Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7673295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7673461Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7673758Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7674227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7674339Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7674741Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7674844Z return prims._uniform_helper( 2024-12-18T00:10:34.7674947Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7675033Z x.shape, 2024-12-18T00:10:34.7675129Z ^^^^^^^^ 2024-12-18T00:10:34.7675214Z ...<4 lines>... 2024-12-18T00:10:34.7675326Z generator=generator, 2024-12-18T00:10:34.7675415Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7675498Z ) 2024-12-18T00:10:34.7675595Z ^ 2024-12-18T00:10:34.7675911Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7676027Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7676115Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7676535Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7676654Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7677025Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7677140Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7677526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7677665Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7677766Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7678173Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7678414Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7678521Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7678915Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7679016Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7679434Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7679541Z return prims._uniform_helper( 2024-12-18T00:10:34.7679632Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7679734Z x.shape, 2024-12-18T00:10:34.7679817Z ^^^^^^^^ 2024-12-18T00:10:34.7679916Z ...<4 lines>... 2024-12-18T00:10:34.7680017Z generator=generator, 2024-12-18T00:10:34.7680119Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7680203Z ) 2024-12-18T00:10:34.7680284Z ^ 2024-12-18T00:10:34.7680613Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7680715Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7680819Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7681239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7681347Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7681731Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7681835Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7682233Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7682402Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7682556Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7682962Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7683125Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7683307Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7683685Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7683794Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7684193Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7684309Z return prims._uniform_helper( 2024-12-18T00:10:34.7684395Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7684477Z x.shape, 2024-12-18T00:10:34.7684566Z ^^^^^^^^ 2024-12-18T00:10:34.7684651Z ...<4 lines>... 2024-12-18T00:10:34.7684761Z generator=generator, 2024-12-18T00:10:34.7684844Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7684926Z ) 2024-12-18T00:10:34.7685017Z ^ 2024-12-18T00:10:34.7685335Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7685455Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7685543Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7685967Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7686072Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7686444Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7686559Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7686945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7687084Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7687183Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7687603Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7687773Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7687879Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7688267Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7688363Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7688775Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7688879Z return prims._uniform_helper( 2024-12-18T00:10:34.7688975Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7689061Z x.shape, 2024-12-18T00:10:34.7689144Z ^^^^^^^^ 2024-12-18T00:10:34.7689239Z ...<4 lines>... 2024-12-18T00:10:34.7689334Z generator=generator, 2024-12-18T00:10:34.7689433Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7689512Z ) 2024-12-18T00:10:34.7689593Z ^ 2024-12-18T00:10:34.7689918Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7690020Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7690117Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7690535Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7690653Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7691025Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7691132Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7691532Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7691711Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7691825Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7692226Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7692448Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7692557Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7692949Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7693048Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7693448Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7693566Z return prims._uniform_helper( 2024-12-18T00:10:34.7693651Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7693747Z x.shape, 2024-12-18T00:10:34.7693828Z ^^^^^^^^ 2024-12-18T00:10:34.7693914Z ...<4 lines>... 2024-12-18T00:10:34.7694022Z generator=generator, 2024-12-18T00:10:34.7694107Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7694199Z ) 2024-12-18T00:10:34.7694278Z ^ 2024-12-18T00:10:34.7694598Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7694708Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7694797Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7695231Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7695341Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7695725Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7695835Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7696241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7696373Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7696478Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7696897Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7697065Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7697184Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7697568Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7697678Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7698080Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7698189Z return prims._uniform_helper( 2024-12-18T00:10:34.7698293Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7698381Z x.shape, 2024-12-18T00:10:34.7698472Z ^^^^^^^^ 2024-12-18T00:10:34.7698561Z ...<4 lines>... 2024-12-18T00:10:34.7698661Z generator=generator, 2024-12-18T00:10:34.7698765Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7698850Z ) 2024-12-18T00:10:34.7698942Z ^ 2024-12-18T00:10:34.7699264Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7699378Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7699470Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7699893Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7700009Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7700383Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7700550Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7700944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7701082Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7701185Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7701641Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7701824Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7701933Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7702321Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7702420Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7702832Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7702941Z return prims._uniform_helper( 2024-12-18T00:10:34.7703030Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7703130Z x.shape, 2024-12-18T00:10:34.7703215Z ^^^^^^^^ 2024-12-18T00:10:34.7703312Z ...<4 lines>... 2024-12-18T00:10:34.7703410Z generator=generator, 2024-12-18T00:10:34.7703495Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7703590Z ) 2024-12-18T00:10:34.7703673Z ^ 2024-12-18T00:10:34.7704000Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7704104Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7704195Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7704624Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7704731Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7705112Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7705225Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7705622Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7705752Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7705868Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7706273Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7706446Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7706564Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7706944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7707052Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7707455Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7707573Z return prims._uniform_helper( 2024-12-18T00:10:34.7707663Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7707751Z x.shape, 2024-12-18T00:10:34.7707843Z ^^^^^^^^ 2024-12-18T00:10:34.7707931Z ...<4 lines>... 2024-12-18T00:10:34.7708037Z generator=generator, 2024-12-18T00:10:34.7708125Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7708211Z ) 2024-12-18T00:10:34.7708302Z ^ 2024-12-18T00:10:34.7708619Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7708726Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7708815Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7709244Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7709353Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7709758Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7709900Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7710290Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7710424Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7710575Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7710990Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7711158Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7711264Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7711659Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7711757Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7712170Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7712279Z return prims._uniform_helper( 2024-12-18T00:10:34.7712378Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7712467Z x.shape, 2024-12-18T00:10:34.7712554Z ^^^^^^^^ 2024-12-18T00:10:34.7712652Z ...<4 lines>... 2024-12-18T00:10:34.7712755Z generator=generator, 2024-12-18T00:10:34.7712851Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7712932Z ) 2024-12-18T00:10:34.7713014Z ^ 2024-12-18T00:10:34.7713343Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7713448Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7713546Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7713962Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7714080Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7714456Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7714562Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7714960Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7715093Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7715203Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7715608Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7715782Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7715890Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7716268Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7716377Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7716779Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7716894Z return prims._uniform_helper( 2024-12-18T00:10:34.7716984Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7717081Z x.shape, 2024-12-18T00:10:34.7717167Z ^^^^^^^^ 2024-12-18T00:10:34.7717257Z ...<4 lines>... 2024-12-18T00:10:34.7717365Z generator=generator, 2024-12-18T00:10:34.7717454Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7717546Z ) 2024-12-18T00:10:34.7717627Z ^ 2024-12-18T00:10:34.7717943Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7718052Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7718142Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7718652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7718842Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7719229Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7719335Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7719779Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7719922Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7720027Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7720445Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7720612Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7720730Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7721112Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7721217Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7721629Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7721736Z return prims._uniform_helper( 2024-12-18T00:10:34.7721834Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7721926Z x.shape, 2024-12-18T00:10:34.7722021Z ^^^^^^^^ 2024-12-18T00:10:34.7722108Z ...<4 lines>... 2024-12-18T00:10:34.7722207Z generator=generator, 2024-12-18T00:10:34.7722304Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7722383Z ) 2024-12-18T00:10:34.7722471Z ^ 2024-12-18T00:10:34.7722789Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7722894Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7722995Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7723419Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7723544Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7723922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7724037Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7724428Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7724554Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7724664Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7725069Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7725242Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7725348Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7725739Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7725840Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7726239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7726351Z return prims._uniform_helper( 2024-12-18T00:10:34.7726443Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7726537Z x.shape, 2024-12-18T00:10:34.7726621Z ^^^^^^^^ 2024-12-18T00:10:34.7726716Z ...<4 lines>... 2024-12-18T00:10:34.7726817Z generator=generator, 2024-12-18T00:10:34.7726905Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7726995Z ) 2024-12-18T00:10:34.7727078Z ^ 2024-12-18T00:10:34.7727401Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7727505Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7727595Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7728077Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7728186Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7728567Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7728722Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7729126Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7729256Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7729359Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7729779Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7729947Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7730067Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7730454Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7730559Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7730963Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7731076Z return prims._uniform_helper( 2024-12-18T00:10:34.7731176Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7731264Z x.shape, 2024-12-18T00:10:34.7731358Z ^^^^^^^^ 2024-12-18T00:10:34.7731448Z ...<4 lines>... 2024-12-18T00:10:34.7731559Z generator=generator, 2024-12-18T00:10:34.7731649Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7731734Z ) 2024-12-18T00:10:34.7731825Z ^ 2024-12-18T00:10:34.7732143Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7732260Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7732359Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7732779Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7732901Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7733280Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 286, in _prim_impl 2024-12-18T00:10:34.7733387Z meta(*args, **kwargs) 2024-12-18T00:10:34.7733478Z ~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7733879Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2741, in _uniform_meta 2024-12-18T00:10:34.7734034Z strides = utils.make_contiguous_strides_for(shape) 2024-12-18T00:10:34.7734510Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py", line 1622, in make_contiguous_strides_for 2024-12-18T00:10:34.7734626Z validate_shape(shape) 2024-12-18T00:10:34.7734718Z ~~~~~~~~~~~~~~^^^^^^^ 2024-12-18T00:10:34.7734880Z RecursionError: maximum recursion depth exceeded 2024-12-18T00:10:34.7734887Z 2024-12-18T00:10:34.7735079Z To execute this test, run the following from the base repo dir: 2024-12-18T00:10:34.7735369Z python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:10:34.7735374Z 2024-12-18T00:10:34.7735614Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:10:34.7735759Z =================================== FAILURES =================================== 2024-12-18T00:10:34.7735995Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:10:34.7736108Z Traceback (most recent call last): 2024-12-18T00:10:34.7736472Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in test_returning_symint 2024-12-18T00:10:34.7736620Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:10:34.7736731Z ~~~~~~~~~~^^^^^^ 2024-12-18T00:10:34.7737198Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7737373Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7737480Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7737944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7738054Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7738457Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7738573Z return prims._uniform_helper( 2024-12-18T00:10:34.7738666Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7738762Z x.shape, 2024-12-18T00:10:34.7738845Z ^^^^^^^^ 2024-12-18T00:10:34.7738929Z ...<4 lines>... 2024-12-18T00:10:34.7739035Z generator=generator, 2024-12-18T00:10:34.7739124Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7739220Z ) 2024-12-18T00:10:34.7739303Z ^ 2024-12-18T00:10:34.7739621Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7739737Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7739827Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7740257Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7740366Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7740751Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7740861Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7741250Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7741386Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7741493Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7741910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7742078Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7742188Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7742573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7742674Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7743081Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7743188Z return prims._uniform_helper( 2024-12-18T00:10:34.7743286Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7743374Z x.shape, 2024-12-18T00:10:34.7743465Z ^^^^^^^^ 2024-12-18T00:10:34.7743552Z ...<4 lines>... 2024-12-18T00:10:34.7743656Z generator=generator, 2024-12-18T00:10:34.7743752Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7743834Z ) 2024-12-18T00:10:34.7743924Z ^ 2024-12-18T00:10:34.7744242Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7744346Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7744446Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7744865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7744976Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7745351Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7745468Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7745858Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7746019Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7746154Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7746564Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7746737Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7749303Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7749713Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7749828Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7750236Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7750357Z return prims._uniform_helper( 2024-12-18T00:10:34.7750447Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7750549Z x.shape, 2024-12-18T00:10:34.7750635Z ^^^^^^^^ 2024-12-18T00:10:34.7750727Z ...<4 lines>... 2024-12-18T00:10:34.7750835Z generator=generator, 2024-12-18T00:10:34.7750925Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7751020Z ) 2024-12-18T00:10:34.7751100Z ^ 2024-12-18T00:10:34.7751418Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7751536Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7751670Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7752091Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7752215Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7752587Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7752709Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7753098Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7753233Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7753345Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7753753Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7753934Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7754042Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7754432Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7754532Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7754945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7755051Z return prims._uniform_helper( 2024-12-18T00:10:34.7755141Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7755235Z x.shape, 2024-12-18T00:10:34.7755319Z ^^^^^^^^ 2024-12-18T00:10:34.7755413Z ...<4 lines>... 2024-12-18T00:10:34.7755513Z generator=generator, 2024-12-18T00:10:34.7755599Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7755689Z ) 2024-12-18T00:10:34.7755770Z ^ 2024-12-18T00:10:34.7756099Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7756203Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7756289Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7756719Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7756830Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7757219Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7757324Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7757776Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7757938Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7758040Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7758575Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7758852Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7758976Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7759359Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7759469Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7759873Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7759991Z return prims._uniform_helper( 2024-12-18T00:10:34.7760083Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7760174Z x.shape, 2024-12-18T00:10:34.7760275Z ^^^^^^^^ 2024-12-18T00:10:34.7760362Z ...<4 lines>... 2024-12-18T00:10:34.7760474Z generator=generator, 2024-12-18T00:10:34.7760564Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7760643Z ) 2024-12-18T00:10:34.7760734Z ^ 2024-12-18T00:10:34.7761053Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7761169Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7761261Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7761682Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7761798Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7762171Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7762290Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7762683Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7762823Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7762927Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7763346Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7763510Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7763617Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7764011Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7764113Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7764522Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7764628Z return prims._uniform_helper( 2024-12-18T00:10:34.7764727Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7764816Z x.shape, 2024-12-18T00:10:34.7764900Z ^^^^^^^^ 2024-12-18T00:10:34.7764997Z ...<4 lines>... 2024-12-18T00:10:34.7765094Z generator=generator, 2024-12-18T00:10:34.7765201Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7765284Z ) 2024-12-18T00:10:34.7765379Z ^ 2024-12-18T00:10:34.7765698Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7765802Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7765905Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7766323Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7766444Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7766819Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7766995Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7767386Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7767517Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7767631Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7768101Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7768286Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7768397Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7768790Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7768892Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7769311Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7769423Z return prims._uniform_helper( 2024-12-18T00:10:34.7769516Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7769618Z x.shape, 2024-12-18T00:10:34.7769704Z ^^^^^^^^ 2024-12-18T00:10:34.7769802Z ...<4 lines>... 2024-12-18T00:10:34.7769903Z generator=generator, 2024-12-18T00:10:34.7769992Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7770092Z ) 2024-12-18T00:10:34.7770174Z ^ 2024-12-18T00:10:34.7770506Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7770609Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7770700Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7771133Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7771240Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7771624Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7771737Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7772139Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7772269Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7772373Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7772793Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7772960Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7773076Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7773457Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7773724Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7774128Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7774254Z return prims._uniform_helper( 2024-12-18T00:10:34.7774341Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7774427Z x.shape, 2024-12-18T00:10:34.7774523Z ^^^^^^^^ 2024-12-18T00:10:34.7774610Z ...<4 lines>... 2024-12-18T00:10:34.7774725Z generator=generator, 2024-12-18T00:10:34.7774811Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7774894Z ) 2024-12-18T00:10:34.7774987Z ^ 2024-12-18T00:10:34.7775305Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7775422Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7775509Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7775926Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7776045Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7776481Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7776637Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7777027Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7777206Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7777355Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7777780Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7777947Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7778054Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7778448Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7778549Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7778966Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7779072Z return prims._uniform_helper( 2024-12-18T00:10:34.7779176Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7779260Z x.shape, 2024-12-18T00:10:34.7779345Z ^^^^^^^^ 2024-12-18T00:10:34.7779446Z ...<4 lines>... 2024-12-18T00:10:34.7779550Z generator=generator, 2024-12-18T00:10:34.7779649Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7779729Z ) 2024-12-18T00:10:34.7779812Z ^ 2024-12-18T00:10:34.7780139Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7780244Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7780341Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7780756Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7780869Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7781257Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7781365Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7781764Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7781900Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7782011Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7782417Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7782595Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7782702Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7783083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7783197Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7783600Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7783718Z return prims._uniform_helper( 2024-12-18T00:10:34.7783805Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7783905Z x.shape, 2024-12-18T00:10:34.7783992Z ^^^^^^^^ 2024-12-18T00:10:34.7784085Z ...<4 lines>... 2024-12-18T00:10:34.7784195Z generator=generator, 2024-12-18T00:10:34.7784282Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7784377Z ) 2024-12-18T00:10:34.7784457Z ^ 2024-12-18T00:10:34.7784776Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7784892Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7784981Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7785409Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7785575Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7785961Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7786065Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7786519Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7786663Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7786767Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7787182Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7787350Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7787467Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7787844Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7787950Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7788365Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7788469Z return prims._uniform_helper( 2024-12-18T00:10:34.7788569Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7788658Z x.shape, 2024-12-18T00:10:34.7788757Z ^^^^^^^^ 2024-12-18T00:10:34.7788842Z ...<4 lines>... 2024-12-18T00:10:34.7788941Z generator=generator, 2024-12-18T00:10:34.7789038Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7789123Z ) 2024-12-18T00:10:34.7789217Z ^ 2024-12-18T00:10:34.7789530Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7789632Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7789732Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7790152Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7790270Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7790643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7790761Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7791150Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7791279Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7791392Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7791798Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7791979Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7792086Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7792483Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7792587Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7792992Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7793110Z return prims._uniform_helper( 2024-12-18T00:10:34.7793205Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7793304Z x.shape, 2024-12-18T00:10:34.7793390Z ^^^^^^^^ 2024-12-18T00:10:34.7793480Z ...<4 lines>... 2024-12-18T00:10:34.7793589Z generator=generator, 2024-12-18T00:10:34.7793679Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7793769Z ) 2024-12-18T00:10:34.7793850Z ^ 2024-12-18T00:10:34.7794178Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7794281Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7794372Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7794863Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7794973Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7795358Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7795490Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7795922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7796053Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7796157Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7796575Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7796743Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7796863Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7797248Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7797362Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7797766Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7797874Z return prims._uniform_helper( 2024-12-18T00:10:34.7797978Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7798065Z x.shape, 2024-12-18T00:10:34.7798163Z ^^^^^^^^ 2024-12-18T00:10:34.7798251Z ...<4 lines>... 2024-12-18T00:10:34.7798433Z generator=generator, 2024-12-18T00:10:34.7798536Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7798620Z ) 2024-12-18T00:10:34.7798714Z ^ 2024-12-18T00:10:34.7799034Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7799157Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7799254Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7799674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7799798Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7800176Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7800299Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7800691Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7800833Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7800938Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7801347Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7801527Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7801638Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7802035Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7802138Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7802559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7802666Z return prims._uniform_helper( 2024-12-18T00:10:34.7802756Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7802859Z x.shape, 2024-12-18T00:10:34.7802946Z ^^^^^^^^ 2024-12-18T00:10:34.7803045Z ...<4 lines>... 2024-12-18T00:10:34.7803144Z generator=generator, 2024-12-18T00:10:34.7803236Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7803334Z ) 2024-12-18T00:10:34.7803418Z ^ 2024-12-18T00:10:34.7803747Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7803911Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7804018Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7804439Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7804550Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7804989Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7805098Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7805503Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7805632Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7805750Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7806157Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7806328Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7806453Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7806837Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7806948Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7807349Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7807469Z return prims._uniform_helper( 2024-12-18T00:10:34.7807557Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7807645Z x.shape, 2024-12-18T00:10:34.7807738Z ^^^^^^^^ 2024-12-18T00:10:34.7807826Z ...<4 lines>... 2024-12-18T00:10:34.7807939Z generator=generator, 2024-12-18T00:10:34.7808026Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7808108Z ) 2024-12-18T00:10:34.7808203Z ^ 2024-12-18T00:10:34.7808520Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7808640Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7808730Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7809154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7809262Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7809636Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7809754Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7810146Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7810285Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7810386Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7810805Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7810977Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7811084Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7811477Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7811579Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7811994Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7812098Z return prims._uniform_helper( 2024-12-18T00:10:34.7812201Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7812288Z x.shape, 2024-12-18T00:10:34.7812374Z ^^^^^^^^ 2024-12-18T00:10:34.7812472Z ...<4 lines>... 2024-12-18T00:10:34.7812573Z generator=generator, 2024-12-18T00:10:34.7812672Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7812754Z ) 2024-12-18T00:10:34.7812872Z ^ 2024-12-18T00:10:34.7813200Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7813331Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7813434Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7813850Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7814023Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7814399Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7814505Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7814902Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7815035Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7815146Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7815557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7815738Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7815847Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7816227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7816340Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7816742Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7816863Z return prims._uniform_helper( 2024-12-18T00:10:34.7816954Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7817051Z x.shape, 2024-12-18T00:10:34.7817134Z ^^^^^^^^ 2024-12-18T00:10:34.7817223Z ...<4 lines>... 2024-12-18T00:10:34.7817333Z generator=generator, 2024-12-18T00:10:34.7817422Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7817518Z ) 2024-12-18T00:10:34.7817603Z ^ 2024-12-18T00:10:34.7817920Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7818034Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7818125Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7818555Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7818663Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7819047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7819152Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7819541Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7819680Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7819782Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7820201Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7820368Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7820486Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7820868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7820968Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7821386Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7821493Z return prims._uniform_helper( 2024-12-18T00:10:34.7821592Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7821679Z x.shape, 2024-12-18T00:10:34.7821779Z ^^^^^^^^ 2024-12-18T00:10:34.7821867Z ...<4 lines>... 2024-12-18T00:10:34.7821967Z generator=generator, 2024-12-18T00:10:34.7822094Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7822222Z ) 2024-12-18T00:10:34.7822316Z ^ 2024-12-18T00:10:34.7822634Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7822738Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7822839Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7823312Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7823433Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7823809Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7823929Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7824321Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7824451Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7824568Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7824974Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7825152Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7825257Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7825652Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7825751Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7826165Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7826270Z return prims._uniform_helper( 2024-12-18T00:10:34.7826360Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7826454Z x.shape, 2024-12-18T00:10:34.7826542Z ^^^^^^^^ 2024-12-18T00:10:34.7826641Z ...<4 lines>... 2024-12-18T00:10:34.7826741Z generator=generator, 2024-12-18T00:10:34.7826831Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7826926Z ) 2024-12-18T00:10:34.7827009Z ^ 2024-12-18T00:10:34.7827335Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7827439Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7827529Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7827960Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7828072Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7828461Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7828569Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7828968Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7829100Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7829205Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7829630Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7829799Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7829917Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7830299Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7830410Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7830810Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7830931Z return prims._uniform_helper( 2024-12-18T00:10:34.7831019Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7831107Z x.shape, 2024-12-18T00:10:34.7831233Z ^^^^^^^^ 2024-12-18T00:10:34.7831360Z ...<4 lines>... 2024-12-18T00:10:34.7831473Z generator=generator, 2024-12-18T00:10:34.7831560Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7831643Z ) 2024-12-18T00:10:34.7831736Z ^ 2024-12-18T00:10:34.7832054Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7832199Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7832316Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7832740Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7832861Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7833242Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7833361Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7833750Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7833895Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7834000Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7834420Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7834590Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7834702Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7835089Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7835190Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7835603Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7835711Z return prims._uniform_helper( 2024-12-18T00:10:34.7835813Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7835905Z x.shape, 2024-12-18T00:10:34.7835997Z ^^^^^^^^ 2024-12-18T00:10:34.7836097Z ...<4 lines>... 2024-12-18T00:10:34.7836195Z generator=generator, 2024-12-18T00:10:34.7836291Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7836376Z ) 2024-12-18T00:10:34.7836457Z ^ 2024-12-18T00:10:34.7836786Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7836892Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7836994Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7837411Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7837530Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7837906Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7838016Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7838509Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7838647Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7838758Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7839166Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7839348Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7839456Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7839842Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7839955Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7840356Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7840474Z return prims._uniform_helper( 2024-12-18T00:10:34.7840598Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7840724Z x.shape, 2024-12-18T00:10:34.7840807Z ^^^^^^^^ 2024-12-18T00:10:34.7840894Z ...<4 lines>... 2024-12-18T00:10:34.7841001Z generator=generator, 2024-12-18T00:10:34.7841091Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7841176Z ) 2024-12-18T00:10:34.7841259Z ^ 2024-12-18T00:10:34.7841627Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7841739Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7841829Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7842254Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7842363Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7842745Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7842854Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7843248Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7843384Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7843488Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7843907Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7844076Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7844199Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7844582Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7844683Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7845094Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7845203Z return prims._uniform_helper( 2024-12-18T00:10:34.7845299Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7845386Z x.shape, 2024-12-18T00:10:34.7845484Z ^^^^^^^^ 2024-12-18T00:10:34.7845572Z ...<4 lines>... 2024-12-18T00:10:34.7845672Z generator=generator, 2024-12-18T00:10:34.7845767Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7845851Z ) 2024-12-18T00:10:34.7845939Z ^ 2024-12-18T00:10:34.7846258Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7846363Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7846463Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7846880Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7846997Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7847372Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7847492Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7847884Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7848018Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7848129Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7848539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7848714Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7848822Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7849210Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7849310Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7849713Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7849883Z return prims._uniform_helper( 2024-12-18T00:10:34.7849971Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7850062Z x.shape, 2024-12-18T00:10:34.7850147Z ^^^^^^^^ 2024-12-18T00:10:34.7850233Z ...<4 lines>... 2024-12-18T00:10:34.7850339Z generator=generator, 2024-12-18T00:10:34.7850429Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7850547Z ) 2024-12-18T00:10:34.7850658Z ^ 2024-12-18T00:10:34.7850989Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7851095Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7851184Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7851613Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7851723Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7852107Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7852218Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7852617Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7852745Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7852850Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7853268Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7853434Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7853548Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7853928Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7854036Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7854437Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7854547Z return prims._uniform_helper( 2024-12-18T00:10:34.7854643Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7854730Z x.shape, 2024-12-18T00:10:34.7854822Z ^^^^^^^^ 2024-12-18T00:10:34.7854912Z ...<4 lines>... 2024-12-18T00:10:34.7855010Z generator=generator, 2024-12-18T00:10:34.7855110Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7855193Z ) 2024-12-18T00:10:34.7855281Z ^ 2024-12-18T00:10:34.7855600Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7855710Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7855802Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7856219Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7856334Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7856708Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7856826Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7857218Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7857359Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7857462Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7857870Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7858045Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7858156Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7858543Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7858648Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7859130Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7859238Z return prims._uniform_helper( 2024-12-18T00:10:34.7859327Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7859421Z x.shape, 2024-12-18T00:10:34.7859507Z ^^^^^^^^ 2024-12-18T00:10:34.7859607Z ...<4 lines>... 2024-12-18T00:10:34.7859758Z generator=generator, 2024-12-18T00:10:34.7859846Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7859942Z ) 2024-12-18T00:10:34.7860023Z ^ 2024-12-18T00:10:34.7860350Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7860456Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7860553Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7860973Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7861081Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7861465Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7861576Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7861976Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7862110Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7862225Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7862635Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7862802Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7862920Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7863302Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7863413Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7863818Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7863934Z return prims._uniform_helper( 2024-12-18T00:10:34.7864024Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7864111Z x.shape, 2024-12-18T00:10:34.7864206Z ^^^^^^^^ 2024-12-18T00:10:34.7864297Z ...<4 lines>... 2024-12-18T00:10:34.7864402Z generator=generator, 2024-12-18T00:10:34.7864491Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7864572Z ) 2024-12-18T00:10:34.7868843Z ^ 2024-12-18T00:10:34.7869212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7869329Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7869420Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7869850Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7869986Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7870365Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7870485Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7870887Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7871032Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7871135Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7871545Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7871725Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7871833Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7872223Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7872430Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7872846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7872955Z return prims._uniform_helper( 2024-12-18T00:10:34.7873058Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7873180Z x.shape, 2024-12-18T00:10:34.7873300Z ^^^^^^^^ 2024-12-18T00:10:34.7873398Z ...<4 lines>... 2024-12-18T00:10:34.7873496Z generator=generator, 2024-12-18T00:10:34.7873824Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7873908Z ) 2024-12-18T00:10:34.7873990Z ^ 2024-12-18T00:10:34.7874325Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7874432Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7874532Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7874951Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7875065Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7875452Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7875561Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7875968Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7876100Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7876213Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7876619Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7876797Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7876908Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7877295Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7877410Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7877816Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7877932Z return prims._uniform_helper( 2024-12-18T00:10:34.7878024Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7878112Z x.shape, 2024-12-18T00:10:34.7878205Z ^^^^^^^^ 2024-12-18T00:10:34.7878290Z ...<4 lines>... 2024-12-18T00:10:34.7878461Z generator=generator, 2024-12-18T00:10:34.7878550Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7878640Z ) 2024-12-18T00:10:34.7878722Z ^ 2024-12-18T00:10:34.7879041Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7879153Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7879243Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7879674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7879784Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7880159Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7880277Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7880672Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7880814Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7880918Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7881332Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7881501Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7881621Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7882110Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7882210Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7882619Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7882763Z return prims._uniform_helper( 2024-12-18T00:10:34.7882900Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7882989Z x.shape, 2024-12-18T00:10:34.7883074Z ^^^^^^^^ 2024-12-18T00:10:34.7883172Z ...<4 lines>... 2024-12-18T00:10:34.7883273Z generator=generator, 2024-12-18T00:10:34.7883370Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7883456Z ) 2024-12-18T00:10:34.7883545Z ^ 2024-12-18T00:10:34.7883865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7883972Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7884079Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7884502Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7884621Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7884999Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7885122Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7885514Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7885647Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7885761Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7886166Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7886340Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7886452Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7886844Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7886944Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7887349Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7887463Z return prims._uniform_helper( 2024-12-18T00:10:34.7887554Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7887650Z x.shape, 2024-12-18T00:10:34.7887732Z ^^^^^^^^ 2024-12-18T00:10:34.7887819Z ...<4 lines>... 2024-12-18T00:10:34.7887930Z generator=generator, 2024-12-18T00:10:34.7888020Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7888113Z ) 2024-12-18T00:10:34.7888197Z ^ 2024-12-18T00:10:34.7888523Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7888632Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7888721Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7889147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7889255Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7889643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7889751Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7890147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7890277Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7890384Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7890801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7890996Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7891143Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7891528Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7891637Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7892103Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7892213Z return prims._uniform_helper( 2024-12-18T00:10:34.7892310Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7892398Z x.shape, 2024-12-18T00:10:34.7892489Z ^^^^^^^^ 2024-12-18T00:10:34.7892577Z ...<4 lines>... 2024-12-18T00:10:34.7892674Z generator=generator, 2024-12-18T00:10:34.7892771Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7892857Z ) 2024-12-18T00:10:34.7892948Z ^ 2024-12-18T00:10:34.7893268Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7893389Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7893483Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7893901Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7894024Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7894406Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7894520Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7894913Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7895052Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7895154Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7895563Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7895743Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7895849Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7896242Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7896343Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7896754Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7896862Z return prims._uniform_helper( 2024-12-18T00:10:34.7896953Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7897049Z x.shape, 2024-12-18T00:10:34.7897132Z ^^^^^^^^ 2024-12-18T00:10:34.7897225Z ...<4 lines>... 2024-12-18T00:10:34.7897322Z generator=generator, 2024-12-18T00:10:34.7897409Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7897502Z ) 2024-12-18T00:10:34.7897586Z ^ 2024-12-18T00:10:34.7897913Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7898019Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7898118Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7898538Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7898651Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7899036Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7899143Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7899546Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7899675Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7899787Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7900195Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7900485Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7900605Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7901017Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7901153Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7901559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7901675Z return prims._uniform_helper( 2024-12-18T00:10:34.7901766Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7901854Z x.shape, 2024-12-18T00:10:34.7901949Z ^^^^^^^^ 2024-12-18T00:10:34.7902036Z ...<4 lines>... 2024-12-18T00:10:34.7902144Z generator=generator, 2024-12-18T00:10:34.7902235Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7902316Z ) 2024-12-18T00:10:34.7902408Z ^ 2024-12-18T00:10:34.7902727Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7902838Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7902928Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7903361Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7903469Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7903842Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7903961Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7904350Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7904489Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7904593Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7905014Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7905183Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7905291Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7905684Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7905785Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7906195Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7906302Z return prims._uniform_helper( 2024-12-18T00:10:34.7906402Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7906490Z x.shape, 2024-12-18T00:10:34.7906575Z ^^^^^^^^ 2024-12-18T00:10:34.7906671Z ...<4 lines>... 2024-12-18T00:10:34.7906769Z generator=generator, 2024-12-18T00:10:34.7906865Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7906951Z ) 2024-12-18T00:10:34.7907030Z ^ 2024-12-18T00:10:34.7907352Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7907455Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7907556Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7907981Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7908099Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7908475Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7908584Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7908982Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7909112Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7909254Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7909688Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7909864Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7909973Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7910421Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7910535Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7910940Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7911057Z return prims._uniform_helper( 2024-12-18T00:10:34.7911151Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7911242Z x.shape, 2024-12-18T00:10:34.7911326Z ^^^^^^^^ 2024-12-18T00:10:34.7911415Z ...<4 lines>... 2024-12-18T00:10:34.7911526Z generator=generator, 2024-12-18T00:10:34.7911617Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7911708Z ) 2024-12-18T00:10:34.7911790Z ^ 2024-12-18T00:10:34.7912109Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7912220Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7912328Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7912749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7912867Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7913241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7913348Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7913750Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7913882Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7913993Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7914401Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7914577Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7914687Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7915068Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7915178Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7915582Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7915699Z return prims._uniform_helper( 2024-12-18T00:10:34.7915790Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7915884Z x.shape, 2024-12-18T00:10:34.7915967Z ^^^^^^^^ 2024-12-18T00:10:34.7916056Z ...<4 lines>... 2024-12-18T00:10:34.7916166Z generator=generator, 2024-12-18T00:10:34.7916257Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7916347Z ) 2024-12-18T00:10:34.7916431Z ^ 2024-12-18T00:10:34.7916746Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7916862Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7916955Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7917381Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7917489Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7917869Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7917976Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7918453Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7918656Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7918761Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7919179Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7919371Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7919515Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7919898Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7919999Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7920415Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7920519Z return prims._uniform_helper( 2024-12-18T00:10:34.7920618Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7920708Z x.shape, 2024-12-18T00:10:34.7920806Z ^^^^^^^^ 2024-12-18T00:10:34.7920891Z ...<4 lines>... 2024-12-18T00:10:34.7920989Z generator=generator, 2024-12-18T00:10:34.7921086Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7921168Z ) 2024-12-18T00:10:34.7921257Z ^ 2024-12-18T00:10:34.7921580Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7921699Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7921837Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7922346Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7922464Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7922837Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7922954Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7923351Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7923483Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7923597Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7924002Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7924184Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7924291Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7924681Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7924782Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7925185Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7925296Z return prims._uniform_helper( 2024-12-18T00:10:34.7925392Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7925489Z x.shape, 2024-12-18T00:10:34.7925575Z ^^^^^^^^ 2024-12-18T00:10:34.7925669Z ...<4 lines>... 2024-12-18T00:10:34.7925767Z generator=generator, 2024-12-18T00:10:34.7925856Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7925950Z ) 2024-12-18T00:10:34.7926031Z ^ 2024-12-18T00:10:34.7926361Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7926468Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7926555Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7926983Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7927091Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7927473Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7927580Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7928049Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7928181Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7928288Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7928747Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7928943Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7929060Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7929443Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7929550Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7929953Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7930062Z return prims._uniform_helper( 2024-12-18T00:10:34.7930162Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7930248Z x.shape, 2024-12-18T00:10:34.7930342Z ^^^^^^^^ 2024-12-18T00:10:34.7930431Z ...<4 lines>... 2024-12-18T00:10:34.7930536Z generator=generator, 2024-12-18T00:10:34.7930625Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7930709Z ) 2024-12-18T00:10:34.7930802Z ^ 2024-12-18T00:10:34.7931122Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7931235Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7931324Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7931739Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7931860Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7932232Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7932351Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7932741Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7932879Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7932980Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7933390Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7933567Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7933676Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7934065Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7934165Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7934571Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7934680Z return prims._uniform_helper( 2024-12-18T00:10:34.7934770Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7934868Z x.shape, 2024-12-18T00:10:34.7934953Z ^^^^^^^^ 2024-12-18T00:10:34.7935049Z ...<4 lines>... 2024-12-18T00:10:34.7935149Z generator=generator, 2024-12-18T00:10:34.7935244Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7935330Z ) 2024-12-18T00:10:34.7935413Z ^ 2024-12-18T00:10:34.7935739Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7935842Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7935942Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7936357Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7936464Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7936846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7937016Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7937404Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7937539Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7937663Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7938108Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7938277Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7938392Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7938774Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7938872Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7939282Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7939388Z return prims._uniform_helper( 2024-12-18T00:10:34.7939484Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7939569Z x.shape, 2024-12-18T00:10:34.7939660Z ^^^^^^^^ 2024-12-18T00:10:34.7939748Z ...<4 lines>... 2024-12-18T00:10:34.7939844Z generator=generator, 2024-12-18T00:10:34.7939942Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7940022Z ) 2024-12-18T00:10:34.7940115Z ^ 2024-12-18T00:10:34.7940431Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7940533Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7940637Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7941052Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7941169Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7941545Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7941665Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7942056Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7942185Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7942297Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7942702Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7942878Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7942982Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7943369Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7943468Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7943870Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7943989Z return prims._uniform_helper( 2024-12-18T00:10:34.7944074Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7944172Z x.shape, 2024-12-18T00:10:34.7944250Z ^^^^^^^^ 2024-12-18T00:10:34.7944335Z ...<4 lines>... 2024-12-18T00:10:34.7944447Z generator=generator, 2024-12-18T00:10:34.7944531Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7944621Z ) 2024-12-18T00:10:34.7944699Z ^ 2024-12-18T00:10:34.7945029Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7945129Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7945218Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7945647Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7945779Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7946188Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7946292Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7946686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7946863Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7946965Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7947382Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7947544Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7947660Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7948037Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7948149Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7948550Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7948652Z return prims._uniform_helper( 2024-12-18T00:10:34.7948748Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7948829Z x.shape, 2024-12-18T00:10:34.7948921Z ^^^^^^^^ 2024-12-18T00:10:34.7949007Z ...<4 lines>... 2024-12-18T00:10:34.7949102Z generator=generator, 2024-12-18T00:10:34.7949197Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7949276Z ) 2024-12-18T00:10:34.7949368Z ^ 2024-12-18T00:10:34.7949681Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7949796Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7949884Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7950296Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7950415Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7950786Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7950900Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7951289Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7951426Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7951528Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7951929Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7952106Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7952208Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7952595Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7952695Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7953104Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7953209Z return prims._uniform_helper( 2024-12-18T00:10:34.7953294Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7953392Z x.shape, 2024-12-18T00:10:34.7953472Z ^^^^^^^^ 2024-12-18T00:10:34.7953566Z ...<4 lines>... 2024-12-18T00:10:34.7953661Z generator=generator, 2024-12-18T00:10:34.7953746Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7953837Z ) 2024-12-18T00:10:34.7953916Z ^ 2024-12-18T00:10:34.7954242Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7954343Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7954442Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7954858Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7955018Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7955403Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7955508Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7955955Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7956081Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7956191Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7956600Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7956763Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7956877Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7957255Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7957367Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7957767Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7957878Z return prims._uniform_helper( 2024-12-18T00:10:34.7957967Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7958051Z x.shape, 2024-12-18T00:10:34.7958144Z ^^^^^^^^ 2024-12-18T00:10:34.7958227Z ...<4 lines>... 2024-12-18T00:10:34.7958424Z generator=generator, 2024-12-18T00:10:34.7958514Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7958594Z ) 2024-12-18T00:10:34.7958686Z ^ 2024-12-18T00:10:34.7959003Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7959116Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7959202Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7959634Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7959738Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7960109Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7960228Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7960617Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7960753Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7960850Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7961265Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7961431Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7961535Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7961930Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7962025Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7962435Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7962537Z return prims._uniform_helper( 2024-12-18T00:10:34.7962633Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7962719Z x.shape, 2024-12-18T00:10:34.7962798Z ^^^^^^^^ 2024-12-18T00:10:34.7962893Z ...<4 lines>... 2024-12-18T00:10:34.7962995Z generator=generator, 2024-12-18T00:10:34.7963090Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7963167Z ) 2024-12-18T00:10:34.7963247Z ^ 2024-12-18T00:10:34.7963571Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7963672Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7963844Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7964258Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7964374Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7964776Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7964906Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7965309Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7965434Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7965543Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7965946Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7966120Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7966229Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7966605Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7966715Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7967116Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7967231Z return prims._uniform_helper( 2024-12-18T00:10:34.7967318Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7967413Z x.shape, 2024-12-18T00:10:34.7967494Z ^^^^^^^^ 2024-12-18T00:10:34.7967580Z ...<4 lines>... 2024-12-18T00:10:34.7967689Z generator=generator, 2024-12-18T00:10:34.7967773Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7967863Z ) 2024-12-18T00:10:34.7967940Z ^ 2024-12-18T00:10:34.7968255Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7968368Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7968456Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7968882Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7968988Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7969373Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7969476Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7969865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7970003Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7970101Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7970520Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7970687Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7970803Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7971181Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7971281Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7971699Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7971801Z return prims._uniform_helper( 2024-12-18T00:10:34.7971899Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7971984Z x.shape, 2024-12-18T00:10:34.7972076Z ^^^^^^^^ 2024-12-18T00:10:34.7972159Z ...<4 lines>... 2024-12-18T00:10:34.7972257Z generator=generator, 2024-12-18T00:10:34.7972354Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7972435Z ) 2024-12-18T00:10:34.7972526Z ^ 2024-12-18T00:10:34.7972842Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7972995Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7973090Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7973506Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7973796Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7974268Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7974386Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7974778Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7974903Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7975016Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7975419Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7975602Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7975707Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7976097Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7976196Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7976607Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7976714Z return prims._uniform_helper( 2024-12-18T00:10:34.7976799Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7976895Z x.shape, 2024-12-18T00:10:34.7976977Z ^^^^^^^^ 2024-12-18T00:10:34.7977073Z ...<4 lines>... 2024-12-18T00:10:34.7977170Z generator=generator, 2024-12-18T00:10:34.7977260Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7977351Z ) 2024-12-18T00:10:34.7977429Z ^ 2024-12-18T00:10:34.7977761Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7977863Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7977952Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7978377Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7978487Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7978874Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7978977Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7979373Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7979503Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7979601Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7980013Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7980182Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7980299Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7980675Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7980788Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7981191Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7981302Z return prims._uniform_helper( 2024-12-18T00:10:34.7981391Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7981473Z x.shape, 2024-12-18T00:10:34.7981565Z ^^^^^^^^ 2024-12-18T00:10:34.7981645Z ...<4 lines>... 2024-12-18T00:10:34.7981749Z generator=generator, 2024-12-18T00:10:34.7981831Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7981948Z ) 2024-12-18T00:10:34.7982087Z ^ 2024-12-18T00:10:34.7982402Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7982511Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7982597Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7983038Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7983182Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7983555Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7983671Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7984054Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7984192Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7984292Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7984705Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7984872Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7984974Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7985363Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7985460Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7985863Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7985967Z return prims._uniform_helper( 2024-12-18T00:10:34.7986061Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7986145Z x.shape, 2024-12-18T00:10:34.7986226Z ^^^^^^^^ 2024-12-18T00:10:34.7986318Z ...<4 lines>... 2024-12-18T00:10:34.7986413Z generator=generator, 2024-12-18T00:10:34.7986508Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7986591Z ) 2024-12-18T00:10:34.7986668Z ^ 2024-12-18T00:10:34.7986993Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7987096Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7987192Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7987611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7987729Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7988105Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7988211Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7988610Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7988735Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7988847Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7989253Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7989428Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7989535Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7989913Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7990021Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7990417Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7990531Z return prims._uniform_helper( 2024-12-18T00:10:34.7990616Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7990707Z x.shape, 2024-12-18T00:10:34.7990789Z ^^^^^^^^ 2024-12-18T00:10:34.7990873Z ...<4 lines>... 2024-12-18T00:10:34.7991011Z generator=generator, 2024-12-18T00:10:34.7991117Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7991209Z ) 2024-12-18T00:10:34.7991288Z ^ 2024-12-18T00:10:34.7991603Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7991715Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7991827Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7992279Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7992386Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7992767Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7992871Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7993256Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7993395Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7993497Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7993910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7994074Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7994191Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7994573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7994669Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7995083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7995182Z return prims._uniform_helper( 2024-12-18T00:10:34.7995281Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.7995364Z x.shape, 2024-12-18T00:10:34.7995460Z ^^^^^^^^ 2024-12-18T00:10:34.7995548Z ...<4 lines>... 2024-12-18T00:10:34.7995645Z generator=generator, 2024-12-18T00:10:34.7995743Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7995819Z ) 2024-12-18T00:10:34.7995909Z ^ 2024-12-18T00:10:34.7996221Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.7996328Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.7996427Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7996843Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.7996963Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.7997333Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.7997447Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.7997837Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.7997966Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.7998079Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7998559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.7998740Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.7998845Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.7999231Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.7999329Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.7999729Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.7999844Z return prims._uniform_helper( 2024-12-18T00:10:34.7999928Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8000071Z x.shape, 2024-12-18T00:10:34.8000177Z ^^^^^^^^ 2024-12-18T00:10:34.8000263Z ...<4 lines>... 2024-12-18T00:10:34.8000371Z generator=generator, 2024-12-18T00:10:34.8000453Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8000547Z ) 2024-12-18T00:10:34.8000625Z ^ 2024-12-18T00:10:34.8000974Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8001104Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8001191Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8001621Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8001726Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8002105Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8002210Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8002607Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8002739Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8002837Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8003253Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8003417Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8003531Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8003908Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8004019Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8004423Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8004531Z return prims._uniform_helper( 2024-12-18T00:10:34.8004631Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8004723Z x.shape, 2024-12-18T00:10:34.8004816Z ^^^^^^^^ 2024-12-18T00:10:34.8004904Z ...<4 lines>... 2024-12-18T00:10:34.8005001Z generator=generator, 2024-12-18T00:10:34.8005099Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8005184Z ) 2024-12-18T00:10:34.8005277Z ^ 2024-12-18T00:10:34.8005599Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8005713Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8005804Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8006221Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8006341Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8006716Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8006833Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8007227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8007367Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8007474Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8007884Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8008064Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8008172Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8008564Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8008668Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8009078Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8009213Z return prims._uniform_helper( 2024-12-18T00:10:34.8009325Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8009422Z x.shape, 2024-12-18T00:10:34.8009508Z ^^^^^^^^ 2024-12-18T00:10:34.8009601Z ...<4 lines>... 2024-12-18T00:10:34.8009702Z generator=generator, 2024-12-18T00:10:34.8009791Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8009889Z ) 2024-12-18T00:10:34.8009971Z ^ 2024-12-18T00:10:34.8010348Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8010454Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8010554Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8010972Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8011079Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8011463Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8011574Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8011976Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8012107Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8012218Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8012628Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8012794Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8012911Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8013292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8013400Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8013801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8013916Z return prims._uniform_helper( 2024-12-18T00:10:34.8014003Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8014090Z x.shape, 2024-12-18T00:10:34.8014183Z ^^^^^^^^ 2024-12-18T00:10:34.8014273Z ...<4 lines>... 2024-12-18T00:10:34.8014376Z generator=generator, 2024-12-18T00:10:34.8014464Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8014549Z ) 2024-12-18T00:10:34.8014642Z ^ 2024-12-18T00:10:34.8014959Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8015069Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8015159Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8015585Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8015696Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8016071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8016193Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8016585Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8016725Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8016834Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8017251Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8017417Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8017525Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8017913Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8018014Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8018429Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8018588Z return prims._uniform_helper( 2024-12-18T00:10:34.8018686Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8018774Z x.shape, 2024-12-18T00:10:34.8018858Z ^^^^^^^^ 2024-12-18T00:10:34.8018956Z ...<4 lines>... 2024-12-18T00:10:34.8019061Z generator=generator, 2024-12-18T00:10:34.8019183Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8019294Z ) 2024-12-18T00:10:34.8019374Z ^ 2024-12-18T00:10:34.8019701Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8019803Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8019901Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8020321Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8020438Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8020816Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8020925Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8021329Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8021461Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8021573Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8021981Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8022158Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8022265Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8022645Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8022752Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8023159Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8023274Z return prims._uniform_helper( 2024-12-18T00:10:34.8023362Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8023459Z x.shape, 2024-12-18T00:10:34.8023544Z ^^^^^^^^ 2024-12-18T00:10:34.8023630Z ...<4 lines>... 2024-12-18T00:10:34.8023738Z generator=generator, 2024-12-18T00:10:34.8023829Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8023919Z ) 2024-12-18T00:10:34.8024001Z ^ 2024-12-18T00:10:34.8024314Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8024425Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8024512Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8024937Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8025046Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8025430Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8025536Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8025922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8026064Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8026166Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8026581Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8026745Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8026857Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8027240Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8027369Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8027808Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8027915Z return prims._uniform_helper( 2024-12-18T00:10:34.8028015Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8028103Z x.shape, 2024-12-18T00:10:34.8028223Z ^^^^^^^^ 2024-12-18T00:10:34.8028310Z ...<4 lines>... 2024-12-18T00:10:34.8028446Z generator=generator, 2024-12-18T00:10:34.8028542Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8028625Z ) 2024-12-18T00:10:34.8028716Z ^ 2024-12-18T00:10:34.8029034Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8029138Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8029240Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8029657Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8029779Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8030155Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8030272Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8030666Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8030797Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8030907Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8031315Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8031488Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8031596Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8031986Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8032090Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8032501Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8032606Z return prims._uniform_helper( 2024-12-18T00:10:34.8032698Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8032794Z x.shape, 2024-12-18T00:10:34.8032880Z ^^^^^^^^ 2024-12-18T00:10:34.8032972Z ...<4 lines>... 2024-12-18T00:10:34.8033072Z generator=generator, 2024-12-18T00:10:34.8033160Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8033248Z ) 2024-12-18T00:10:34.8033332Z ^ 2024-12-18T00:10:34.8033658Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8033761Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8033848Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8034273Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8034386Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8034772Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8034879Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8035281Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8035411Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8035514Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8035930Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8036097Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8036213Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8036631Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8036768Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8037170Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8037288Z return prims._uniform_helper( 2024-12-18T00:10:34.8037402Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8037573Z x.shape, 2024-12-18T00:10:34.8037666Z ^^^^^^^^ 2024-12-18T00:10:34.8037756Z ...<4 lines>... 2024-12-18T00:10:34.8037864Z generator=generator, 2024-12-18T00:10:34.8037955Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8038037Z ) 2024-12-18T00:10:34.8038131Z ^ 2024-12-18T00:10:34.8038532Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8038650Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8038744Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8039165Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8039289Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8039667Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8039789Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8040184Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8040324Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8040429Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8040846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8041016Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8041125Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8041525Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8041628Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8042040Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8042151Z return prims._uniform_helper( 2024-12-18T00:10:34.8042254Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8042344Z x.shape, 2024-12-18T00:10:34.8042429Z ^^^^^^^^ 2024-12-18T00:10:34.8042526Z ...<4 lines>... 2024-12-18T00:10:34.8042625Z generator=generator, 2024-12-18T00:10:34.8042724Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8042810Z ) 2024-12-18T00:10:34.8042895Z ^ 2024-12-18T00:10:34.8043224Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8043330Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8043433Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8043851Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8043967Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8044346Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8044450Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8044851Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8044979Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8045092Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8045498Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8045673Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8045839Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8046218Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8046328Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8046756Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8046897Z return prims._uniform_helper( 2024-12-18T00:10:34.8046988Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8047083Z x.shape, 2024-12-18T00:10:34.8047167Z ^^^^^^^^ 2024-12-18T00:10:34.8047254Z ...<4 lines>... 2024-12-18T00:10:34.8047362Z generator=generator, 2024-12-18T00:10:34.8047454Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8047548Z ) 2024-12-18T00:10:34.8047631Z ^ 2024-12-18T00:10:34.8047948Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8048066Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8048160Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8048585Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8048695Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8049087Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8049195Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8049582Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8049727Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8049831Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8050248Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8050416Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8050535Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8050915Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8051013Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8051431Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8051536Z return prims._uniform_helper( 2024-12-18T00:10:34.8051636Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8051724Z x.shape, 2024-12-18T00:10:34.8051818Z ^^^^^^^^ 2024-12-18T00:10:34.8051905Z ...<4 lines>... 2024-12-18T00:10:34.8052005Z generator=generator, 2024-12-18T00:10:34.8052103Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8052186Z ) 2024-12-18T00:10:34.8052276Z ^ 2024-12-18T00:10:34.8052593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8052700Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8052801Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8053220Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8053336Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8053712Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8053828Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8054222Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8054350Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8054462Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8054868Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8055097Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8055206Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8055591Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8055690Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8056147Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8056269Z return prims._uniform_helper( 2024-12-18T00:10:34.8056360Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8056461Z x.shape, 2024-12-18T00:10:34.8056544Z ^^^^^^^^ 2024-12-18T00:10:34.8056629Z ...<4 lines>... 2024-12-18T00:10:34.8056740Z generator=generator, 2024-12-18T00:10:34.8056829Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8056927Z ) 2024-12-18T00:10:34.8057008Z ^ 2024-12-18T00:10:34.8057335Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8057442Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8057531Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8057960Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8058074Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8058459Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8058566Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8058964Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8059092Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8059196Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8059611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8059783Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8059900Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8060280Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8060396Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8060796Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8060902Z return prims._uniform_helper( 2024-12-18T00:10:34.8061002Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8061091Z x.shape, 2024-12-18T00:10:34.8061185Z ^^^^^^^^ 2024-12-18T00:10:34.8061273Z ...<4 lines>... 2024-12-18T00:10:34.8061372Z generator=generator, 2024-12-18T00:10:34.8061470Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8061554Z ) 2024-12-18T00:10:34.8061647Z ^ 2024-12-18T00:10:34.8061962Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8062074Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8062162Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8062581Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8062697Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8063073Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8063192Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8063581Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8063719Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8063822Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8064286Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8064463Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8064571Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8065024Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8065126Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8065541Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8065648Z return prims._uniform_helper( 2024-12-18T00:10:34.8065738Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8065836Z x.shape, 2024-12-18T00:10:34.8065922Z ^^^^^^^^ 2024-12-18T00:10:34.8066019Z ...<4 lines>... 2024-12-18T00:10:34.8066118Z generator=generator, 2024-12-18T00:10:34.8066208Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8066303Z ) 2024-12-18T00:10:34.8066385Z ^ 2024-12-18T00:10:34.8066710Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8066818Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8066924Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8067347Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8067456Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8067840Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8067947Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8068346Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8068475Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8068590Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8068998Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8069164Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8069282Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8069665Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8069773Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8070175Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8070289Z return prims._uniform_helper( 2024-12-18T00:10:34.8070378Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8070463Z x.shape, 2024-12-18T00:10:34.8070558Z ^^^^^^^^ 2024-12-18T00:10:34.8070646Z ...<4 lines>... 2024-12-18T00:10:34.8070756Z generator=generator, 2024-12-18T00:10:34.8070845Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8070940Z ) 2024-12-18T00:10:34.8071036Z ^ 2024-12-18T00:10:34.8071353Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8071472Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8071568Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8072008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8072117Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8072494Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8072615Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8073007Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8073177Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8073307Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8073878Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8074050Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8074211Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8074644Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8074749Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8075167Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8075275Z return prims._uniform_helper( 2024-12-18T00:10:34.8075381Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8075472Z x.shape, 2024-12-18T00:10:34.8075557Z ^^^^^^^^ 2024-12-18T00:10:34.8075664Z ...<4 lines>... 2024-12-18T00:10:34.8075767Z generator=generator, 2024-12-18T00:10:34.8075873Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8075959Z ) 2024-12-18T00:10:34.8076044Z ^ 2024-12-18T00:10:34.8076381Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8076490Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8076597Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8077018Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8077141Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8077517Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8077628Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8078031Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8078166Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8078279Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8078749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8078934Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8079044Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8079427Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8079541Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8079944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8080062Z return prims._uniform_helper( 2024-12-18T00:10:34.8080152Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8080254Z x.shape, 2024-12-18T00:10:34.8080341Z ^^^^^^^^ 2024-12-18T00:10:34.8080429Z ...<4 lines>... 2024-12-18T00:10:34.8080542Z generator=generator, 2024-12-18T00:10:34.8080629Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8080727Z ) 2024-12-18T00:10:34.8080809Z ^ 2024-12-18T00:10:34.8081130Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8081246Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8081337Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8081764Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8081873Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8082258Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8082367Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8082760Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8082980Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8083084Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8083505Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8083730Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8083852Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8084239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8084356Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8084760Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8084866Z return prims._uniform_helper( 2024-12-18T00:10:34.8084973Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8085064Z x.shape, 2024-12-18T00:10:34.8085164Z ^^^^^^^^ 2024-12-18T00:10:34.8085251Z ...<4 lines>... 2024-12-18T00:10:34.8085352Z generator=generator, 2024-12-18T00:10:34.8085453Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8085534Z ) 2024-12-18T00:10:34.8085628Z ^ 2024-12-18T00:10:34.8085949Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8086055Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8086153Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8086570Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8086690Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8087062Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8087182Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8087579Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8087710Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8087824Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8088237Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8088417Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8088525Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8088918Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8089018Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8089434Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8089540Z return prims._uniform_helper( 2024-12-18T00:10:34.8089637Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8089738Z x.shape, 2024-12-18T00:10:34.8089821Z ^^^^^^^^ 2024-12-18T00:10:34.8089921Z ...<4 lines>... 2024-12-18T00:10:34.8090020Z generator=generator, 2024-12-18T00:10:34.8090108Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8090201Z ) 2024-12-18T00:10:34.8090284Z ^ 2024-12-18T00:10:34.8090618Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8090726Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8090816Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8091247Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8091356Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8091744Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8091905Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8092311Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8092437Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8092541Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8093012Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8093181Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8093301Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8093684Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8093799Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8094202Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8094327Z return prims._uniform_helper( 2024-12-18T00:10:34.8094417Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8094505Z x.shape, 2024-12-18T00:10:34.8094598Z ^^^^^^^^ 2024-12-18T00:10:34.8094686Z ...<4 lines>... 2024-12-18T00:10:34.8094799Z generator=generator, 2024-12-18T00:10:34.8094888Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8094975Z ) 2024-12-18T00:10:34.8095070Z ^ 2024-12-18T00:10:34.8095385Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8095500Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8095589Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8096006Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8096127Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8096499Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8096623Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8097015Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8097157Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8097261Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8097684Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8097849Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8097957Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8098349Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8098449Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8098862Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8098972Z return prims._uniform_helper( 2024-12-18T00:10:34.8099074Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8099160Z x.shape, 2024-12-18T00:10:34.8099244Z ^^^^^^^^ 2024-12-18T00:10:34.8099343Z ...<4 lines>... 2024-12-18T00:10:34.8099444Z generator=generator, 2024-12-18T00:10:34.8099547Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8099633Z ) 2024-12-18T00:10:34.8099718Z ^ 2024-12-18T00:10:34.8100047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8100152Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8100258Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8100678Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8100803Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8101207Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8101358Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8101762Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8101896Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8102063Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8102476Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8102662Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8102771Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8103154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8103266Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8103671Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8103791Z return prims._uniform_helper( 2024-12-18T00:10:34.8103882Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8103983Z x.shape, 2024-12-18T00:10:34.8104066Z ^^^^^^^^ 2024-12-18T00:10:34.8104155Z ...<4 lines>... 2024-12-18T00:10:34.8104267Z generator=generator, 2024-12-18T00:10:34.8104357Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8104453Z ) 2024-12-18T00:10:34.8104537Z ^ 2024-12-18T00:10:34.8104854Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8104973Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8105064Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8105492Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8105605Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8105998Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8106106Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8106497Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8106649Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8106755Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8107176Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8107341Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8107466Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8107843Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8107948Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8108368Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8108478Z return prims._uniform_helper( 2024-12-18T00:10:34.8108583Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8108672Z x.shape, 2024-12-18T00:10:34.8108771Z ^^^^^^^^ 2024-12-18T00:10:34.8108865Z ...<4 lines>... 2024-12-18T00:10:34.8108972Z generator=generator, 2024-12-18T00:10:34.8109080Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8109171Z ) 2024-12-18T00:10:34.8109271Z ^ 2024-12-18T00:10:34.8109590Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8109699Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8109807Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8110228Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8110403Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8110780Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8110902Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8111322Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8111484Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8111601Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8112010Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8112193Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8112304Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8112699Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8112803Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8113206Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8113322Z return prims._uniform_helper( 2024-12-18T00:10:34.8113412Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8113514Z x.shape, 2024-12-18T00:10:34.8113602Z ^^^^^^^^ 2024-12-18T00:10:34.8113700Z ...<4 lines>... 2024-12-18T00:10:34.8113799Z generator=generator, 2024-12-18T00:10:34.8113886Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8113981Z ) 2024-12-18T00:10:34.8114065Z ^ 2024-12-18T00:10:34.8114399Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8114503Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8114595Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8115028Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8115142Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8115528Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8115637Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8116047Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8116177Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8116281Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8116700Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8116876Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8116995Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8117379Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8117495Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8117899Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8118004Z return prims._uniform_helper( 2024-12-18T00:10:34.8118109Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8118201Z x.shape, 2024-12-18T00:10:34.8118386Z ^^^^^^^^ 2024-12-18T00:10:34.8118480Z ...<4 lines>... 2024-12-18T00:10:34.8118581Z generator=generator, 2024-12-18T00:10:34.8118685Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8118770Z ) 2024-12-18T00:10:34.8118868Z ^ 2024-12-18T00:10:34.8119188Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8119309Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8119402Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8119883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8120004Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8120382Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8120528Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8120945Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8121089Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8121194Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8121601Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8121780Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8121889Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8122283Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8122387Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8122801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8122909Z return prims._uniform_helper( 2024-12-18T00:10:34.8123004Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8123106Z x.shape, 2024-12-18T00:10:34.8123194Z ^^^^^^^^ 2024-12-18T00:10:34.8123298Z ...<4 lines>... 2024-12-18T00:10:34.8123399Z generator=generator, 2024-12-18T00:10:34.8123489Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8123587Z ) 2024-12-18T00:10:34.8123671Z ^ 2024-12-18T00:10:34.8124001Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8124107Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8124213Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8124631Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8124742Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8125133Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8125246Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8125648Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8125778Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8125891Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8126297Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8126465Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8126586Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8126967Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8127079Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8127483Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8127603Z return prims._uniform_helper( 2024-12-18T00:10:34.8127695Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8127782Z x.shape, 2024-12-18T00:10:34.8127877Z ^^^^^^^^ 2024-12-18T00:10:34.8127963Z ...<4 lines>... 2024-12-18T00:10:34.8128071Z generator=generator, 2024-12-18T00:10:34.8128158Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8128242Z ) 2024-12-18T00:10:34.8128337Z ^ 2024-12-18T00:10:34.8128656Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8128806Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8128926Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8129358Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8129466Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8129892Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8130010Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8130405Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8130545Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8130649Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8131066Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8131236Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8131345Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8131736Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8131838Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8132257Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8132364Z return prims._uniform_helper( 2024-12-18T00:10:34.8132465Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8132553Z x.shape, 2024-12-18T00:10:34.8132640Z ^^^^^^^^ 2024-12-18T00:10:34.8132737Z ...<4 lines>... 2024-12-18T00:10:34.8132837Z generator=generator, 2024-12-18T00:10:34.8132934Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8133016Z ) 2024-12-18T00:10:34.8133100Z ^ 2024-12-18T00:10:34.8133430Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8133538Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8133640Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8134059Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8134180Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8134560Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8134669Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8135075Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8135205Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8135323Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8135730Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8135916Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8136026Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8136408Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8136523Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8136932Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8137053Z return prims._uniform_helper( 2024-12-18T00:10:34.8137144Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8137242Z x.shape, 2024-12-18T00:10:34.8137326Z ^^^^^^^^ 2024-12-18T00:10:34.8137414Z ...<4 lines>... 2024-12-18T00:10:34.8137525Z generator=generator, 2024-12-18T00:10:34.8137614Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8137709Z ) 2024-12-18T00:10:34.8137791Z ^ 2024-12-18T00:10:34.8138159Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8138302Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8138393Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8138825Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8138960Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8139375Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8139491Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8139885Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8140027Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8140133Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8140552Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8140724Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8140843Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8141227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8141344Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8141749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8141858Z return prims._uniform_helper( 2024-12-18T00:10:34.8141962Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8142052Z x.shape, 2024-12-18T00:10:34.8142149Z ^^^^^^^^ 2024-12-18T00:10:34.8142240Z ...<4 lines>... 2024-12-18T00:10:34.8142343Z generator=generator, 2024-12-18T00:10:34.8142442Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8142527Z ) 2024-12-18T00:10:34.8142624Z ^ 2024-12-18T00:10:34.8142941Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8143046Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8143154Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8143580Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8143707Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8144085Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8144202Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8144594Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8144724Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8144840Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8145253Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8145432Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8145540Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8145932Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8146035Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8146452Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8146560Z return prims._uniform_helper( 2024-12-18T00:10:34.8146651Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8146751Z x.shape, 2024-12-18T00:10:34.8146837Z ^^^^^^^^ 2024-12-18T00:10:34.8146939Z ...<4 lines>... 2024-12-18T00:10:34.8147036Z generator=generator, 2024-12-18T00:10:34.8147153Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8147272Z ) 2024-12-18T00:10:34.8147353Z ^ 2024-12-18T00:10:34.8147680Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8147785Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8147871Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8148349Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8148460Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8148847Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8148955Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8149354Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8149487Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8149603Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8150013Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8150181Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8150298Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8150682Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8150793Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8151197Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8151314Z return prims._uniform_helper( 2024-12-18T00:10:34.8151406Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8151494Z x.shape, 2024-12-18T00:10:34.8151585Z ^^^^^^^^ 2024-12-18T00:10:34.8151674Z ...<4 lines>... 2024-12-18T00:10:34.8151784Z generator=generator, 2024-12-18T00:10:34.8151877Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8151963Z ) 2024-12-18T00:10:34.8152053Z ^ 2024-12-18T00:10:34.8152371Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8152486Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8152578Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8152996Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8153116Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8153492Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8153609Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8153999Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8154140Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8154246Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8154660Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8154829Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8154938Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8155332Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8155432Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8155845Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8155953Z return prims._uniform_helper( 2024-12-18T00:10:34.8156057Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8156145Z x.shape, 2024-12-18T00:10:34.8156230Z ^^^^^^^^ 2024-12-18T00:10:34.8156357Z ...<4 lines>... 2024-12-18T00:10:34.8156484Z generator=generator, 2024-12-18T00:10:34.8156582Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8156666Z ) 2024-12-18T00:10:34.8156749Z ^ 2024-12-18T00:10:34.8157074Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8157204Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8157330Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8157751Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8157871Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8158247Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8158429Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8158836Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8158972Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8159084Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8159494Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8159675Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8159786Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8160169Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8160279Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8160685Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8160801Z return prims._uniform_helper( 2024-12-18T00:10:34.8160892Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8160990Z x.shape, 2024-12-18T00:10:34.8161079Z ^^^^^^^^ 2024-12-18T00:10:34.8161171Z ...<4 lines>... 2024-12-18T00:10:34.8161282Z generator=generator, 2024-12-18T00:10:34.8161371Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8161464Z ) 2024-12-18T00:10:34.8161547Z ^ 2024-12-18T00:10:34.8161865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8161983Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8162076Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8162506Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8162615Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8163000Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8163107Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8163500Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8163644Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8163748Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8164160Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8164332Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8164448Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8164832Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8164931Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8165343Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8165449Z return prims._uniform_helper( 2024-12-18T00:10:34.8165546Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8165692Z x.shape, 2024-12-18T00:10:34.8165786Z ^^^^^^^^ 2024-12-18T00:10:34.8165875Z ...<4 lines>... 2024-12-18T00:10:34.8165974Z generator=generator, 2024-12-18T00:10:34.8166072Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8166155Z ) 2024-12-18T00:10:34.8166246Z ^ 2024-12-18T00:10:34.8166630Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8166736Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8166840Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8167257Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8167374Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8167749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8167864Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8168258Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8168389Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8168499Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8168910Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8169089Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8169198Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8169596Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8169697Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8170099Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8170219Z return prims._uniform_helper( 2024-12-18T00:10:34.8170314Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8170411Z x.shape, 2024-12-18T00:10:34.8170496Z ^^^^^^^^ 2024-12-18T00:10:34.8170593Z ...<4 lines>... 2024-12-18T00:10:34.8170695Z generator=generator, 2024-12-18T00:10:34.8170784Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8170876Z ) 2024-12-18T00:10:34.8170959Z ^ 2024-12-18T00:10:34.8175697Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8175862Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8175958Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8176415Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8176529Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8176924Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8177037Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8177434Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8177582Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8177687Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8178117Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8178288Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8178409Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8178795Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8178895Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8179310Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8179580Z return prims._uniform_helper( 2024-12-18T00:10:34.8179684Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8179774Z x.shape, 2024-12-18T00:10:34.8179875Z ^^^^^^^^ 2024-12-18T00:10:34.8179965Z ...<4 lines>... 2024-12-18T00:10:34.8180066Z generator=generator, 2024-12-18T00:10:34.8180165Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8180289Z ) 2024-12-18T00:10:34.8180384Z ^ 2024-12-18T00:10:34.8180749Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8180859Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8180965Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8181382Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8181502Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8181880Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8182000Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8182391Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8182524Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8182640Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8183049Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8183231Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8183344Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8183738Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8183842Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8184255Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8184370Z return prims._uniform_helper( 2024-12-18T00:10:34.8184458Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8184559Z x.shape, 2024-12-18T00:10:34.8184643Z ^^^^^^^^ 2024-12-18T00:10:34.8184740Z ...<4 lines>... 2024-12-18T00:10:34.8184839Z generator=generator, 2024-12-18T00:10:34.8184930Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8185025Z ) 2024-12-18T00:10:34.8185110Z ^ 2024-12-18T00:10:34.8185435Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8185540Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8185630Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8186054Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8186165Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8186554Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8186666Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8187067Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8187198Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8187307Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8187730Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8187900Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8188022Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8188404Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8188516Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8188950Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8189093Z return prims._uniform_helper( 2024-12-18T00:10:34.8189185Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8189274Z x.shape, 2024-12-18T00:10:34.8189372Z ^^^^^^^^ 2024-12-18T00:10:34.8189463Z ...<4 lines>... 2024-12-18T00:10:34.8189665Z generator=generator, 2024-12-18T00:10:34.8189786Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8189873Z ) 2024-12-18T00:10:34.8189971Z ^ 2024-12-18T00:10:34.8190292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8190410Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8190503Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8190920Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8191046Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8191427Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8191548Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8191944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8192087Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8192193Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8192614Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8192785Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8192896Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8193286Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8193391Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8193806Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8193913Z return prims._uniform_helper( 2024-12-18T00:10:34.8194013Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8194104Z x.shape, 2024-12-18T00:10:34.8194191Z ^^^^^^^^ 2024-12-18T00:10:34.8194292Z ...<4 lines>... 2024-12-18T00:10:34.8194394Z generator=generator, 2024-12-18T00:10:34.8194495Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8194580Z ) 2024-12-18T00:10:34.8194667Z ^ 2024-12-18T00:10:34.8194998Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8195103Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8195207Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8195627Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8195740Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8196127Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8196239Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8196642Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8196774Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8196891Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8197300Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8197478Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8197587Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8197970Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8198138Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8198641Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8198762Z return prims._uniform_helper( 2024-12-18T00:10:34.8198853Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8198979Z x.shape, 2024-12-18T00:10:34.8199091Z ^^^^^^^^ 2024-12-18T00:10:34.8199184Z ...<4 lines>... 2024-12-18T00:10:34.8199297Z generator=generator, 2024-12-18T00:10:34.8199386Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8199485Z ) 2024-12-18T00:10:34.8199572Z ^ 2024-12-18T00:10:34.8199893Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8200012Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8200104Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8200539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8200654Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8201039Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8201148Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8201545Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8201687Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8201792Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8202211Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8202379Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8202499Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8202881Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8202987Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8203404Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8203512Z return prims._uniform_helper( 2024-12-18T00:10:34.8203616Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8203706Z x.shape, 2024-12-18T00:10:34.8203794Z ^^^^^^^^ 2024-12-18T00:10:34.8203892Z ...<4 lines>... 2024-12-18T00:10:34.8203993Z generator=generator, 2024-12-18T00:10:34.8204093Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8204178Z ) 2024-12-18T00:10:34.8204274Z ^ 2024-12-18T00:10:34.8204597Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8204702Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8204806Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8205226Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8205351Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8205728Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8205852Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8206246Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8206378Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8206493Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8206903Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8207085Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8207195Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8207646Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8207749Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8208156Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8208313Z return prims._uniform_helper( 2024-12-18T00:10:34.8208442Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8208547Z x.shape, 2024-12-18T00:10:34.8208635Z ^^^^^^^^ 2024-12-18T00:10:34.8208725Z ...<4 lines>... 2024-12-18T00:10:34.8208842Z generator=generator, 2024-12-18T00:10:34.8208932Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8209026Z ) 2024-12-18T00:10:34.8209112Z ^ 2024-12-18T00:10:34.8209444Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8209553Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8209649Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8210085Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8210196Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8210584Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8210698Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8211106Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8211236Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8211342Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8211764Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8211934Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8212062Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8212445Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8212558Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8212966Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8213075Z return prims._uniform_helper( 2024-12-18T00:10:34.8213177Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8213267Z x.shape, 2024-12-18T00:10:34.8213362Z ^^^^^^^^ 2024-12-18T00:10:34.8213450Z ...<4 lines>... 2024-12-18T00:10:34.8213553Z generator=generator, 2024-12-18T00:10:34.8213654Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8213738Z ) 2024-12-18T00:10:34.8213834Z ^ 2024-12-18T00:10:34.8214153Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8214271Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8214364Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8214783Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8214905Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8215287Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8215407Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8215801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8215943Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8216048Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8216459Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8216672Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8216809Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8217205Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8217308Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8217777Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8217886Z return prims._uniform_helper( 2024-12-18T00:10:34.8217979Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8218081Z x.shape, 2024-12-18T00:10:34.8218168Z ^^^^^^^^ 2024-12-18T00:10:34.8218270Z ...<4 lines>... 2024-12-18T00:10:34.8218372Z generator=generator, 2024-12-18T00:10:34.8218461Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8218557Z ) 2024-12-18T00:10:34.8218640Z ^ 2024-12-18T00:10:34.8218969Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8219083Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8219180Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8219604Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8219728Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8220107Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8220230Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8220622Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8220764Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8220867Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8221275Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8221458Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8221566Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8221958Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8222060Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8222479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8222586Z return prims._uniform_helper( 2024-12-18T00:10:34.8222677Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8222776Z x.shape, 2024-12-18T00:10:34.8222863Z ^^^^^^^^ 2024-12-18T00:10:34.8222958Z ...<4 lines>... 2024-12-18T00:10:34.8223057Z generator=generator, 2024-12-18T00:10:34.8223145Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8223244Z ) 2024-12-18T00:10:34.8223327Z ^ 2024-12-18T00:10:34.8223662Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8223769Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8223872Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8224292Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8224405Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8224796Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8224906Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8225307Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8225442Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8225557Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8225965Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8226192Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8226313Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8226694Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8226832Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8227263Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8227385Z return prims._uniform_helper( 2024-12-18T00:10:34.8227478Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8227566Z x.shape, 2024-12-18T00:10:34.8227666Z ^^^^^^^^ 2024-12-18T00:10:34.8227755Z ...<4 lines>... 2024-12-18T00:10:34.8227864Z generator=generator, 2024-12-18T00:10:34.8227955Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8228045Z ) 2024-12-18T00:10:34.8228141Z ^ 2024-12-18T00:10:34.8228459Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8228574Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8228665Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8229095Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8229209Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8229585Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8229704Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8230094Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8230236Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8230340Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8230760Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8230930Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8231038Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8231433Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8231537Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8231949Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8232056Z return prims._uniform_helper( 2024-12-18T00:10:34.8232159Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8232246Z x.shape, 2024-12-18T00:10:34.8232332Z ^^^^^^^^ 2024-12-18T00:10:34.8232432Z ...<4 lines>... 2024-12-18T00:10:34.8232532Z generator=generator, 2024-12-18T00:10:34.8232633Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8232722Z ) 2024-12-18T00:10:34.8232804Z ^ 2024-12-18T00:10:34.8233132Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8233238Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8233342Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8233764Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8233888Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8234260Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8234372Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8234774Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8234905Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8235047Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8235482Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8235666Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8235775Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8236213Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8236328Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8236738Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8236859Z return prims._uniform_helper( 2024-12-18T00:10:34.8236950Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8237055Z x.shape, 2024-12-18T00:10:34.8237142Z ^^^^^^^^ 2024-12-18T00:10:34.8237232Z ...<4 lines>... 2024-12-18T00:10:34.8237346Z generator=generator, 2024-12-18T00:10:34.8237438Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8237533Z ) 2024-12-18T00:10:34.8237616Z ^ 2024-12-18T00:10:34.8237930Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8238047Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8238142Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8238659Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8238771Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8239163Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8239271Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8239661Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8239805Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8239912Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8240333Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8240503Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8240628Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8241015Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8241118Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8241538Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8241647Z return prims._uniform_helper( 2024-12-18T00:10:34.8241751Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8241841Z x.shape, 2024-12-18T00:10:34.8241940Z ^^^^^^^^ 2024-12-18T00:10:34.8242031Z ...<4 lines>... 2024-12-18T00:10:34.8242135Z generator=generator, 2024-12-18T00:10:34.8242235Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8242319Z ) 2024-12-18T00:10:34.8242414Z ^ 2024-12-18T00:10:34.8242731Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8242837Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8242943Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8243362Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8243484Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8243860Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8243979Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8244385Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8244590Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8244706Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8245115Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8245285Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8245463Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8245847Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8245963Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8246368Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8246490Z return prims._uniform_helper( 2024-12-18T00:10:34.8246581Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8246670Z x.shape, 2024-12-18T00:10:34.8246770Z ^^^^^^^^ 2024-12-18T00:10:34.8246859Z ...<4 lines>... 2024-12-18T00:10:34.8246976Z generator=generator, 2024-12-18T00:10:34.8247065Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8247146Z ) 2024-12-18T00:10:34.8247245Z ^ 2024-12-18T00:10:34.8247562Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8247683Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8247775Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8248197Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8248322Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8248700Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8248823Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8249215Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8249365Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8249468Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8249890Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8250062Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8250172Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8250568Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8250667Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8251086Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8251193Z return prims._uniform_helper( 2024-12-18T00:10:34.8251297Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8251387Z x.shape, 2024-12-18T00:10:34.8251470Z ^^^^^^^^ 2024-12-18T00:10:34.8251571Z ...<4 lines>... 2024-12-18T00:10:34.8251669Z generator=generator, 2024-12-18T00:10:34.8251770Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8251850Z ) 2024-12-18T00:10:34.8251935Z ^ 2024-12-18T00:10:34.8252265Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8252367Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8252475Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8252890Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8253013Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8253389Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8253495Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8253963Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8254092Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8254209Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8254640Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8254848Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8254956Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8255344Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8255456Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8255857Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8255979Z return prims._uniform_helper( 2024-12-18T00:10:34.8256069Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8256170Z x.shape, 2024-12-18T00:10:34.8256255Z ^^^^^^^^ 2024-12-18T00:10:34.8256344Z ...<4 lines>... 2024-12-18T00:10:34.8256454Z generator=generator, 2024-12-18T00:10:34.8256541Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8256639Z ) 2024-12-18T00:10:34.8256725Z ^ 2024-12-18T00:10:34.8257044Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8257160Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8257249Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8257678Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8257786Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8258172Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8258283Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8258677Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8258822Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8258923Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8259343Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8259513Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8259632Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8260015Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8260113Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8260526Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8260632Z return prims._uniform_helper( 2024-12-18T00:10:34.8260733Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8260818Z x.shape, 2024-12-18T00:10:34.8260913Z ^^^^^^^^ 2024-12-18T00:10:34.8260999Z ...<4 lines>... 2024-12-18T00:10:34.8261099Z generator=generator, 2024-12-18T00:10:34.8261200Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8261283Z ) 2024-12-18T00:10:34.8261376Z ^ 2024-12-18T00:10:34.8261694Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8261798Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8261902Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8262317Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8262437Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8262808Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8262978Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8263391Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8263568Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8263756Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8264217Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8264402Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8264510Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8264909Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8265011Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8265413Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8265537Z return prims._uniform_helper( 2024-12-18T00:10:34.8265626Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8265727Z x.shape, 2024-12-18T00:10:34.8265813Z ^^^^^^^^ 2024-12-18T00:10:34.8265914Z ...<4 lines>... 2024-12-18T00:10:34.8266014Z generator=generator, 2024-12-18T00:10:34.8266103Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8266201Z ) 2024-12-18T00:10:34.8266280Z ^ 2024-12-18T00:10:34.8266611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8266714Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8266807Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8267237Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8267346Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8267732Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8267841Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8268242Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8268372Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8268477Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8268894Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8269061Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8269182Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8269563Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8269671Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8270073Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8270178Z return prims._uniform_helper( 2024-12-18T00:10:34.8270279Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8270365Z x.shape, 2024-12-18T00:10:34.8270461Z ^^^^^^^^ 2024-12-18T00:10:34.8270544Z ...<4 lines>... 2024-12-18T00:10:34.8270644Z generator=generator, 2024-12-18T00:10:34.8270743Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8270823Z ) 2024-12-18T00:10:34.8270916Z ^ 2024-12-18T00:10:34.8271227Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8271343Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8271430Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8271851Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8271999Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8272398Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8272521Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8272909Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8273081Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8273209Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8273833Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8274017Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8274123Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8274517Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8274619Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8275036Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8275147Z return prims._uniform_helper( 2024-12-18T00:10:34.8275233Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8275336Z x.shape, 2024-12-18T00:10:34.8275419Z ^^^^^^^^ 2024-12-18T00:10:34.8275524Z ...<4 lines>... 2024-12-18T00:10:34.8275623Z generator=generator, 2024-12-18T00:10:34.8275711Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8275808Z ) 2024-12-18T00:10:34.8275888Z ^ 2024-12-18T00:10:34.8276214Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8276318Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8276421Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8276839Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8276950Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8277337Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8277443Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8277845Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8277976Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8278091Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8278562Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8278733Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8278859Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8279242Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8279364Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8279772Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8279893Z return prims._uniform_helper( 2024-12-18T00:10:34.8279985Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8280075Z x.shape, 2024-12-18T00:10:34.8280177Z ^^^^^^^^ 2024-12-18T00:10:34.8280267Z ...<4 lines>... 2024-12-18T00:10:34.8280380Z generator=generator, 2024-12-18T00:10:34.8280469Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8280550Z ) 2024-12-18T00:10:34.8280646Z ^ 2024-12-18T00:10:34.8280964Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8281085Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8281177Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8281611Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8281865Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8282241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8282365Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8282826Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8282969Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8283073Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8283495Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8283665Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8283778Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8284175Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8284281Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8284700Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8284806Z return prims._uniform_helper( 2024-12-18T00:10:34.8284909Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8284999Z x.shape, 2024-12-18T00:10:34.8285083Z ^^^^^^^^ 2024-12-18T00:10:34.8285185Z ...<4 lines>... 2024-12-18T00:10:34.8285285Z generator=generator, 2024-12-18T00:10:34.8285388Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8285466Z ) 2024-12-18T00:10:34.8285549Z ^ 2024-12-18T00:10:34.8285882Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8285985Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8286088Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8286507Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8286633Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8287002Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8287109Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8287513Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8287642Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8287757Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8288162Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8288341Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8288447Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8288834Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8288947Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8289349Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8289469Z return prims._uniform_helper( 2024-12-18T00:10:34.8289560Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8289660Z x.shape, 2024-12-18T00:10:34.8289745Z ^^^^^^^^ 2024-12-18T00:10:34.8289832Z ...<4 lines>... 2024-12-18T00:10:34.8289944Z generator=generator, 2024-12-18T00:10:34.8290032Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8290128Z ) 2024-12-18T00:10:34.8290209Z ^ 2024-12-18T00:10:34.8290529Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8290645Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8290769Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8291225Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8291333Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8291748Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8291882Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8292280Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8292424Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8292525Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8292946Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8293115Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8293237Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8293617Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8293729Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8294135Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8294242Z return prims._uniform_helper( 2024-12-18T00:10:34.8294342Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8294430Z x.shape, 2024-12-18T00:10:34.8294529Z ^^^^^^^^ 2024-12-18T00:10:34.8294615Z ...<4 lines>... 2024-12-18T00:10:34.8294711Z generator=generator, 2024-12-18T00:10:34.8294813Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8294895Z ) 2024-12-18T00:10:34.8295018Z ^ 2024-12-18T00:10:34.8295498Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8295609Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8295716Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8296136Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8296258Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8296638Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8296760Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8297153Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8297296Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8297401Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8297809Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8297992Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8298100Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8298491Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8298591Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8299012Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8299118Z return prims._uniform_helper( 2024-12-18T00:10:34.8299208Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8299308Z x.shape, 2024-12-18T00:10:34.8299391Z ^^^^^^^^ 2024-12-18T00:10:34.8299489Z ...<4 lines>... 2024-12-18T00:10:34.8299589Z generator=generator, 2024-12-18T00:10:34.8299675Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8299769Z ) 2024-12-18T00:10:34.8299852Z ^ 2024-12-18T00:10:34.8300182Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8300348Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8300440Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8300869Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8300976Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8301412Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8301518Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8301922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8302052Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8302167Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8302576Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8302748Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8302870Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8303251Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8303363Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8303768Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8303884Z return prims._uniform_helper( 2024-12-18T00:10:34.8303975Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8304060Z x.shape, 2024-12-18T00:10:34.8304158Z ^^^^^^^^ 2024-12-18T00:10:34.8304243Z ...<4 lines>... 2024-12-18T00:10:34.8304359Z generator=generator, 2024-12-18T00:10:34.8304445Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8304529Z ) 2024-12-18T00:10:34.8304625Z ^ 2024-12-18T00:10:34.8304944Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8305065Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8305154Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8305573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8305697Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8306071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8306190Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8306581Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8306723Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8306825Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8307247Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8307420Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8307526Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8307922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8308023Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8308441Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8308551Z return prims._uniform_helper( 2024-12-18T00:10:34.8308655Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8308743Z x.shape, 2024-12-18T00:10:34.8308828Z ^^^^^^^^ 2024-12-18T00:10:34.8308929Z ...<4 lines>... 2024-12-18T00:10:34.8309027Z generator=generator, 2024-12-18T00:10:34.8309128Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8309241Z ) 2024-12-18T00:10:34.8309348Z ^ 2024-12-18T00:10:34.8309680Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8309783Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8309886Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8310329Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8310490Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8310865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8310972Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8311375Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8311505Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8311618Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8312029Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8312209Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8312318Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8312699Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8312815Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8313214Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8313340Z return prims._uniform_helper( 2024-12-18T00:10:34.8313432Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8313535Z x.shape, 2024-12-18T00:10:34.8313623Z ^^^^^^^^ 2024-12-18T00:10:34.8313714Z ...<4 lines>... 2024-12-18T00:10:34.8313831Z generator=generator, 2024-12-18T00:10:34.8313926Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8314027Z ) 2024-12-18T00:10:34.8314113Z ^ 2024-12-18T00:10:34.8314434Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8314555Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8314647Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8315083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8315195Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8315587Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8315696Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8316089Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8316235Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8316342Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8316771Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8316940Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8317061Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8317448Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8317552Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8317972Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8318081Z return prims._uniform_helper( 2024-12-18T00:10:34.8318186Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8318276Z x.shape, 2024-12-18T00:10:34.8318455Z ^^^^^^^^ 2024-12-18T00:10:34.8318548Z ...<4 lines>... 2024-12-18T00:10:34.8318685Z generator=generator, 2024-12-18T00:10:34.8318812Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8318896Z ) 2024-12-18T00:10:34.8318993Z ^ 2024-12-18T00:10:34.8319314Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8319420Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8319549Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8319996Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8320120Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8320498Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8320619Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8321011Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8321145Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8321262Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8321667Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8321845Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8321955Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8322350Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8322451Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8322852Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8322968Z return prims._uniform_helper( 2024-12-18T00:10:34.8323060Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8323158Z x.shape, 2024-12-18T00:10:34.8323244Z ^^^^^^^^ 2024-12-18T00:10:34.8323345Z ...<4 lines>... 2024-12-18T00:10:34.8323448Z generator=generator, 2024-12-18T00:10:34.8323538Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8323632Z ) 2024-12-18T00:10:34.8323716Z ^ 2024-12-18T00:10:34.8324046Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8324152Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8324246Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8324676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8324787Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8325177Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8325287Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8325694Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8325829Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8325935Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8326354Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8326525Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8326653Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8327036Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8327151Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8327559Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8327666Z return prims._uniform_helper( 2024-12-18T00:10:34.8327767Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8327853Z x.shape, 2024-12-18T00:10:34.8327979Z ^^^^^^^^ 2024-12-18T00:10:34.8328347Z ...<4 lines>... 2024-12-18T00:10:34.8328458Z generator=generator, 2024-12-18T00:10:34.8328548Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8328634Z ) 2024-12-18T00:10:34.8328727Z ^ 2024-12-18T00:10:34.8329048Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8329218Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8329310Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8329732Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8329854Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8330231Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8330356Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8330750Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8330899Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8331004Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8331414Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8331602Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8331711Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8332107Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8332211Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8332630Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8332738Z return prims._uniform_helper( 2024-12-18T00:10:34.8332829Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8332932Z x.shape, 2024-12-18T00:10:34.8333019Z ^^^^^^^^ 2024-12-18T00:10:34.8333119Z ...<4 lines>... 2024-12-18T00:10:34.8333219Z generator=generator, 2024-12-18T00:10:34.8333307Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8333400Z ) 2024-12-18T00:10:34.8333484Z ^ 2024-12-18T00:10:34.8333815Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8333922Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8334024Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8334446Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8334555Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8334942Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8335050Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8335452Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8335586Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8335702Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8336110Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8336283Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8336403Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8336784Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8336897Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8337299Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8337420Z return prims._uniform_helper( 2024-12-18T00:10:34.8337627Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8337714Z x.shape, 2024-12-18T00:10:34.8337812Z ^^^^^^^^ 2024-12-18T00:10:34.8337902Z ...<4 lines>... 2024-12-18T00:10:34.8338020Z generator=generator, 2024-12-18T00:10:34.8338110Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8338195Z ) 2024-12-18T00:10:34.8338292Z ^ 2024-12-18T00:10:34.8338676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8338794Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8338886Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8339317Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8339426Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8339802Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8339923Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8340316Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8340459Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8340562Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8340987Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8341157Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8341265Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8341657Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8341760Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8342177Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8342288Z return prims._uniform_helper( 2024-12-18T00:10:34.8342393Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8342482Z x.shape, 2024-12-18T00:10:34.8342566Z ^^^^^^^^ 2024-12-18T00:10:34.8342665Z ...<4 lines>... 2024-12-18T00:10:34.8342764Z generator=generator, 2024-12-18T00:10:34.8342868Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8342954Z ) 2024-12-18T00:10:34.8343038Z ^ 2024-12-18T00:10:34.8343372Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8343478Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8343578Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8343996Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8344115Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8344491Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8344604Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8345005Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8345137Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8345252Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8345664Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8345850Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8345962Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8346354Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8346457Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8346860Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8347036Z return prims._uniform_helper( 2024-12-18T00:10:34.8347128Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8347227Z x.shape, 2024-12-18T00:10:34.8347313Z ^^^^^^^^ 2024-12-18T00:10:34.8347402Z ...<4 lines>... 2024-12-18T00:10:34.8347516Z generator=generator, 2024-12-18T00:10:34.8347648Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8347775Z ) 2024-12-18T00:10:34.8347859Z ^ 2024-12-18T00:10:34.8348180Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8348300Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8348393Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8348830Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8348944Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8349338Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8349451Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8349846Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8349994Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8350107Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8350531Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8350702Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8350825Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8351209Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8351327Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8351735Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8351848Z return prims._uniform_helper( 2024-12-18T00:10:34.8351956Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8352046Z x.shape, 2024-12-18T00:10:34.8352147Z ^^^^^^^^ 2024-12-18T00:10:34.8352238Z ...<4 lines>... 2024-12-18T00:10:34.8352344Z generator=generator, 2024-12-18T00:10:34.8352451Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8352536Z ) 2024-12-18T00:10:34.8352637Z ^ 2024-12-18T00:10:34.8352954Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8353063Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8353168Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8353586Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8353710Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8354089Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8354209Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8354601Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8354743Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8354846Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8355253Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8355432Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8355541Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8355931Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8356064Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8356503Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8356608Z return prims._uniform_helper( 2024-12-18T00:10:34.8356698Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8356798Z x.shape, 2024-12-18T00:10:34.8356885Z ^^^^^^^^ 2024-12-18T00:10:34.8357009Z ...<4 lines>... 2024-12-18T00:10:34.8357165Z generator=generator, 2024-12-18T00:10:34.8357254Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8357352Z ) 2024-12-18T00:10:34.8357435Z ^ 2024-12-18T00:10:34.8357768Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8357873Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8357965Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8358496Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8358615Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8359005Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8359114Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8359525Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8359658Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8359777Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8360186Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8360359Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8360481Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8360864Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8360982Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8361387Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8361506Z return prims._uniform_helper( 2024-12-18T00:10:34.8361597Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8361686Z x.shape, 2024-12-18T00:10:34.8361784Z ^^^^^^^^ 2024-12-18T00:10:34.8361876Z ...<4 lines>... 2024-12-18T00:10:34.8361988Z generator=generator, 2024-12-18T00:10:34.8362077Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8362160Z ) 2024-12-18T00:10:34.8362254Z ^ 2024-12-18T00:10:34.8362573Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8362690Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8362781Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8363212Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8363326Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8363702Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8363822Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8364216Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8364358Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8364461Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8364882Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8365051Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8365161Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8365557Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8365724Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8366141Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8366249Z return prims._uniform_helper( 2024-12-18T00:10:34.8366378Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8366494Z x.shape, 2024-12-18T00:10:34.8366581Z ^^^^^^^^ 2024-12-18T00:10:34.8366687Z ...<4 lines>... 2024-12-18T00:10:34.8366791Z generator=generator, 2024-12-18T00:10:34.8366892Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8366977Z ) 2024-12-18T00:10:34.8367061Z ^ 2024-12-18T00:10:34.8367394Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8367499Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8367604Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8368024Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8368147Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8368521Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8368629Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8369034Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8369164Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8369279Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8369688Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8369871Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8369978Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8370362Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8370472Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8370876Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8370997Z return prims._uniform_helper( 2024-12-18T00:10:34.8371090Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8371189Z x.shape, 2024-12-18T00:10:34.8371275Z ^^^^^^^^ 2024-12-18T00:10:34.8371364Z ...<4 lines>... 2024-12-18T00:10:34.8371476Z generator=generator, 2024-12-18T00:10:34.8371566Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8371662Z ) 2024-12-18T00:10:34.8371746Z ^ 2024-12-18T00:10:34.8372063Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8372179Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8372271Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8372700Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8372809Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8373194Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8373306Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8373888Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8374036Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8374139Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8374564Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8374733Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8374936Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8375356Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8375457Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8375904Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8376047Z return prims._uniform_helper( 2024-12-18T00:10:34.8376151Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8376240Z x.shape, 2024-12-18T00:10:34.8376339Z ^^^^^^^^ 2024-12-18T00:10:34.8376428Z ...<4 lines>... 2024-12-18T00:10:34.8376530Z generator=generator, 2024-12-18T00:10:34.8376631Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8376716Z ) 2024-12-18T00:10:34.8376818Z ^ 2024-12-18T00:10:34.8377137Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8377246Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8377355Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8377776Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8377902Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8378282Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8378403Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8378795Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8378929Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8379044Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8379455Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8379637Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8379749Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8380139Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8380240Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8380647Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8380767Z return prims._uniform_helper( 2024-12-18T00:10:34.8380857Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8380954Z x.shape, 2024-12-18T00:10:34.8381039Z ^^^^^^^^ 2024-12-18T00:10:34.8381139Z ...<4 lines>... 2024-12-18T00:10:34.8381241Z generator=generator, 2024-12-18T00:10:34.8381329Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8381425Z ) 2024-12-18T00:10:34.8381511Z ^ 2024-12-18T00:10:34.8381841Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8381952Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8382046Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8382479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8382592Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8382984Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8383095Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8383503Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8383638Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8383743Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8384166Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8384407Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8384536Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8384922Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8385040Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8385500Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8385611Z return prims._uniform_helper( 2024-12-18T00:10:34.8385717Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8385807Z x.shape, 2024-12-18T00:10:34.8385911Z ^^^^^^^^ 2024-12-18T00:10:34.8386003Z ...<4 lines>... 2024-12-18T00:10:34.8386118Z generator=generator, 2024-12-18T00:10:34.8386211Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8386297Z ) 2024-12-18T00:10:34.8386394Z ^ 2024-12-18T00:10:34.8386716Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8386840Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8386932Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8387352Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8387479Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8387858Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8387982Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8388375Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8388517Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8388620Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8389031Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8389216Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8389324Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8389720Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8389824Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8390239Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8390346Z return prims._uniform_helper( 2024-12-18T00:10:34.8390436Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8390534Z x.shape, 2024-12-18T00:10:34.8390619Z ^^^^^^^^ 2024-12-18T00:10:34.8390719Z ...<4 lines>... 2024-12-18T00:10:34.8390819Z generator=generator, 2024-12-18T00:10:34.8390921Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8391006Z ) 2024-12-18T00:10:34.8391089Z ^ 2024-12-18T00:10:34.8391419Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8391526Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8391631Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8392049Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8392162Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8392548Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8392657Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8393062Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8393193Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8393309Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8393751Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8393948Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8394069Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8394479Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8394622Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8395029Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8395152Z return prims._uniform_helper( 2024-12-18T00:10:34.8395244Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8395337Z x.shape, 2024-12-18T00:10:34.8395442Z ^^^^^^^^ 2024-12-18T00:10:34.8395531Z ...<4 lines>... 2024-12-18T00:10:34.8395643Z generator=generator, 2024-12-18T00:10:34.8395738Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8395824Z ) 2024-12-18T00:10:34.8395920Z ^ 2024-12-18T00:10:34.8396241Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8396359Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8396450Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8396883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8396994Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8397371Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 287, in _prim_impl 2024-12-18T00:10:34.8397492Z return impl_aten(*args, **kwargs) 2024-12-18T00:10:34.8397883Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2755, in _uniform_aten 2024-12-18T00:10:34.8398027Z a.uniform_(low, high, generator=generator) 2024-12-18T00:10:34.8398135Z ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8398645Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2792, in uniform_ 2024-12-18T00:10:34.8398817Z return self.copy_(uniform(self, low, high, generator)) 2024-12-18T00:10:34.8398929Z ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8399331Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/wrappers.py", line 291, in _fn 2024-12-18T00:10:34.8399436Z result = fn(*args, **kwargs) 2024-12-18T00:10:34.8399858Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_decomp/decompositions.py", line 2780, in uniform 2024-12-18T00:10:34.8399972Z return prims._uniform_helper( 2024-12-18T00:10:34.8400079Z ~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:10:34.8400171Z x.shape, 2024-12-18T00:10:34.8400259Z ^^^^^^^^ 2024-12-18T00:10:34.8400365Z ...<4 lines>... 2024-12-18T00:10:34.8400472Z generator=generator, 2024-12-18T00:10:34.8400577Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8400662Z ) 2024-12-18T00:10:34.8400749Z ^ 2024-12-18T00:10:34.8401083Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:10:34.8401188Z return self._op(*args, **kwargs) 2024-12-18T00:10:34.8401291Z ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8401712Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 302, in _backend_select_impl 2024-12-18T00:10:34.8401835Z return _prim_impl(*args, **kwargs) 2024-12-18T00:10:34.8402211Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 286, in _prim_impl 2024-12-18T00:10:34.8402309Z meta(*args, **kwargs) 2024-12-18T00:10:34.8402410Z ~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:10:34.8402801Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/__init__.py", line 2741, in _uniform_meta 2024-12-18T00:10:34.8403024Z strides = utils.make_contiguous_strides_for(shape) 2024-12-18T00:10:34.8403505Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py", line 1622, in make_contiguous_strides_for 2024-12-18T00:10:34.8403620Z validate_shape(shape) 2024-12-18T00:10:34.8403708Z ~~~~~~~~~~~~~~^^^^^^^ 2024-12-18T00:10:34.8403887Z RecursionError: maximum recursion depth exceeded 2024-12-18T00:10:34.8403932Z 2024-12-18T00:10:34.8404125Z To execute this test, run the following from the base repo dir: 2024-12-18T00:10:34.8404410Z python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:10:34.8404416Z 2024-12-18T00:10:34.8404665Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:10:34.8405260Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-b6196ef157b65f12.xml - 2024-12-18T00:10:34.8405433Z =========================== short test summary info ============================ 2024-12-18T00:10:34.8405972Z FAILED [0.0129s] test_python_dispatch.py::TestPythonRegistration::test_returning_symint - RecursionError: maximum recursion depth exceeded 2024-12-18T00:10:34.8405978Z 2024-12-18T00:10:34.8406182Z To execute this test, run the following from the base repo dir: 2024-12-18T00:10:34.8406464Z python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:10:34.8406469Z 2024-12-18T00:10:34.8406715Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:10:34.8406879Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2024-12-18T00:10:34.8407064Z =============== 1 failed, 14 passed, 8 skipped, 2 rerun in 0.44s =============== 2024-12-18T00:10:34.8407169Z Got exit code 1 2024-12-18T00:10:34.8407269Z Retrying single test... 2024-12-18T00:10:34.8407713Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-e1580265922cae15.xml 2024-12-18T00:10:34.8407864Z ============================= test session starts ============================== 2024-12-18T00:10:34.8408178Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:10:34.8408276Z cachedir: .pytest_cache 2024-12-18T00:10:34.8408732Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:10:34.8408857Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:10:34.8408955Z configfile: pytest.ini 2024-12-18T00:10:34.8409433Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:10:34.8409629Z collecting ... collected 119 items / 118 deselected / 1 selected 2024-12-18T00:10:34.8410124Z stepcurrent: skipping 22 already run items. Running only test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint 2024-12-18T00:10:34.8410232Z Running 1 items in this shard 2024-12-18T00:10:34.8410237Z 2024-12-18T00:10:34.8410574Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint PASSED [0.1129s] [100%] 2024-12-18T00:10:34.8410579Z 2024-12-18T00:10:34.8411161Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-e1580265922cae15.xml - 2024-12-18T00:10:34.8411330Z ====================== 1 passed, 118 deselected in 0.15s ======================= 2024-12-18T00:10:34.8411434Z Got exit code 0 2024-12-18T00:10:34.8411650Z Test succeeeded in new process, continuing with the rest of the tests 2024-12-18T00:10:34.8412094Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-e7eb1f67706a506d.xml 2024-12-18T00:10:34.8412244Z ============================= test session starts ============================== 2024-12-18T00:10:34.8412559Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:10:34.8412736Z cachedir: .pytest_cache 2024-12-18T00:10:34.8413198Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:10:34.8413310Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:10:34.8413411Z configfile: pytest.ini 2024-12-18T00:10:34.8413940Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:10:34.8414134Z collecting ... collected 119 items / 23 deselected / 96 selected 2024-12-18T00:10:34.8414278Z stepcurrent: skipping 23 already run items. 2024-12-18T00:10:34.8414387Z Running 96 items in this shard 2024-12-18T00:10:34.8414392Z 2024-12-18T00:10:34.8414702Z test_python_dispatch.py::TestPythonDispatch::test_all_same_mode PASSED [0.0033s] [ 1%] 2024-12-18T00:10:34.8415008Z test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr PASSED [0.0030s] [ 2%] 2024-12-18T00:10:34.8415270Z test_python_dispatch.py::TestPythonDispatch::test_basic PASSED [0.0061s] [ 3%] 2024-12-18T00:10:34.8415672Z test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode PASSED [0.0028s] [ 4%] 2024-12-18T00:10:34.8415994Z test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor PASSED [0.0018s] [ 5%] 2024-12-18T00:10:34.8416305Z test_python_dispatch.py::TestPythonDispatch::test_custom_autograd PASSED [0.0046s] [ 6%] 2024-12-18T00:10:34.8416676Z test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes PASSED [0.0678s] [ 7%] 2024-12-18T00:10:34.8417056Z test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path PASSED [0.0020s] [ 8%] 2024-12-18T00:10:34.8417413Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass PASSED [0.0023s] [ 9%] 2024-12-18T00:10:34.8417765Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass PASSED [0.0024s] [ 10%] 2024-12-18T00:10:34.8418260Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type PASSED [0.0019s] [ 11%] 2024-12-18T00:10:34.8418645Z test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once PASSED [0.0021s] [ 12%] 2024-12-18T00:10:34.8418960Z test_python_dispatch.py::TestPythonDispatch::test_device_slowpath PASSED [0.0023s] [ 13%] 2024-12-18T00:10:34.8419248Z test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath PASSED [0.0021s] [ 14%] 2024-12-18T00:10:34.8419573Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call PASSED [0.0022s] [ 15%] 2024-12-18T00:10:34.8419921Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg PASSED [0.0020s] [ 16%] 2024-12-18T00:10:34.8420282Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd PASSED [0.0018s] [ 17%] 2024-12-18T00:10:34.8420646Z test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode PASSED [0.0020s] [ 18%] 2024-12-18T00:10:34.8420976Z test_python_dispatch.py::TestPythonDispatch::test_exception_handling PASSED [0.0022s] [ 19%] 2024-12-18T00:10:34.8421264Z test_python_dispatch.py::TestPythonDispatch::test_fancy_strides PASSED [0.0022s] [ 20%] 2024-12-18T00:10:34.8421530Z test_python_dispatch.py::TestPythonDispatch::test_format PASSED [0.0033s] [ 21%] 2024-12-18T00:10:34.8421828Z test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode PASSED [0.0020s] [ 22%] 2024-12-18T00:10:34.8422118Z test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack PASSED [0.0020s] [ 23%] 2024-12-18T00:10:34.8422513Z 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2024-12-18T00:10:34.8427976Z test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode PASSED [0.0021s] [ 41%] 2024-12-18T00:10:34.8428284Z test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode PASSED [0.0020s] [ 42%] 2024-12-18T00:10:34.8428571Z test_python_dispatch.py::TestPythonDispatch::test_new_ones PASSED [0.0021s] [ 43%] 2024-12-18T00:10:34.8428861Z test_python_dispatch.py::TestPythonDispatch::test_none_wrapping PASSED [0.0027s] [ 44%] 2024-12-18T00:10:34.8429197Z test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode PASSED [0.0020s] [ 45%] 2024-12-18T00:10:34.8429516Z test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list PASSED [0.0023s] [ 46%] 2024-12-18T00:10:34.8429776Z test_python_dispatch.py::TestPythonDispatch::test_out PASSED [0.0022s] [ 47%] 2024-12-18T00:10:34.8430103Z test_python_dispatch.py::TestPythonDispatch::test_produce_real_type PASSED [0.0025s] [ 48%] 2024-12-18T00:10:34.8430394Z test_python_dispatch.py::TestPythonDispatch::test_record_stream PASSED [0.0020s] [ 50%] 2024-12-18T00:10:34.8430842Z test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride PASSED [0.0026s] [ 51%] 2024-12-18T00:10:34.8431131Z test_python_dispatch.py::TestPythonDispatch::test_return_stream PASSED [0.0023s] [ 52%] 2024-12-18T00:10:34.8431412Z test_python_dispatch.py::TestPythonDispatch::test_set_data PASSED [0.0022s] [ 53%] 2024-12-18T00:10:34.8431747Z test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach PASSED [0.0025s] [ 54%] 2024-12-18T00:10:34.8432057Z test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path PASSED [0.0024s] [ 55%] 2024-12-18T00:10:34.8432395Z test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass PASSED [0.0017s] [ 56%] 2024-12-18T00:10:34.8432666Z test_python_dispatch.py::TestPythonDispatch::test_storage PASSED [0.0018s] [ 57%] 2024-12-18T00:10:34.8433075Z test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object PASSED [0.0019s] [ 58%] 2024-12-18T00:10:34.8433383Z test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path PASSED [0.0024s] [ 59%] 2024-12-18T00:10:34.8433758Z test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check PASSED [0.0026s] [ 60%] 2024-12-18T00:10:34.8434064Z test_python_dispatch.py::TestPythonDispatch::test_subclass_creation PASSED [0.0024s] [ 61%] 2024-12-18T00:10:34.8434419Z test_python_dispatch.py::TestPythonDispatch::test_subclass_priority PASSED [0.0021s] [ 62%] 2024-12-18T00:10:34.8434806Z test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path PASSED [0.0023s] [ 63%] 2024-12-18T00:10:34.8435200Z test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode PASSED [0.0023s] [ 64%] 2024-12-18T00:10:34.8435584Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic PASSED [0.0019s] [ 65%] 2024-12-18T00:10:34.8435986Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch PASSED [0.0026s] [ 66%] 2024-12-18T00:10:34.8436385Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority PASSED [0.0022s] [ 67%] 2024-12-18T00:10:34.8436768Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors PASSED [0.0019s] [ 68%] 2024-12-18T00:10:34.8437047Z test_python_dispatch.py::TestPythonDispatch::test_version PASSED [0.0023s] [ 69%] 2024-12-18T00:10:34.8437440Z test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch PASSED [0.0019s] [ 70%] 2024-12-18T00:10:34.8437801Z test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately PASSED [0.0018s] [ 71%] 2024-12-18T00:10:34.8438107Z test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes PASSED [0.0019s] [ 72%] 2024-12-18T00:10:34.8438601Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys PASSED [0.0018s] [ 73%] 2024-12-18T00:10:34.8439048Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype PASSED [0.0406s] [ 75%] 2024-12-18T00:10:34.8439487Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode PASSED [0.0027s] [ 76%] 2024-12-18T00:10:34.8439832Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes PASSED [0.0039s] [ 77%] 2024-12-18T00:10:34.8440101Z test_python_dispatch.py::TestPythonDispatcher::test_basic PASSED [0.0019s] [ 78%] 2024-12-18T00:10:34.8440382Z test_python_dispatch.py::TestPythonDispatcher::test_lstsq PASSED [0.0042s] [ 79%] 2024-12-18T00:10:34.8440859Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32 PASSED [0.0206s] [ 80%] 2024-12-18T00:10:34.8441325Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu PASSED [0.0038s] [ 81%] 2024-12-18T00:10:34.8441889Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32 PASSED [0.0081s] [ 82%] 2024-12-18T00:10:34.8442473Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32 PASSED [0.0086s] [ 83%] 2024-12-18T00:10:34.8443039Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32 PASSED [0.0078s] [ 84%] 2024-12-18T00:10:34.8443650Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32 PASSED [0.0077s] [ 85%] 2024-12-18T00:10:34.8444217Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32 PASSED [0.0130s] [ 86%] 2024-12-18T00:10:34.8444818Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32 PASSED [0.0077s] [ 87%] 2024-12-18T00:10:34.8445384Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32 PASSED [0.0080s] [ 88%] 2024-12-18T00:10:34.8445990Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32 PASSED [0.0080s] [ 89%] 2024-12-18T00:10:34.8446622Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32 PASSED [0.0083s] [ 90%] 2024-12-18T00:10:34.8447251Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32 PASSED [0.0081s] [ 91%] 2024-12-18T00:10:34.8447876Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32 PASSED [0.0076s] [ 92%] 2024-12-18T00:10:34.8448365Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu PASSED [0.0024s] [ 93%] 2024-12-18T00:10:34.8448859Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32 PASSED [0.0079s] [ 94%] 2024-12-18T00:10:34.8449393Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32 PASSED [0.0083s] [ 95%] 2024-12-18T00:10:34.8449859Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu PASSED [0.0024s] [ 96%] 2024-12-18T00:10:34.8450344Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32 PASSED [0.0077s] [ 97%] 2024-12-18T00:10:34.8450880Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32 PASSED [0.0082s] [ 98%] 2024-12-18T00:10:34.8451363Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 PASSED [0.0076s] [100%] 2024-12-18T00:10:34.8451368Z 2024-12-18T00:10:34.8451972Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-e7eb1f67706a506d.xml - 2024-12-18T00:10:34.8452140Z ====================== 96 passed, 23 deselected in 0.57s ======================= 2024-12-18T00:10:34.8452687Z The following tests failed and then succeeded when run in a new process['test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint'] 2024-12-18T00:10:34.8452696Z 2024-12-18T00:10:34.8453107Z FINISHED PRINTING LOG FILE of test_python_dispatch 1/1 (test/test-reports/test_python_dispatch_1.1_0033ef2426482c3c_.log) 2024-12-18T00:10:34.8453112Z 2024-12-18T00:10:34.8453321Z Running test_mobile_optimizer 1/1 ... [2024-12-18 00:10:34.595705] 2024-12-18T00:10:34.8453434Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:10:34.8454293Z 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'] ... [2024-12-18 00:10:34.596009] 2024-12-18T00:10:40.3177981Z 2024-12-18T00:10:40.3178937Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_f665825be2fa42b3_.log 2024-12-18T00:10:40.3182171Z 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 2024-12-18T00:10:40.3186213Z 2024-12-18T00:10:40.3186432Z Running nn/test_convolution 1/1 ... [2024-12-18 00:10:40.317923] 2024-12-18T00:10:40.3186862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:10:40.3187928Z 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'] ... [2024-12-18 00:10:40.318202] 2024-12-18T00:11:02.8147393Z 2024-12-18T00:11:02.8148567Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_baeaa5342077aa8d_.log 2024-12-18T00:11:02.8486852Z 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, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, 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 2024-12-18T00:11:02.8833844Z 2024-12-18T00:11:02.8834025Z Running test_nn 1/1 ... [2024-12-18 00:11:02.815816] 2024-12-18T00:11:02.8834411Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:11:02.8835423Z 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'] ... [2024-12-18 00:11:02.816115] 2024-12-18T00:13:18.6002367Z 2024-12-18T00:13:18.6004645Z test_nn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.1_bd115f3a06286ddc_.log 2024-12-18T00:13:18.7169294Z Running 2179 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_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_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_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_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_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_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_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_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 2024-12-18T00:13:18.8303970Z 2024-12-18T00:13:18.8304270Z Running test_multiprocessing_spawn 1/1 ... [2024-12-18 00:13:18.603575] 2024-12-18T00:13:18.8304749Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:13:18.8305863Z 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'] ... [2024-12-18 00:13:18.603904] 2024-12-18T00:15:29.4819873Z 2024-12-18T00:15:29.4820947Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_da912da716890ae1_.log 2024-12-18T00:15:29.4833629Z 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 2024-12-18T00:15:29.4845371Z 2024-12-18T00:15:29.4845553Z Running test_overrides 1/1 ... [2024-12-18 00:15:29.481843] 2024-12-18T00:15:29.4846011Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:15:29.4847127Z 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'] ... [2024-12-18 00:15:29.482136] 2024-12-18T00:15:36.8559915Z 2024-12-18T00:15:36.8560780Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_b031959ae77ef42f_.log 2024-12-18T00:15:36.9055722Z Running 1466 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__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mtia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_nested___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_quantized___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse_csr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_vulkan___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xla___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_itemsize___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_layout___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mH___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mT___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_name___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_names___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_nbytes___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_ndim___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_output_nr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_real___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_requires_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_retains_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_shape___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_volatile___get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___add__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___and__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array_wrap__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___bool__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___complex__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___contains__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___cuda_array_interface_____get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___deepcopy__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___div__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___dlpack__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___dlpack_device__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___eq__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___float__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___floordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___format__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ge__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___getitem__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___gt__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___iadd__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___iand__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___idiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ifloordiv__, 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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, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, 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_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, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_spherical_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlog1py, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlogy, 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, 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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, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, 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, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, 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, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, 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 2024-12-18T00:15:36.9536214Z 2024-12-18T00:15:36.9536506Z Running distributions/test_distributions 1/1 ... [2024-12-18 00:15:36.857656] 2024-12-18T00:15:36.9537016Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:15:36.9538170Z 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'] ... [2024-12-18 00:15:36.857967] 2024-12-18T00:16:22.3822556Z 2024-12-18T00:16:22.3823704Z distributions/test_distributions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.1_b0928c995b94e8b9_.log 2024-12-18T00:16:22.3912741Z Running 225 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_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 2024-12-18T00:16:22.4000363Z 2024-12-18T00:16:22.4000602Z Running test_autoload_disable 1/1 ... [2024-12-18 00:16:22.382655] 2024-12-18T00:16:24.8480453Z running install 2024-12-18T00:16:24.8481540Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:16:24.8482365Z !! 2024-12-18T00:16:24.8482484Z 2024-12-18T00:16:24.8482637Z ******************************************************************************** 2024-12-18T00:16:24.8483023Z Please avoid running ``setup.py`` directly. 2024-12-18T00:16:24.8483436Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:16:24.8483831Z standards-based tools. 2024-12-18T00:16:24.8484030Z 2024-12-18T00:16:24.8484346Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:16:24.8484890Z ******************************************************************************** 2024-12-18T00:16:24.8485152Z 2024-12-18T00:16:24.8485241Z !! 2024-12-18T00:16:24.8485468Z self.initialize_options() 2024-12-18T00:16:24.8613573Z running build 2024-12-18T00:16:24.8613827Z running build_py 2024-12-18T00:16:24.8688973Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:16:24.8691469Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:16:24.8694468Z running build_ext 2024-12-18T00:16:24.9511726Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:16:24.9512711Z creating build/temp.linux-x86_64-cpython-313 2024-12-18T00:16:24.9517766Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:16:26.4825238Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T00:16:26.4826314Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T00:16:26.4827196Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2024-12-18T00:16:26.4827763Z from extension.cpp:1: 2024-12-18T00:16:26.4829055Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T00:16:26.4829916Z extension.cpp:45:53: required from here 2024-12-18T00:16:26.4831386Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T00:16:26.4832984Z 1539 | class class_ : public detail::generic_type { 2024-12-18T00:16:26.4833374Z | ^~~~~~ 2024-12-18T00:16:26.4835389Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T00:16:26.4836788Z extension.cpp:45:53: required from here 2024-12-18T00:16:26.4840127Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2024-12-18T00:16:26.4842934Z 1599 | with_internals([&](internals &internals) { 2024-12-18T00:16:26.4843331Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:26.4843852Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T00:16:26.4844460Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:26.4844933Z 1601 | : internals.registered_types_cpp; 2024-12-18T00:16:26.4845363Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:26.4845801Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T00:16:26.4846214Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:26.4846622Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T00:16:26.4847037Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:26.4847375Z 1604 | }); 2024-12-18T00:16:26.4847627Z | ~ 2024-12-18T00:16:26.4850836Z 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 2024-12-18T00:16:26.8873946Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:16:26.8878597Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:16:28.0710605Z 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 2024-12-18T00:16:28.4524650Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:16:28.4528871Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:16:29.6622196Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:29.6623289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:29.6624131Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:29.6625052Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:29.6625738Z from rng_extension.cpp:6: 2024-12-18T00:16:29.6626561Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:29.6627393Z 1123 | # pragma unroll 2024-12-18T00:16:29.6627655Z | 2024-12-18T00:16:29.6628210Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T00:16:29.6629129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:29.6629953Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:29.6630806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:29.6631795Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:29.6632479Z from rng_extension.cpp:6: 2024-12-18T00:16:29.6633273Z /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] 2024-12-18T00:16:29.6634238Z 59 | #pragma unroll 2024-12-18T00:16:29.6634562Z | 2024-12-18T00:16:29.6635251Z /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] 2024-12-18T00:16:29.6636029Z 72 | #pragma unroll 2024-12-18T00:16:29.6636282Z | 2024-12-18T00:16:29.6637062Z /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] 2024-12-18T00:16:29.6637821Z 87 | #pragma unroll 2024-12-18T00:16:29.6638077Z | 2024-12-18T00:16:29.6638724Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T00:16:29.6639637Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:29.6640459Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:29.6641260Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:29.6642180Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:29.6642850Z from rng_extension.cpp:6: 2024-12-18T00:16:29.6643666Z /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] 2024-12-18T00:16:29.6644471Z 153 | #pragma unroll 2024-12-18T00:16:29.6644724Z | 2024-12-18T00:16:29.6647877Z 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 2024-12-18T00:16:30.0704198Z running install_lib 2024-12-18T00:16:30.0784337Z 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 2024-12-18T00:16:30.0883804Z 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 2024-12-18T00:16:30.0979242Z 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 2024-12-18T00:16:30.1080240Z running install_egg_info 2024-12-18T00:16:30.1252768Z running egg_info 2024-12-18T00:16:30.1321426Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:16:30.1324405Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:16:30.1326259Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:16:30.1327910Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:16:30.1400252Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:16:30.1408565Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:16:30.1409921Z 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) 2024-12-18T00:16:30.1411494Z 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 2024-12-18T00:16:30.1417627Z running install_scripts 2024-12-18T00:16:33.2272760Z 2024-12-18T00:16:33.2273336Z Running tests... 2024-12-18T00:16:33.2273865Z ---------------------------------------------------------------------- 2024-12-18T00:16:33.2405429Z . 2024-12-18T00:16:33.2406429Z ---------------------------------------------------------------------- 2024-12-18T00:16:33.2406858Z Ran 1 test in 0.013s 2024-12-18T00:16:33.2407021Z 2024-12-18T00:16:33.2407114Z OK 2024-12-18T00:16:33.2407236Z 2024-12-18T00:16:33.2407348Z Generating XML reports... 2024-12-18T00:16:33.8225848Z Running doctests 1/1 ... [2024-12-18 00:16:33.822283] 2024-12-18T00:16:33.9118239Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2024-12-18T00:16:33.9118841Z Listing tests 2024-12-18T00:16:34.1013806Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1496. 2024-12-18T00:16:34.1014727Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1015143Z 2024-12-18T00:16:34.1015280Z dim_order(ambiguity_check=False) -> tuple 2024-12-18T00:16:34.1015528Z 2024-12-18T00:16:34.1015765Z Returns the uniquely determined tuple of int describing the dim order or 2024-12-18T00:16:34.1016254Z physical layout of :attr:`self`. 2024-12-18T00:16:34.1016455Z 2024-12-18T00:16:34.1016661Z The dim order represents how dimensions are laid out in memory, 2024-12-18T00:16:34.1017131Z starting from the outermost to the innermost dimension. 2024-12-18T00:16:34.1017422Z 2024-12-18T00:16:34.1017609Z Note that the dim order may not always be uniquely determined. 2024-12-18T00:16:34.1018307Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2024-12-18T00:16:34.1019214Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2024-12-18T00:16:34.1019999Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2024-12-18T00:16:34.1020706Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2024-12-18T00:16:34.1021270Z Otherwise, it will raise TypeError. 2024-12-18T00:16:34.1021481Z 2024-12-18T00:16:34.1021570Z Args: 2024-12-18T00:16:34.1022018Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2024-12-18T00:16:34.1022469Z 2024-12-18T00:16:34.1022583Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2024-12-18T00:16:34.1022900Z (0, 1, 2, 3) 2024-12-18T00:16:34.1023199Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2024-12-18T00:16:34.1023553Z (0, 2, 1, 3) 2024-12-18T00:16:34.1023901Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2024-12-18T00:16:34.1024334Z (0, 2, 3, 1) 2024-12-18T00:16:34.1024586Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2024-12-18T00:16:34.1024907Z (0, 1, 2, 3) 2024-12-18T00:16:34.1025140Z >>> try: 2024-12-18T00:16:34.1025451Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2024-12-18T00:16:34.1025855Z ... except RuntimeError as e: 2024-12-18T00:16:34.1026144Z ... print(e) 2024-12-18T00:16:34.1026624Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2024-12-18T00:16:34.1027179Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2024-12-18T00:16:34.1027617Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2024-12-18T00:16:34.1028085Z ... ) # It can be mapped to contiguous format 2024-12-18T00:16:34.1028424Z (0, 1, 2, 3) 2024-12-18T00:16:34.1028646Z >>> try: 2024-12-18T00:16:34.1028973Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2024-12-18T00:16:34.1029391Z ... except TypeError as e: 2024-12-18T00:16:34.1029880Z ... print(e) 2024-12-18T00:16:34.1030324Z The ambiguity_check argument must be a bool or a list of memory formats. 2024-12-18T00:16:34.1030782Z .. warning:: 2024-12-18T00:16:34.1031115Z The dim_order tensor API is experimental and subject to change. 2024-12-18T00:16:34.1031439Z 2024-12-18T00:16:34.1031744Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1032114Z 2024-12-18T00:16:34.1547282Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=431. 2024-12-18T00:16:34.1548171Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1548789Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-12-18T00:16:34.1549169Z 2024-12-18T00:16:34.1549357Z This is helpful when you want to visualize data over some 2024-12-18T00:16:34.1549806Z range of inputs. See below for a plotting example. 2024-12-18T00:16:34.1550077Z 2024-12-18T00:16:34.1550247Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-12-18T00:16:34.1550720Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-12-18T00:16:34.1551222Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-12-18T00:16:34.1551697Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-12-18T00:16:34.1552164Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-12-18T00:16:34.1552580Z to the result shape. 2024-12-18T00:16:34.1552761Z 2024-12-18T00:16:34.1552865Z .. note:: 2024-12-18T00:16:34.1553175Z 0D inputs are treated equivalently to 1D inputs of a 2024-12-18T00:16:34.1553555Z single element. 2024-12-18T00:16:34.1553727Z 2024-12-18T00:16:34.1553832Z .. warning:: 2024-12-18T00:16:34.1554173Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-12-18T00:16:34.1554630Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-12-18T00:16:34.1554917Z 2024-12-18T00:16:34.1555070Z In the future `torch.meshgrid` will transition to 2024-12-18T00:16:34.1555451Z `indexing='xy'` as the default. 2024-12-18T00:16:34.1555682Z 2024-12-18T00:16:34.1555873Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-12-18T00:16:34.1556356Z this issue with the goal of migrating to NumPy's behavior. 2024-12-18T00:16:34.1556650Z 2024-12-18T00:16:34.1556753Z .. seealso:: 2024-12-18T00:16:34.1556900Z 2024-12-18T00:16:34.1557072Z :func:`torch.cartesian_prod` has the same effect but it 2024-12-18T00:16:34.1557495Z collects the data in a tensor of vectors. 2024-12-18T00:16:34.1557749Z 2024-12-18T00:16:34.1557837Z Args: 2024-12-18T00:16:34.1558240Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-12-18T00:16:34.1558874Z treated as tensors of size :math:`(1,)` automatically 2024-12-18T00:16:34.1559159Z 2024-12-18T00:16:34.1559353Z indexing: (str, optional): the indexing mode, either "xy" 2024-12-18T00:16:34.1559811Z or "ij", defaults to "ij". See warning for future changes. 2024-12-18T00:16:34.1560111Z 2024-12-18T00:16:34.1560267Z If "xy" is selected, the first dimension corresponds 2024-12-18T00:16:34.1560717Z to the cardinality of the second input and the second 2024-12-18T00:16:34.1561176Z dimension corresponds to the cardinality of the first 2024-12-18T00:16:34.1561560Z input. 2024-12-18T00:16:34.1561712Z 2024-12-18T00:16:34.1561873Z If "ij" is selected, the dimensions are in the same 2024-12-18T00:16:34.1562258Z order as the cardinality of the inputs. 2024-12-18T00:16:34.1562511Z 2024-12-18T00:16:34.1562600Z Returns: 2024-12-18T00:16:34.1562909Z seq (sequence of Tensors): If the input has :math:`N` 2024-12-18T00:16:34.1563494Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-12-18T00:16:34.1563962Z output will also have :math:`N` tensors, where each tensor 2024-12-18T00:16:34.1564467Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-12-18T00:16:34.1564797Z 2024-12-18T00:16:34.1564944Z Example:: 2024-12-18T00:16:34.1565166Z 2024-12-18T00:16:34.1565461Z >>> x = torch.tensor([1, 2, 3]) 2024-12-18T00:16:34.1566079Z >>> y = torch.tensor([4, 5, 6]) 2024-12-18T00:16:34.1566306Z 2024-12-18T00:16:34.1566517Z Observe the element-wise pairings across the grid, (1, 4), 2024-12-18T00:16:34.1566954Z (1, 5), ..., (3, 6). This is the same thing as the 2024-12-18T00:16:34.1567308Z cartesian product. 2024-12-18T00:16:34.1567649Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-12-18T00:16:34.1568021Z >>> grid_x 2024-12-18T00:16:34.1568277Z tensor([[1, 1, 1], 2024-12-18T00:16:34.1568553Z [2, 2, 2], 2024-12-18T00:16:34.1568821Z [3, 3, 3]]) 2024-12-18T00:16:34.1569077Z >>> grid_y 2024-12-18T00:16:34.1569325Z tensor([[4, 5, 6], 2024-12-18T00:16:34.1569598Z [4, 5, 6], 2024-12-18T00:16:34.1569867Z [4, 5, 6]]) 2024-12-18T00:16:34.1570040Z 2024-12-18T00:16:34.1570226Z This correspondence can be seen when these grids are 2024-12-18T00:16:34.1570606Z stacked properly. 2024-12-18T00:16:34.1570999Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-12-18T00:16:34.1571457Z ... torch.cartesian_prod(x, y)) 2024-12-18T00:16:34.1571790Z True 2024-12-18T00:16:34.1571927Z 2024-12-18T00:16:34.1572117Z `torch.meshgrid` is commonly used to produce a grid for 2024-12-18T00:16:34.1572493Z plotting. 2024-12-18T00:16:34.1572787Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-12-18T00:16:34.1573167Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-12-18T00:16:34.1573538Z >>> import matplotlib.pyplot as plt 2024-12-18T00:16:34.1574089Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-12-18T00:16:34.1574443Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-12-18T00:16:34.1574825Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-12-18T00:16:34.1575220Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-12-18T00:16:34.1575592Z >>> ax = plt.axes(projection='3d') 2024-12-18T00:16:34.1575984Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-12-18T00:16:34.1576357Z >>> plt.show() 2024-12-18T00:16:34.1576528Z 2024-12-18T00:16:34.1576657Z .. image:: ../_static/img/meshgrid.png 2024-12-18T00:16:34.1576986Z :width: 512 2024-12-18T00:16:34.1577154Z 2024-12-18T00:16:34.1577242Z 2024-12-18T00:16:34.1577624Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1577995Z 2024-12-18T00:16:34.1578533Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=820. 2024-12-18T00:16:34.1579400Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1580173Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-12-18T00:16:34.1580673Z 2024-12-18T00:16:34.1580839Z Returns the unique elements of the input tensor. 2024-12-18T00:16:34.1581099Z 2024-12-18T00:16:34.1581408Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-12-18T00:16:34.1582035Z this function also eliminates non-consecutive duplicate values. 2024-12-18T00:16:34.1582358Z 2024-12-18T00:16:34.1582590Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-12-18T00:16:34.1583369Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-12-18T00:16:34.1584071Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-12-18T00:16:34.1584654Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-12-18T00:16:34.1584944Z 2024-12-18T00:16:34.1585085Z Args: 2024-12-18T00:16:34.1585371Z input (Tensor): the input tensor 2024-12-18T00:16:34.1585800Z sorted (bool): Whether to sort the unique elements in ascending order 2024-12-18T00:16:34.1586252Z before returning as output. 2024-12-18T00:16:34.1586680Z return_inverse (bool): Whether to also return the indices for where 2024-12-18T00:16:34.1587230Z elements in the original input ended up in the returned unique list. 2024-12-18T00:16:34.1587793Z return_counts (bool): Whether to also return the counts for each unique 2024-12-18T00:16:34.1588232Z element. 2024-12-18T00:16:34.1588582Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-12-18T00:16:34.1589116Z unique of the flattened input is returned. Otherwise, each of the 2024-12-18T00:16:34.1589648Z tensors indexed by the given dimension is treated as one of the 2024-12-18T00:16:34.1590185Z elements to apply the unique operation upon. See examples for more 2024-12-18T00:16:34.1590630Z details. Default: ``None`` 2024-12-18T00:16:34.1590838Z 2024-12-18T00:16:34.1590930Z Returns: 2024-12-18T00:16:34.1591352Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-12-18T00:16:34.1591771Z 2024-12-18T00:16:34.1591966Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-12-18T00:16:34.1592428Z - **inverse_indices** (*Tensor*): (optional) if 2024-12-18T00:16:34.1592872Z :attr:`return_inverse` is True, there will be an additional 2024-12-18T00:16:34.1593386Z returned tensor (same shape as input) representing the indices 2024-12-18T00:16:34.1593901Z for where elements in the original input map to in the output; 2024-12-18T00:16:34.1594408Z otherwise, this function will only return a single tensor. 2024-12-18T00:16:34.1594842Z - **counts** (*Tensor*): (optional) if 2024-12-18T00:16:34.1595262Z :attr:`return_counts` is True, there will be an additional 2024-12-18T00:16:34.1595751Z returned tensor (same shape as output or output.size(dim), 2024-12-18T00:16:34.1596248Z if dim was specified) representing the number of occurrences 2024-12-18T00:16:34.1596664Z for each unique value or tensor. 2024-12-18T00:16:34.1596901Z 2024-12-18T00:16:34.1596995Z Example:: 2024-12-18T00:16:34.1597137Z 2024-12-18T00:16:34.1597351Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-12-18T00:16:34.1597784Z >>> output 2024-12-18T00:16:34.1598029Z tensor([1, 2, 3]) 2024-12-18T00:16:34.1598195Z 2024-12-18T00:16:34.1598322Z >>> output, inverse_indices = torch.unique( 2024-12-18T00:16:34.1598901Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T00:16:34.1599364Z >>> output 2024-12-18T00:16:34.1599613Z tensor([1, 2, 3]) 2024-12-18T00:16:34.1599891Z >>> inverse_indices 2024-12-18T00:16:34.1600171Z tensor([0, 2, 1, 2]) 2024-12-18T00:16:34.1600344Z 2024-12-18T00:16:34.1600469Z >>> output, inverse_indices = torch.unique( 2024-12-18T00:16:34.1600953Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T00:16:34.1601402Z >>> output 2024-12-18T00:16:34.1601643Z tensor([1, 2, 3]) 2024-12-18T00:16:34.1601909Z >>> inverse_indices 2024-12-18T00:16:34.1602167Z tensor([[0, 2], 2024-12-18T00:16:34.1602466Z [1, 2]]) 2024-12-18T00:16:34.1602667Z 2024-12-18T00:16:34.1602770Z >>> a = torch.tensor([ 2024-12-18T00:16:34.1603046Z ... [ 2024-12-18T00:16:34.1603285Z ... [1, 1, 0, 0], 2024-12-18T00:16:34.1603554Z ... [1, 1, 0, 0], 2024-12-18T00:16:34.1603834Z ... [0, 0, 1, 1], 2024-12-18T00:16:34.1604112Z ... ], 2024-12-18T00:16:34.1604392Z ... [ 2024-12-18T00:16:34.1604655Z ... [0, 0, 1, 1], 2024-12-18T00:16:34.1604922Z ... [0, 0, 1, 1], 2024-12-18T00:16:34.1605200Z ... [1, 1, 1, 1], 2024-12-18T00:16:34.1605473Z ... ], 2024-12-18T00:16:34.1605703Z ... [ 2024-12-18T00:16:34.1605923Z ... [1, 1, 0, 0], 2024-12-18T00:16:34.1606202Z ... [1, 1, 0, 0], 2024-12-18T00:16:34.1606480Z ... [0, 0, 1, 1], 2024-12-18T00:16:34.1606751Z ... ], 2024-12-18T00:16:34.1606982Z ... ]) 2024-12-18T00:16:34.1607113Z 2024-12-18T00:16:34.1607340Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-12-18T00:16:34.1607873Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-12-18T00:16:34.1608346Z >>> # each other, so one of them will be removed. 2024-12-18T00:16:34.1608705Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-12-18T00:16:34.1609019Z tensor(True) 2024-12-18T00:16:34.1609302Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-12-18T00:16:34.1609625Z >>> a_unique_dim0 2024-12-18T00:16:34.1609893Z tensor([[[0, 0, 1, 1], 2024-12-18T00:16:34.1610168Z [0, 0, 1, 1], 2024-12-18T00:16:34.1610437Z [1, 1, 1, 1]], 2024-12-18T00:16:34.1610711Z [[1, 1, 0, 0], 2024-12-18T00:16:34.1610968Z [1, 1, 0, 0], 2024-12-18T00:16:34.1611238Z [0, 0, 1, 1]]]) 2024-12-18T00:16:34.1611443Z 2024-12-18T00:16:34.1611659Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-12-18T00:16:34.1612103Z >>> # `a_unique_dim0`: 2024-12-18T00:16:34.1612414Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-12-18T00:16:34.1612736Z tensor(True) 2024-12-18T00:16:34.1613020Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-12-18T00:16:34.1613355Z tensor(True) 2024-12-18T00:16:34.1613506Z 2024-12-18T00:16:34.1613728Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-12-18T00:16:34.1614250Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-12-18T00:16:34.1614664Z >>> # them will be removed. 2024-12-18T00:16:34.1614963Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-12-18T00:16:34.1615276Z tensor(True) 2024-12-18T00:16:34.1615544Z >>> torch.unique(a, dim=1) 2024-12-18T00:16:34.1615848Z tensor([[[0, 0, 1, 1], 2024-12-18T00:16:34.1616110Z [1, 1, 0, 0]], 2024-12-18T00:16:34.1616392Z [[1, 1, 1, 1], 2024-12-18T00:16:34.1616661Z [0, 0, 1, 1]], 2024-12-18T00:16:34.1616934Z [[0, 0, 1, 1], 2024-12-18T00:16:34.1617207Z [1, 1, 0, 0]]]) 2024-12-18T00:16:34.1617385Z 2024-12-18T00:16:34.1617608Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-12-18T00:16:34.1618096Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-12-18T00:16:34.1618557Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-12-18T00:16:34.1618968Z >>> # sub-tensors will be removed. 2024-12-18T00:16:34.1619297Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-12-18T00:16:34.1619601Z tensor(True) 2024-12-18T00:16:34.1619848Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-12-18T00:16:34.1620152Z tensor(True) 2024-12-18T00:16:34.1620410Z >>> torch.unique(a, dim=2) 2024-12-18T00:16:34.1620755Z tensor([[[0, 1], 2024-12-18T00:16:34.1621047Z [0, 1], 2024-12-18T00:16:34.1621286Z [1, 0]], 2024-12-18T00:16:34.1621542Z [[1, 0], 2024-12-18T00:16:34.1621802Z [1, 0], 2024-12-18T00:16:34.1622054Z [1, 1]], 2024-12-18T00:16:34.1622313Z [[0, 1], 2024-12-18T00:16:34.1622555Z [0, 1], 2024-12-18T00:16:34.1622843Z [1, 0]]]) 2024-12-18T00:16:34.1623129Z 2024-12-18T00:16:34.1623503Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1623872Z 2024-12-18T00:16:34.1726889Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560. 2024-12-18T00:16:34.1727771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1728161Z 2024-12-18T00:16:34.1728355Z Load a model from a github repo or a local directory. 2024-12-18T00:16:34.1728629Z 2024-12-18T00:16:34.1728863Z Note: Loading a model is the typical use case, but this can also be used to 2024-12-18T00:16:34.1729425Z for loading other objects such as tokenizers, loss functions, etc. 2024-12-18T00:16:34.1729757Z 2024-12-18T00:16:34.1729927Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-12-18T00:16:34.1730389Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-12-18T00:16:34.1730780Z ref (a tag or a branch). 2024-12-18T00:16:34.1730947Z 2024-12-18T00:16:34.1731128Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-12-18T00:16:34.1731510Z path to a local directory. 2024-12-18T00:16:34.1731698Z 2024-12-18T00:16:34.1731784Z Args: 2024-12-18T00:16:34.1732041Z repo_or_dir (str): If ``source`` is 'github', 2024-12-18T00:16:34.1732574Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-12-18T00:16:34.1733351Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-12-18T00:16:34.1734038Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-12-18T00:16:34.1734617Z If ``source`` is 'local' then it should be a path to a local directory. 2024-12-18T00:16:34.1735133Z model (str): the name of a callable (entrypoint) defined in the 2024-12-18T00:16:34.1735552Z repo/dir's ``hubconf.py``. 2024-12-18T00:16:34.1735959Z *args (optional): the corresponding args for callable ``model``. 2024-12-18T00:16:34.1736455Z source (str, optional): 'github' or 'local'. Specifies how 2024-12-18T00:16:34.1736906Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-12-18T00:16:34.1737410Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-12-18T00:16:34.1738042Z This parameter was introduced in v1.12 and helps ensuring that users 2024-12-18T00:16:34.1738518Z only run code from repos that they trust. 2024-12-18T00:16:34.1738759Z 2024-12-18T00:16:34.1738967Z - If ``False``, a prompt will ask the user whether the repo should 2024-12-18T00:16:34.1739374Z be trusted. 2024-12-18T00:16:34.1739710Z - If ``True``, the repo will be added to the trusted list and loaded 2024-12-18T00:16:34.1740151Z without requiring explicit confirmation. 2024-12-18T00:16:34.1740580Z - If ``"check"``, the repo will be checked against the list of 2024-12-18T00:16:34.1741082Z trusted repos in the cache. If it is not present in that list, the 2024-12-18T00:16:34.1741611Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-12-18T00:16:34.1742112Z - If ``None``: this will raise a warning, inviting the user to set 2024-12-18T00:16:34.1742593Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-12-18T00:16:34.1743108Z is only present for backward compatibility and will be removed in 2024-12-18T00:16:34.1743525Z v2.0. 2024-12-18T00:16:34.1743771Z 2024-12-18T00:16:34.1744040Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-12-18T00:16:34.1744586Z force_reload (bool, optional): whether to force a fresh download of 2024-12-18T00:16:34.1745112Z the github repo unconditionally. Does not have any effect if 2024-12-18T00:16:34.1745534Z ``source = 'local'``. Default is ``False``. 2024-12-18T00:16:34.1746052Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-12-18T00:16:34.1746595Z local caches. Note that the message about first download cannot be 2024-12-18T00:16:34.1747102Z muted. Does not have any effect if ``source = 'local'``. 2024-12-18T00:16:34.1747493Z Default is ``True``. 2024-12-18T00:16:34.1747958Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-12-18T00:16:34.1748664Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-12-18T00:16:34.1749339Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-12-18T00:16:34.1749917Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-12-18T00:16:34.1750439Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-12-18T00:16:34.1750769Z 2024-12-18T00:16:34.1750872Z Returns: 2024-12-18T00:16:34.1751177Z The output of the ``model`` callable when called with the given 2024-12-18T00:16:34.1751586Z ``*args`` and ``**kwargs``. 2024-12-18T00:16:34.1751783Z 2024-12-18T00:16:34.1751872Z Example: 2024-12-18T00:16:34.1752134Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T00:16:34.1752487Z >>> # from a github repo 2024-12-18T00:16:34.1752764Z >>> repo = "pytorch/vision" 2024-12-18T00:16:34.1753061Z >>> model = torch.hub.load( 2024-12-18T00:16:34.1753444Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-12-18T00:16:34.1753836Z ... ) 2024-12-18T00:16:34.1754076Z >>> # from a local directory 2024-12-18T00:16:34.1754389Z >>> path = "/some/local/path/pytorch/vision" 2024-12-18T00:16:34.1754739Z >>> # xdoctest: +SKIP 2024-12-18T00:16:34.1755153Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-12-18T00:16:34.1755514Z 2024-12-18T00:16:34.1755781Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1756151Z 2024-12-18T00:16:34.1756672Z 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=687. 2024-12-18T00:16:34.1757537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1758072Z Download object at the given URL to a local path. 2024-12-18T00:16:34.1758330Z 2024-12-18T00:16:34.1758499Z Args: 2024-12-18T00:16:34.1758757Z url (str): URL of the object to download 2024-12-18T00:16:34.1759228Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-12-18T00:16:34.1759929Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-12-18T00:16:34.1760484Z Default: None 2024-12-18T00:16:34.1760908Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-12-18T00:16:34.1761372Z Default: True 2024-12-18T00:16:34.1761549Z 2024-12-18T00:16:34.1761641Z Example: 2024-12-18T00:16:34.1761914Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T00:16:34.1762278Z >>> # xdoctest: +REQUIRES(POSIX) 2024-12-18T00:16:34.1762616Z >>> torch.hub.download_url_to_file( 2024-12-18T00:16:34.1763058Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-12-18T00:16:34.1763504Z ... "/tmp/temporary_file", 2024-12-18T00:16:34.1763812Z ... ) 2024-12-18T00:16:34.1763938Z 2024-12-18T00:16:34.1764034Z 2024-12-18T00:16:34.1764447Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1764841Z 2024-12-18T00:16:34.1765347Z 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=812. 2024-12-18T00:16:34.1766203Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1766791Z Loads the Torch serialized object at the given URL. 2024-12-18T00:16:34.1767066Z 2024-12-18T00:16:34.1767247Z If downloaded file is a zip file, it will be automatically 2024-12-18T00:16:34.1767644Z decompressed. 2024-12-18T00:16:34.1767788Z 2024-12-18T00:16:34.1768018Z If the object is already present in `model_dir`, it's deserialized and 2024-12-18T00:16:34.1768449Z returned. 2024-12-18T00:16:34.1768785Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-12-18T00:16:34.1769321Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-12-18T00:16:34.1769645Z 2024-12-18T00:16:34.1769732Z Args: 2024-12-18T00:16:34.1769982Z url (str): URL of the object to download 2024-12-18T00:16:34.1770418Z model_dir (str, optional): directory in which to save the object 2024-12-18T00:16:34.1771075Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-12-18T00:16:34.1771820Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-12-18T00:16:34.1772294Z Default: True 2024-12-18T00:16:34.1772799Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-12-18T00:16:34.1773472Z ``filename-.ext`` where ```` is the first eight or more 2024-12-18T00:16:34.1774219Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-12-18T00:16:34.1774753Z ensure unique names and to verify the contents of the file. 2024-12-18T00:16:34.1775175Z Default: False 2024-12-18T00:16:34.1775690Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-12-18T00:16:34.1776476Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-12-18T00:16:34.1777184Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-12-18T00:16:34.1777560Z 2024-12-18T00:16:34.1777661Z Example: 2024-12-18T00:16:34.1777921Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T00:16:34.1778323Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-12-18T00:16:34.1778803Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-12-18T00:16:34.1779225Z ... ) 2024-12-18T00:16:34.1779354Z 2024-12-18T00:16:34.1779451Z 2024-12-18T00:16:34.1779810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1780192Z 2024-12-18T00:16:34.1807288Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=360. 2024-12-18T00:16:34.1808147Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:34.1808754Z Registers the function implementation as the fallback for the given key. 2024-12-18T00:16:34.1809124Z 2024-12-18T00:16:34.1809344Z This function only works for a library with global namespace ("_"). 2024-12-18T00:16:34.1809676Z 2024-12-18T00:16:34.1809775Z Args: 2024-12-18T00:16:34.1810187Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-12-18T00:16:34.1810691Z to register a fallthrough. 2024-12-18T00:16:34.1811237Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-12-18T00:16:34.1811847Z the dispatch key that the library was created with. 2024-12-18T00:16:34.1812665Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-12-18T00:16:34.1813486Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-12-18T00:16:34.1813925Z 2024-12-18T00:16:34.1814082Z Example:: 2024-12-18T00:16:34.1814371Z >>> my_lib = Library("_", "IMPL") 2024-12-18T00:16:34.1814744Z >>> def fallback_kernel(op, *args, **kwargs): 2024-12-18T00:16:34.1815125Z >>> # Handle all autocast ops generically 2024-12-18T00:16:34.1815463Z >>> # ... 2024-12-18T00:16:34.1815773Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T00:16:34.1816124Z 2024-12-18T00:16:34.1816864Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-12-18T00:16:34.1817619Z 2024-12-18T00:16:34.1817752Z my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T00:16:34.1818083Z ^ 2024-12-18T00:16:34.1869562Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=725. 2024-12-18T00:16:34.1870598Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:34.1871198Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-12-18T00:16:34.1871540Z 2024-12-18T00:16:34.1871734Z Also sometimes known as a "meta kernel", "abstract impl". 2024-12-18T00:16:34.1872024Z 2024-12-18T00:16:34.1872277Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-12-18T00:16:34.1872855Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-12-18T00:16:34.1873437Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-12-18T00:16:34.1874090Z what the properties of the output Tensors are. 2024-12-18T00:16:34.1874364Z 2024-12-18T00:16:34.1874598Z The FakeTensor implementation has the same signature as the operator. 2024-12-18T00:16:34.1875167Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-12-18T00:16:34.1875716Z implementation, assume that all Tensor inputs to the operator are 2024-12-18T00:16:34.1876258Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-12-18T00:16:34.1876788Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-12-18T00:16:34.1877348Z The FakeTensor implementation must consist of only PyTorch operations 2024-12-18T00:16:34.1877897Z (and may not directly access the storage or data of any input or 2024-12-18T00:16:34.1878315Z intermediate Tensors). 2024-12-18T00:16:34.1878552Z 2024-12-18T00:16:34.1878719Z This API may be used as a decorator (see examples). 2024-12-18T00:16:34.1878982Z 2024-12-18T00:16:34.1879127Z For a detailed guide on custom ops, please see 2024-12-18T00:16:34.1879680Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-12-18T00:16:34.1880040Z 2024-12-18T00:16:34.1880172Z Examples: 2024-12-18T00:16:34.1880544Z >>> import torch 2024-12-18T00:16:34.1881047Z >>> import numpy as np 2024-12-18T00:16:34.1881374Z >>> from torch import Tensor 2024-12-18T00:16:34.1881659Z >>> 2024-12-18T00:16:34.1881976Z >>> # Example 1: an operator without data-dependent output shape 2024-12-18T00:16:34.1882507Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-12-18T00:16:34.1883065Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-12-18T00:16:34.1883586Z >>> raise NotImplementedError("Implementation goes here") 2024-12-18T00:16:34.1883964Z >>> 2024-12-18T00:16:34.1884261Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-12-18T00:16:34.1884751Z >>> def _(x, weight, bias): 2024-12-18T00:16:34.1885106Z >>> assert x.dim() == 2 2024-12-18T00:16:34.1885419Z >>> assert weight.dim() == 2 2024-12-18T00:16:34.1885734Z >>> assert bias.dim() == 1 2024-12-18T00:16:34.1886072Z >>> assert x.shape[1] == weight.shape[1] 2024-12-18T00:16:34.1886483Z >>> assert weight.shape[0] == bias.shape[0] 2024-12-18T00:16:34.1886884Z >>> assert x.device == weight.device 2024-12-18T00:16:34.1887215Z >>> 2024-12-18T00:16:34.1887446Z >>> return (x @ weight.t()) + bias 2024-12-18T00:16:34.1887761Z >>> 2024-12-18T00:16:34.1888064Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-12-18T00:16:34.1888459Z >>> x = torch.randn(2, 3) 2024-12-18T00:16:34.1888771Z >>> w = torch.randn(3, 3) 2024-12-18T00:16:34.1889068Z >>> b = torch.randn(3) 2024-12-18T00:16:34.1889405Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-12-18T00:16:34.1889751Z >>> 2024-12-18T00:16:34.1889986Z >>> assert y.shape == (2, 3) 2024-12-18T00:16:34.1890280Z >>> 2024-12-18T00:16:34.1890571Z >>> # Example 2: an operator with data-dependent output shape 2024-12-18T00:16:34.1891084Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-12-18T00:16:34.1891557Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-12-18T00:16:34.1891916Z >>> x_np = x.numpy(force=True) 2024-12-18T00:16:34.1892275Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-12-18T00:16:34.1892662Z >>> return torch.tensor(res, device=x.device) 2024-12-18T00:16:34.1892989Z >>> 2024-12-18T00:16:34.1893293Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-12-18T00:16:34.1893676Z >>> def _(x): 2024-12-18T00:16:34.1893984Z >>> # Number of nonzero-elements is data-dependent. 2024-12-18T00:16:34.1894405Z >>> # Since we cannot peek at the data in an fake impl, 2024-12-18T00:16:34.1894831Z >>> # we use the ctx object to construct a new symint that 2024-12-18T00:16:34.1895237Z >>> # represents the data-dependent size. 2024-12-18T00:16:34.1895595Z >>> ctx = torch.library.get_ctx() 2024-12-18T00:16:34.1895936Z >>> nnz = ctx.new_dynamic_size() 2024-12-18T00:16:34.1896268Z >>> shape = [nnz, x.dim()] 2024-12-18T00:16:34.1896619Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-12-18T00:16:34.1896985Z >>> return result 2024-12-18T00:16:34.1897256Z >>> 2024-12-18T00:16:34.1897566Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T00:16:34.1897953Z >>> 2024-12-18T00:16:34.1898184Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-12-18T00:16:34.1898655Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-12-18T00:16:34.1899133Z >>> trace.print_readable() 2024-12-18T00:16:34.1899428Z >>> 2024-12-18T00:16:34.1899780Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-12-18T00:16:34.1900120Z 2024-12-18T00:16:34.1900204Z 2024-12-18T00:16:34.1900845Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-12-18T00:16:34.1901492Z 2024-12-18T00:16:34.1901579Z _._ = None 2024-12-18T00:16:34.1901791Z ^ 2024-12-18T00:16:34.1902433Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=846. 2024-12-18T00:16:34.1903305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1903834Z Register a backward formula for this custom op. 2024-12-18T00:16:34.1904084Z 2024-12-18T00:16:34.1904301Z In order for an operator to work with autograd, you need to register 2024-12-18T00:16:34.1904713Z a backward formula: 2024-12-18T00:16:34.1905172Z 1. You must tell us how to compute gradients during the backward pass 2024-12-18T00:16:34.1905620Z by providing us a "backward" function. 2024-12-18T00:16:34.1906068Z 2. If you need any values from the forward to compute gradients, you can 2024-12-18T00:16:34.1906554Z use `setup_context` to save values for backward. 2024-12-18T00:16:34.1906806Z 2024-12-18T00:16:34.1907103Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-12-18T00:16:34.1907637Z - ``grads`` is one or more gradients. The number of gradients matches 2024-12-18T00:16:34.1908081Z the number of outputs of the operator. 2024-12-18T00:16:34.1908533Z The ``ctx`` object is `the same ctx object `_ used by 2024-12-18T00:16:34.1909116Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-12-18T00:16:34.1909634Z same as :meth:`torch.autograd.Function.backward`. 2024-12-18T00:16:34.1909898Z 2024-12-18T00:16:34.1910112Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-12-18T00:16:34.1910681Z Please save quantities needed for backward onto the ``ctx`` object via 2024-12-18T00:16:34.1911262Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-12-18T00:16:34.1911820Z or assigning them as attributes of ``ctx``. If your custom op has 2024-12-18T00:16:34.1912360Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-12-18T00:16:34.1912889Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-12-18T00:16:34.1913196Z 2024-12-18T00:16:34.1913416Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-12-18T00:16:34.1913986Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-12-18T00:16:34.1914571Z not depend on or mutate global state. If you need a non-traceable backward, 2024-12-18T00:16:34.1915152Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-12-18T00:16:34.1915500Z 2024-12-18T00:16:34.1915732Z If you need different autograd behavior on different devices, then we 2024-12-18T00:16:34.1916309Z recommend creating two different custom operators, one for each device 2024-12-18T00:16:34.1916893Z that needs different behavior, and switching between them at runtime. 2024-12-18T00:16:34.1917234Z 2024-12-18T00:16:34.1917329Z Examples: 2024-12-18T00:16:34.1917572Z >>> import torch 2024-12-18T00:16:34.1917852Z >>> import numpy as np 2024-12-18T00:16:34.1918154Z >>> from torch import Tensor 2024-12-18T00:16:34.1918554Z >>> 2024-12-18T00:16:34.1918882Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-12-18T00:16:34.1919332Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-12-18T00:16:34.1919681Z >>> x_np = x.cpu().numpy() 2024-12-18T00:16:34.1920000Z >>> y_np = np.sin(x_np) 2024-12-18T00:16:34.1920368Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T00:16:34.1920727Z >>> 2024-12-18T00:16:34.1921014Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-12-18T00:16:34.1921376Z >>> x, = inputs 2024-12-18T00:16:34.1921658Z >>> ctx.save_for_backward(x) 2024-12-18T00:16:34.1921963Z >>> 2024-12-18T00:16:34.1922184Z >>> def backward(ctx, grad): 2024-12-18T00:16:34.1922498Z >>> x, = ctx.saved_tensors 2024-12-18T00:16:34.1922817Z >>> return grad * x.cos() 2024-12-18T00:16:34.1923113Z >>> 2024-12-18T00:16:34.1923361Z >>> torch.library.register_autograd( 2024-12-18T00:16:34.1923763Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-12-18T00:16:34.1924228Z ... ) 2024-12-18T00:16:34.1924453Z >>> 2024-12-18T00:16:34.1924796Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T00:16:34.1925380Z >>> y = numpy_sin(x) 2024-12-18T00:16:34.1925941Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T00:16:34.1926396Z >>> assert torch.allclose(grad_x, x.cos()) 2024-12-18T00:16:34.1926728Z >>> 2024-12-18T00:16:34.1926976Z >>> # Example with a keyword-only arg 2024-12-18T00:16:34.1927414Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T00:16:34.1927910Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-12-18T00:16:34.1928321Z >>> x_np = x.cpu().numpy() 2024-12-18T00:16:34.1928637Z >>> y_np = x_np * val 2024-12-18T00:16:34.1928995Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T00:16:34.1929360Z >>> 2024-12-18T00:16:34.1929701Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-12-18T00:16:34.1930192Z >>> ctx.val = keyword_only_inputs["val"] 2024-12-18T00:16:34.1930530Z >>> 2024-12-18T00:16:34.1930768Z >>> def backward(ctx, grad): 2024-12-18T00:16:34.1931091Z >>> return grad * ctx.val 2024-12-18T00:16:34.1931377Z >>> 2024-12-18T00:16:34.1931627Z >>> torch.library.register_autograd( 2024-12-18T00:16:34.1932039Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-12-18T00:16:34.1932428Z ... ) 2024-12-18T00:16:34.1932648Z >>> 2024-12-18T00:16:34.1932889Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T00:16:34.1933237Z >>> y = numpy_mul(x, val=3.14) 2024-12-18T00:16:34.1933623Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T00:16:34.1934089Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-12-18T00:16:34.1934377Z 2024-12-18T00:16:34.1934475Z 2024-12-18T00:16:34.1934846Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1935213Z 2024-12-18T00:16:34.1935715Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1258. 2024-12-18T00:16:34.1936554Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.1937142Z Given an operator and some sample arguments, tests if the operator is 2024-12-18T00:16:34.1937579Z registered correctly. 2024-12-18T00:16:34.1937763Z 2024-12-18T00:16:34.1937976Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-12-18T00:16:34.1938557Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-12-18T00:16:34.1939146Z and these APIs require that the functions you pass them satisfy certain 2024-12-18T00:16:34.1939719Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-12-18T00:16:34.1940202Z ``opcheck`` tests these metadata and properties. 2024-12-18T00:16:34.1940467Z 2024-12-18T00:16:34.1940582Z Concretely, we test the following: 2024-12-18T00:16:34.1940810Z 2024-12-18T00:16:34.1940986Z - test_schema: If the schema matches the implementation of 2024-12-18T00:16:34.1941513Z the operator. For example: if the schema specifies a Tensor is mutated, 2024-12-18T00:16:34.1942072Z then we check the implementation mutates the Tensor. If the schema 2024-12-18T00:16:34.1942593Z specifies that we return a new Tensor, then we check that the 2024-12-18T00:16:34.1943113Z implementation returns a new Tensor (instead of an existing one or 2024-12-18T00:16:34.1943560Z a view of an existing one). 2024-12-18T00:16:34.1943962Z - test_autograd_registration: If the operator supports training 2024-12-18T00:16:34.1944480Z (autograd): we check that its autograd formula is registered via 2024-12-18T00:16:34.1945011Z torch.library.register_autograd or a manual registration to one 2024-12-18T00:16:34.1945534Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2024-12-18T00:16:34.1945998Z registrations may lead to undefined behavior. 2024-12-18T00:16:34.1946494Z - test_faketensor: If the operator has a FakeTensor kernel 2024-12-18T00:16:34.1947000Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-12-18T00:16:34.1947512Z but not sufficient) for the operator to work with PyTorch compilation 2024-12-18T00:16:34.1948069Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2024-12-18T00:16:34.1948612Z (also sometimes known as a meta kernel) was registered for the 2024-12-18T00:16:34.1949150Z operator and that it is correct. This test takes the result of 2024-12-18T00:16:34.1949661Z running the operator on real tensors and the result of running 2024-12-18T00:16:34.1950174Z the operator on FakeTensors and checks that they have the same 2024-12-18T00:16:34.1960308Z Tensor metadata (sizes/strides/dtype/device/etc). 2024-12-18T00:16:34.1960821Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-12-18T00:16:34.1961347Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-12-18T00:16:34.1961880Z This checks that the outputs (and gradients, if applicable) are the 2024-12-18T00:16:34.1962356Z same under eager-mode PyTorch and torch.compile. 2024-12-18T00:16:34.1962822Z This test is a superset of ``test_faketensor`` and is an e2e test; 2024-12-18T00:16:34.1963307Z other things it tests are that the operator supports 2024-12-18T00:16:34.1963818Z functionalization and that the backward pass (if it exists) also 2024-12-18T00:16:34.1964297Z supports FakeTensor and functionalization. 2024-12-18T00:16:34.1964545Z 2024-12-18T00:16:34.1964749Z For best results, please call ``opcheck`` multiple times with a 2024-12-18T00:16:34.1965249Z representative set of inputs. If your operator supports 2024-12-18T00:16:34.1965788Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-12-18T00:16:34.1966374Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-12-18T00:16:34.1966884Z use ``opcheck`` with inputs on all supported devices. 2024-12-18T00:16:34.1967154Z 2024-12-18T00:16:34.1967258Z Args: 2024-12-18T00:16:34.1967555Z op: The operator. Must either be a function decorated with 2024-12-18T00:16:34.1968071Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-12-18T00:16:34.1968630Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-12-18T00:16:34.1969082Z args: The args to the operator 2024-12-18T00:16:34.1969419Z kwargs: The kwargs to the operator 2024-12-18T00:16:34.1969818Z test_utils: Tests that we should run. Default: all of them. 2024-12-18T00:16:34.1970261Z Example: ("test_schema", "test_faketensor") 2024-12-18T00:16:34.1970710Z raise_exception: If we should raise an exception on the first 2024-12-18T00:16:34.1971194Z error. If False, we will return a dict with information 2024-12-18T00:16:34.1971597Z on if each test passed or not. 2024-12-18T00:16:34.1971818Z 2024-12-18T00:16:34.1971932Z .. warning:: 2024-12-18T00:16:34.1972066Z 2024-12-18T00:16:34.1972283Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-12-18T00:16:34.1972818Z opcheck tests if your usage of torch.library APIs is correct while 2024-12-18T00:16:34.1973363Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-12-18T00:16:34.1974150Z mathematically correct. Use both to test custom ops that support 2024-12-18T00:16:34.1974599Z gradient computation. 2024-12-18T00:16:34.1974786Z 2024-12-18T00:16:34.1974891Z Example: 2024-12-18T00:16:34.1975021Z 2024-12-18T00:16:34.1975157Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:34.1975622Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T00:16:34.1976091Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2024-12-18T00:16:34.1976463Z >>> x_np = x.numpy(force=True) 2024-12-18T00:16:34.1976951Z >>> z_np = x_np * y 2024-12-18T00:16:34.1977266Z >>> return torch.from_numpy(z_np).to(x.device) 2024-12-18T00:16:34.1977617Z >>> 2024-12-18T00:16:34.1977856Z >>> @numpy_mul.register_fake 2024-12-18T00:16:34.1978164Z >>> def _(x, y): 2024-12-18T00:16:34.1978449Z >>> return torch.empty_like(x) 2024-12-18T00:16:34.1978790Z >>> 2024-12-18T00:16:34.1979091Z >>> def setup_context(ctx, inputs, output): 2024-12-18T00:16:34.1979438Z >>> y, = inputs 2024-12-18T00:16:34.1979710Z >>> ctx.y = y 2024-12-18T00:16:34.1979967Z >>> 2024-12-18T00:16:34.1980189Z >>> def backward(ctx, grad): 2024-12-18T00:16:34.1980513Z >>> return grad * ctx.y, None 2024-12-18T00:16:34.1980824Z >>> 2024-12-18T00:16:34.1981174Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2024-12-18T00:16:34.1981609Z >>> 2024-12-18T00:16:34.1981829Z >>> sample_inputs = [ 2024-12-18T00:16:34.1982127Z >>> (torch.randn(3), 3.14), 2024-12-18T00:16:34.1982470Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-12-18T00:16:34.1982871Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-12-18T00:16:34.1983330Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-12-18T00:16:34.1983723Z >>> ] 2024-12-18T00:16:34.1983950Z >>> 2024-12-18T00:16:34.1984189Z >>> for args in sample_inputs: 2024-12-18T00:16:34.1984540Z >>> torch.library.opcheck(numpy_mul, args) 2024-12-18T00:16:34.1984785Z 2024-12-18T00:16:34.1984884Z 2024-12-18T00:16:34.1985245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.1985624Z 2024-12-18T00:16:34.2340969Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1226. 2024-12-18T00:16:34.2342115Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.2342828Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2024-12-18T00:16:34.2343283Z 2024-12-18T00:16:34.2343464Z Loads an object saved with :func:`torch.save` from a file. 2024-12-18T00:16:34.2343770Z 2024-12-18T00:16:34.2344012Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-12-18T00:16:34.2344603Z which underlie tensors, specially. They are first deserialized on the 2024-12-18T00:16:34.2345167Z CPU and are then moved to the device they were saved from. If this fails 2024-12-18T00:16:34.2345735Z (e.g. because the run time system doesn't have certain devices), an exception 2024-12-18T00:16:34.2346322Z is raised. However, storages can be dynamically remapped to an alternative 2024-12-18T00:16:34.2346851Z set of devices using the :attr:`map_location` argument. 2024-12-18T00:16:34.2347138Z 2024-12-18T00:16:34.2347382Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-12-18T00:16:34.2347979Z storage with two arguments: storage and location. The storage argument 2024-12-18T00:16:34.2348556Z will be the initial deserialization of the storage, residing on the CPU. 2024-12-18T00:16:34.2349129Z Each serialized storage has a location tag associated with it which 2024-12-18T00:16:34.2349669Z identifies the device it was saved from, and this tag is the second 2024-12-18T00:16:34.2350257Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-12-18T00:16:34.2350862Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-12-18T00:16:34.2351417Z :attr:`map_location` should return either ``None`` or a storage. If 2024-12-18T00:16:34.2351995Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-12-18T00:16:34.2352621Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-12-18T00:16:34.2353401Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-12-18T00:16:34.2353778Z 2024-12-18T00:16:34.2354014Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-12-18T00:16:34.2354615Z a device tag, it indicates the location where all tensors should be loaded. 2024-12-18T00:16:34.2355019Z 2024-12-18T00:16:34.2355315Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-12-18T00:16:34.2355919Z appearing in the file (keys), to ones that specify where to put the 2024-12-18T00:16:34.2356351Z storages (values). 2024-12-18T00:16:34.2356512Z 2024-12-18T00:16:34.2356734Z User extensions can register their own location tags and tagging and 2024-12-18T00:16:34.2357342Z deserialization methods using :func:`torch.serialization.register_package`. 2024-12-18T00:16:34.2357732Z 2024-12-18T00:16:34.2357821Z Args: 2024-12-18T00:16:34.2358275Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-12-18T00:16:34.2358987Z or a string or os.PathLike object containing a file name 2024-12-18T00:16:34.2359611Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-12-18T00:16:34.2360144Z locations 2024-12-18T00:16:34.2360542Z pickle_module: module used for unpickling metadata and objects (has to 2024-12-18T00:16:34.2361068Z match the :attr:`pickle_module` used to serialize file) 2024-12-18T00:16:34.2361576Z weights_only: Indicates whether unpickler should be restricted to 2024-12-18T00:16:34.2362081Z loading only tensors, primitive types, dictionaries 2024-12-18T00:16:34.2362585Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-12-18T00:16:34.2363051Z See :ref:`weights-only` for more details. 2024-12-18T00:16:34.2363641Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-12-18T00:16:34.2364440Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-12-18T00:16:34.2365245Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-12-18T00:16:34.2366044Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-12-18T00:16:34.2366738Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-12-18T00:16:34.2367334Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-12-18T00:16:34.2367924Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-12-18T00:16:34.2368373Z :attr:`errors=...`. 2024-12-18T00:16:34.2368571Z 2024-12-18T00:16:34.2368676Z .. warning:: 2024-12-18T00:16:34.2369039Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-12-18T00:16:34.2369569Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-12-18T00:16:34.2370177Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-12-18T00:16:34.2370820Z during unpickling. Never load data that could have come from an untrusted 2024-12-18T00:16:34.2371482Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-12-18T00:16:34.2371889Z 2024-12-18T00:16:34.2371980Z .. note:: 2024-12-18T00:16:34.2372376Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-12-18T00:16:34.2373020Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-12-18T00:16:34.2373810Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-12-18T00:16:34.2374415Z 2024-12-18T00:16:34.2374522Z .. note:: 2024-12-18T00:16:34.2374912Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-12-18T00:16:34.2375494Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-12-18T00:16:34.2376050Z when loading files saved by Python 2 in Python 3. If this default 2024-12-18T00:16:34.2376715Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-12-18T00:16:34.2377341Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-12-18T00:16:34.2377946Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-12-18T00:16:34.2378551Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-12-18T00:16:34.2378900Z 2024-12-18T00:16:34.2378991Z Example: 2024-12-18T00:16:34.2379272Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-12-18T00:16:34.2379673Z >>> torch.load("tensors.pt", weights_only=True) 2024-12-18T00:16:34.2380046Z # Load all tensors onto the CPU 2024-12-18T00:16:34.2380513Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-12-18T00:16:34.2381016Z # Load all tensors onto the CPU, using a function 2024-12-18T00:16:34.2381376Z >>> torch.load( 2024-12-18T00:16:34.2381782Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-12-18T00:16:34.2382235Z ... ) 2024-12-18T00:16:34.2382480Z # Load all tensors onto GPU 1 2024-12-18T00:16:34.2382777Z >>> torch.load( 2024-12-18T00:16:34.2383039Z ... "tensors.pt", 2024-12-18T00:16:34.2383395Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-12-18T00:16:34.2383778Z ... weights_only=True, 2024-12-18T00:16:34.2384094Z ... ) # type: ignore[attr-defined] 2024-12-18T00:16:34.2384421Z # Map tensors from GPU 1 to GPU 0 2024-12-18T00:16:34.2384890Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-12-18T00:16:34.2385372Z # Load tensor from io.BytesIO object 2024-12-18T00:16:34.2385848Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-12-18T00:16:34.2386339Z >>> with open("tensor.pt", "rb") as f: 2024-12-18T00:16:34.2386687Z ... buffer = io.BytesIO(f.read()) 2024-12-18T00:16:34.2387026Z >>> torch.load(buffer, weights_only=False) 2024-12-18T00:16:34.2387420Z # Load a module with 'ascii' encoding for unpickling 2024-12-18T00:16:34.2387933Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-12-18T00:16:34.2388495Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-12-18T00:16:34.2388900Z 2024-12-18T00:16:34.2389260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.2389642Z 2024-12-18T00:16:34.3394594Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=21. 2024-12-18T00:16:34.3395510Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:34.3396077Z Check if there is an available :ref:`accelerator`. 2024-12-18T00:16:34.3396408Z 2024-12-18T00:16:34.3396500Z Returns: 2024-12-18T00:16:34.3396911Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2024-12-18T00:16:34.3397308Z 2024-12-18T00:16:34.3397428Z Example:: 2024-12-18T00:16:34.3397560Z 2024-12-18T00:16:34.3397828Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:34.3398306Z 2024-12-18T00:16:34.3399029Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2024-12-18T00:16:34.3399927Z 2024-12-18T00:16:34.3400183Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:34.3400665Z ^ 2024-12-18T00:16:34.3412693Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=110. 2024-12-18T00:16:34.3413727Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:34.3414302Z Wait for all kernels in all streams on the given device to complete. 2024-12-18T00:16:34.3414638Z 2024-12-18T00:16:34.3414727Z Args: 2024-12-18T00:16:34.3415173Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2024-12-18T00:16:34.3415853Z the current :ref:`accelerator` device type. If not given, 2024-12-18T00:16:34.3416406Z use :func:`torch.accelerator.current_device_idx` by default. 2024-12-18T00:16:34.3416717Z 2024-12-18T00:16:34.3417046Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2024-12-18T00:16:34.3417473Z 2024-12-18T00:16:34.3417581Z Example:: 2024-12-18T00:16:34.3417713Z 2024-12-18T00:16:34.3417852Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:34.3418375Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:34.3418909Z >>> start_event = torch.Event(enable_timing=True) 2024-12-18T00:16:34.3419322Z >>> end_event = torch.Event(enable_timing=True) 2024-12-18T00:16:34.3419671Z >>> start_event.record() 2024-12-18T00:16:34.3420109Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2024-12-18T00:16:34.3420582Z >>> sum = torch.sum(tensor) 2024-12-18T00:16:34.3420894Z >>> end_event.record() 2024-12-18T00:16:34.3421213Z >>> torch.accelerator.synchronize() 2024-12-18T00:16:34.3421614Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2024-12-18T00:16:34.3421995Z 2024-12-18T00:16:34.3422673Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2024-12-18T00:16:34.3423340Z 2024-12-18T00:16:34.3423615Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:34.3424101Z ^ 2024-12-18T00:16:34.3615433Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=343. 2024-12-18T00:16:34.3616528Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:34.3617021Z Retrieves the CUDA runtime API module. 2024-12-18T00:16:34.3617257Z 2024-12-18T00:16:34.3617261Z 2024-12-18T00:16:34.3617511Z This function initializes the CUDA runtime environment if it is not already 2024-12-18T00:16:34.3618141Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-12-18T00:16:34.3618718Z runtime API module provides access to various CUDA runtime functions. 2024-12-18T00:16:34.3619060Z 2024-12-18T00:16:34.3619159Z Args: 2024-12-18T00:16:34.3619379Z ``None`` 2024-12-18T00:16:34.3619516Z 2024-12-18T00:16:34.3619602Z Returns: 2024-12-18T00:16:34.3619880Z module: The CUDA runtime API module (_cudart). 2024-12-18T00:16:34.3620146Z 2024-12-18T00:16:34.3620233Z Raises: 2024-12-18T00:16:34.3620589Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-12-18T00:16:34.3621295Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-12-18T00:16:34.3621769Z 2024-12-18T00:16:34.3621913Z Example of CUDA operations with profiling: 2024-12-18T00:16:34.3622245Z >>> import torch 2024-12-18T00:16:34.3622551Z >>> from torch.cuda import cudart, check_error 2024-12-18T00:16:34.3623062Z >>> import os 2024-12-18T00:16:34.3623307Z >>> 2024-12-18T00:16:34.3623555Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-12-18T00:16:34.3623859Z >>> 2024-12-18T00:16:34.3624127Z >>> def perform_cuda_operations_with_streams(): 2024-12-18T00:16:34.3624503Z >>> stream = torch.cuda.Stream() 2024-12-18T00:16:34.3624937Z >>> with torch.cuda.stream(stream): 2024-12-18T00:16:34.3625305Z >>> x = torch.randn(100, 100, device='cuda') 2024-12-18T00:16:34.3625669Z >>> y = torch.randn(100, 100, device='cuda') 2024-12-18T00:16:34.3626019Z >>> z = torch.mul(x, y) 2024-12-18T00:16:34.3626327Z >>> return z 2024-12-18T00:16:34.3626580Z >>> 2024-12-18T00:16:34.3626817Z >>> torch.cuda.synchronize() 2024-12-18T00:16:34.3627154Z >>> print("====== Start nsys profiling ======") 2024-12-18T00:16:34.3627544Z >>> check_error(cudart().cudaProfilerStart()) 2024-12-18T00:16:34.3627941Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-12-18T00:16:34.3628354Z >>> result = perform_cuda_operations_with_streams() 2024-12-18T00:16:34.3628752Z >>> print("CUDA operations completed.") 2024-12-18T00:16:34.3629149Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-12-18T00:16:34.3629564Z >>> print("====== End nsys profiling ======") 2024-12-18T00:16:34.3629817Z 2024-12-18T00:16:34.3630017Z To run this example and save the profiling information, execute: 2024-12-18T00:16:34.3630703Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T00:16:34.3631176Z 2024-12-18T00:16:34.3631431Z This command profiles the CUDA operations in the provided script and saves 2024-12-18T00:16:34.3631994Z the profiling information to a file named `trace_name.prof`. 2024-12-18T00:16:34.3632550Z The `--profile-from-start off` option ensures that profiling starts only 2024-12-18T00:16:34.3633060Z after the `cudaProfilerStart` call in the script. 2024-12-18T00:16:34.3633546Z The `--csv` and `--print-summary` options format the profiling output as a 2024-12-18T00:16:34.3634023Z CSV file and print a summary, respectively. 2024-12-18T00:16:34.3634519Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-12-18T00:16:34.3635041Z overwrite of the output file if it already exists. 2024-12-18T00:16:34.3635382Z 2024-12-18T00:16:34.3636155Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-12-18T00:16:34.3636922Z 2024-12-18T00:16:34.3637269Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T00:16:34.3637824Z ^ 2024-12-18T00:16:34.3734132Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2024-12-18T00:16:34.3735165Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.3735540Z 2024-12-18T00:16:34.3735784Z Append the given callback function to this ``Future``, which will be run 2024-12-18T00:16:34.3736332Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-12-18T00:16:34.3736864Z the same ``Future``, but the order in which they will be executed cannot 2024-12-18T00:16:34.3737376Z be guaranteed (to enforce a certain order consider chaining: 2024-12-18T00:16:34.3737877Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-12-18T00:16:34.3738404Z is the reference to this ``Future``. The callback function can use the 2024-12-18T00:16:34.3738927Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-12-18T00:16:34.3739476Z already completed, the given callback will be run immediately inline. 2024-12-18T00:16:34.3739981Z 2024-12-18T00:16:34.3740175Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-12-18T00:16:34.3740709Z callback might be invoked while the async kernels that are populating 2024-12-18T00:16:34.3741275Z those tensors haven't yet finished executing on the device. However, the 2024-12-18T00:16:34.3741888Z callback will be invoked with some dedicated streams set as current 2024-12-18T00:16:34.3742485Z (fetched from a global pool) which will be synchronized with those 2024-12-18T00:16:34.3743022Z kernels. Hence any operation performed by the callback on these tensors 2024-12-18T00:16:34.3743644Z will be scheduled on the device after the kernels complete. In other 2024-12-18T00:16:34.3744315Z words, as long as the callback doesn't switch streams, it can safely 2024-12-18T00:16:34.3745234Z manipulate the result without any additional synchronization. This is 2024-12-18T00:16:34.3745746Z similar to the non-blocking behavior of :meth:`wait`. 2024-12-18T00:16:34.3746019Z 2024-12-18T00:16:34.3746246Z Similarly, if the callback returns a value that contains tensors that 2024-12-18T00:16:34.3746802Z reside on a GPU, it can do so even if the kernels that are producing 2024-12-18T00:16:34.3747333Z these tensors are still running on the device, as long as the callback 2024-12-18T00:16:34.3747873Z didn't change streams during its execution. If one wants to change 2024-12-18T00:16:34.3748413Z streams, one must be careful to re-synchronize them with the original 2024-12-18T00:16:34.3748968Z streams, that is, those that were current when the callback was invoked. 2024-12-18T00:16:34.3749296Z 2024-12-18T00:16:34.3749395Z Args: 2024-12-18T00:16:34.3749710Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-12-18T00:16:34.3750150Z the only argument. 2024-12-18T00:16:34.3750382Z 2024-12-18T00:16:34.3750471Z Returns: 2024-12-18T00:16:34.3750767Z A new ``Future`` object that holds the return value of the 2024-12-18T00:16:34.3751234Z ``callback`` and will be marked as completed when the given 2024-12-18T00:16:34.3751622Z ``callback`` finishes. 2024-12-18T00:16:34.3751808Z 2024-12-18T00:16:34.3751988Z .. note:: Note that if the callback function throws, either 2024-12-18T00:16:34.3752490Z through the original future being completed with an exception and 2024-12-18T00:16:34.3753026Z calling ``fut.wait()``, or through other code in the callback, the 2024-12-18T00:16:34.3753558Z future returned by ``then`` will be marked appropriately with the 2024-12-18T00:16:34.3754089Z encountered error. However, if this callback later completes 2024-12-18T00:16:34.3754615Z additional futures, those futures are not marked as completed with 2024-12-18T00:16:34.3755162Z an error and the user is responsible for handling completion/waiting 2024-12-18T00:16:34.3755606Z on those futures independently. 2024-12-18T00:16:34.3755815Z 2024-12-18T00:16:34.3755928Z Example:: 2024-12-18T00:16:34.3756208Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T00:16:34.3756562Z >>> def callback(fut): 2024-12-18T00:16:34.3756884Z ... print(f"RPC return value is {fut.wait()}.") 2024-12-18T00:16:34.3757255Z >>> fut = torch.futures.Future() 2024-12-18T00:16:34.3757638Z >>> # The inserted callback will print the return value when 2024-12-18T00:16:34.3758052Z >>> # receiving the response from "worker1" 2024-12-18T00:16:34.3758481Z >>> cb_fut = fut.then(callback) 2024-12-18T00:16:34.3758798Z >>> chain_cb_fut = cb_fut.then( 2024-12-18T00:16:34.3759158Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-12-18T00:16:34.3759514Z ... ) 2024-12-18T00:16:34.3759737Z >>> fut.set_result(5) 2024-12-18T00:16:34.3759999Z RPC return value is 5. 2024-12-18T00:16:34.3760285Z Chained cb done. None 2024-12-18T00:16:34.3760465Z 2024-12-18T00:16:34.3760717Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.3761097Z 2024-12-18T00:16:34.3761712Z 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=209. 2024-12-18T00:16:34.3762655Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.3763031Z 2024-12-18T00:16:34.3763259Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-12-18T00:16:34.3763857Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-12-18T00:16:34.3764306Z cannot be marked completed twice. 2024-12-18T00:16:34.3764509Z 2024-12-18T00:16:34.3764728Z If the result contains tensors that reside on GPUs, this method can be 2024-12-18T00:16:34.3765274Z called even if the asynchronous kernels that are populating those 2024-12-18T00:16:34.3765817Z tensors haven't yet completed running on the device, provided that the 2024-12-18T00:16:34.3766384Z streams on which those kernels were enqueued are set as the current ones 2024-12-18T00:16:34.3766942Z when this method is called. Put simply, it's safe to call this method 2024-12-18T00:16:34.3767477Z immediately after launching those kernels, without any additional 2024-12-18T00:16:34.3768040Z synchronization, as long as one doesn't change streams in between. This 2024-12-18T00:16:34.3768605Z method will record events on all the relevant current streams and will 2024-12-18T00:16:34.3769151Z use them to ensure proper scheduling for all the consumers of this 2024-12-18T00:16:34.3769564Z ``Future``. 2024-12-18T00:16:34.3769688Z 2024-12-18T00:16:34.3769785Z Args: 2024-12-18T00:16:34.3770057Z result (object): the result object of this ``Future``. 2024-12-18T00:16:34.3770346Z 2024-12-18T00:16:34.3770440Z Example:: 2024-12-18T00:16:34.3770717Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T00:16:34.3771079Z >>> import threading 2024-12-18T00:16:34.3771346Z >>> import time 2024-12-18T00:16:34.3771598Z >>> def slow_set_future(fut, value): 2024-12-18T00:16:34.3771921Z ... time.sleep(0.5) 2024-12-18T00:16:34.3772210Z ... fut.set_result(value) 2024-12-18T00:16:34.3772522Z >>> fut = torch.futures.Future() 2024-12-18T00:16:34.3772837Z >>> t = threading.Thread( 2024-12-18T00:16:34.3773116Z ... target=slow_set_future, 2024-12-18T00:16:34.3773432Z ... args=(fut, torch.ones(2) * 3) 2024-12-18T00:16:34.3774000Z ... ) 2024-12-18T00:16:34.3774221Z >>> t.start() 2024-12-18T00:16:34.3774471Z >>> print(fut.wait()) 2024-12-18T00:16:34.3774724Z tensor([3., 3.]) 2024-12-18T00:16:34.3774972Z >>> t.join() 2024-12-18T00:16:34.3775120Z 2024-12-18T00:16:34.3775372Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.3775742Z 2024-12-18T00:16:34.3836700Z 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. 2024-12-18T00:16:34.3837587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.3838193Z Compiles compute shader from source and allows one to invoke kernels 2024-12-18T00:16:34.3838741Z defined there from the comfort of Python runtime 2024-12-18T00:16:34.3839105Z Example:: 2024-12-18T00:16:34.3839250Z 2024-12-18T00:16:34.3839386Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2024-12-18T00:16:34.3839760Z >>> lib = torch.mps._compile_shader( 2024-12-18T00:16:34.3840373Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2024-12-18T00:16:34.3840962Z ... ) 2024-12-18T00:16:34.3841211Z >>> x = torch.zeros(16, device="mps") 2024-12-18T00:16:34.3841543Z >>> lib.full(x, 3.14) 2024-12-18T00:16:34.3841817Z 2024-12-18T00:16:34.3842188Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.3842554Z 2024-12-18T00:16:34.4003033Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2024-12-18T00:16:34.4004041Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:34.4004588Z Return the sum of each row of the given sparse tensor. 2024-12-18T00:16:34.4004859Z 2024-12-18T00:16:34.4005085Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-12-18T00:16:34.4005716Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-12-18T00:16:34.4006249Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-12-18T00:16:34.4006740Z returns a dense tensor instead of a sparse tensor. 2024-12-18T00:16:34.4006997Z 2024-12-18T00:16:34.4007268Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-12-18T00:16:34.4007846Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-12-18T00:16:34.4008152Z 2024-12-18T00:16:34.4008373Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-12-18T00:16:34.4008958Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-12-18T00:16:34.4009323Z 2024-12-18T00:16:34.4009412Z Args: 2024-12-18T00:16:34.4009666Z input (Tensor): the input sparse tensor 2024-12-18T00:16:34.4010183Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-12-18T00:16:34.4010685Z over all dims. 2024-12-18T00:16:34.4011107Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-12-18T00:16:34.4011607Z Default: dtype of :attr:`input`. 2024-12-18T00:16:34.4011844Z 2024-12-18T00:16:34.4011941Z Example:: 2024-12-18T00:16:34.4012073Z 2024-12-18T00:16:34.4012178Z >>> nnz = 3 2024-12-18T00:16:34.4012424Z >>> dims = [5, 5, 2, 3] 2024-12-18T00:16:34.4012760Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-12-18T00:16:34.4013235Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-12-18T00:16:34.4013681Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-12-18T00:16:34.4014030Z >>> size = torch.Size(dims) 2024-12-18T00:16:34.4014382Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:34.4014759Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-12-18T00:16:34.4015099Z >>> S 2024-12-18T00:16:34.4015356Z tensor(indices=tensor([[2, 0, 3], 2024-12-18T00:16:34.4015684Z [2, 4, 1]]), 2024-12-18T00:16:34.4016032Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-12-18T00:16:34.4016402Z [ 0.3411, 0.0918, -0.2312]], 2024-12-18T00:16:34.4016625Z 2024-12-18T00:16:34.4016741Z [[ 0.5348, 0.0634, -2.0494], 2024-12-18T00:16:34.4017081Z [-0.7125, -1.0646, 2.1844]], 2024-12-18T00:16:34.4017319Z 2024-12-18T00:16:34.4017429Z [[ 0.1276, 0.1874, -0.6334], 2024-12-18T00:16:34.4017777Z [-1.9682, -0.5340, 0.7483]]]), 2024-12-18T00:16:34.4018161Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-12-18T00:16:34.4018418Z 2024-12-18T00:16:34.4018620Z # when sum over only part of sparse_dims, return a sparse tensor 2024-12-18T00:16:34.4019038Z >>> torch.sparse.sum(S, [1, 3]) 2024-12-18T00:16:34.4019372Z tensor(indices=tensor([[0, 2, 3]]), 2024-12-18T00:16:34.4019709Z values=tensor([[-1.4512, 0.4073], 2024-12-18T00:16:34.4020042Z [-0.8901, 0.2017], 2024-12-18T00:16:34.4020367Z [-0.3183, -1.7539]]), 2024-12-18T00:16:34.4020714Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-12-18T00:16:34.4020973Z 2024-12-18T00:16:34.4021127Z # when sum over all sparse dim, return a dense tensor 2024-12-18T00:16:34.4021504Z # with summed dims squeezed 2024-12-18T00:16:34.4021876Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-12-18T00:16:34.4022231Z tensor([-2.6596, -1.1450]) 2024-12-18T00:16:34.4022503Z 2024-12-18T00:16:34.4022878Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:34.4023261Z 2024-12-18T00:16:35.0625275Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=40. 2024-12-18T00:16:35.0626489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:35.0626884Z 2024-12-18T00:16:35.0627119Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-12-18T00:16:35.0627671Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-12-18T00:16:35.0628204Z pushes the map into PyTorch operations called by ``func``, effectively 2024-12-18T00:16:35.0628659Z vectorizing those operations. 2024-12-18T00:16:35.0628867Z 2024-12-18T00:16:35.0629084Z vmap is useful for handling batch dimensions: one can write a function 2024-12-18T00:16:35.0629634Z ``func`` that runs on examples and then lift it to a function that can 2024-12-18T00:16:35.0630170Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-12-18T00:16:35.0630675Z compute batched gradients when composed with autograd. 2024-12-18T00:16:35.0630952Z 2024-12-18T00:16:35.0631065Z .. note:: 2024-12-18T00:16:35.0631388Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-12-18T00:16:35.0631825Z convenience. Use whichever one you'd like. 2024-12-18T00:16:35.0632065Z 2024-12-18T00:16:35.0632165Z Args: 2024-12-18T00:16:35.0632501Z func (function): A Python function that takes one or more arguments. 2024-12-18T00:16:35.0632934Z Must return one or more Tensors. 2024-12-18T00:16:35.0633362Z in_dims (int or nested structure): Specifies which dimension of the 2024-12-18T00:16:35.0633860Z inputs should be mapped over. ``in_dims`` should have a 2024-12-18T00:16:35.0634350Z structure like the inputs. If the ``in_dim`` for a particular 2024-12-18T00:16:35.0634853Z input is None, then that indicates there is no map dimension. 2024-12-18T00:16:35.0635257Z Default: 0. 2024-12-18T00:16:35.0635594Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-12-18T00:16:35.0636111Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-12-18T00:16:35.0636584Z it should have one element per output. Default: 0. 2024-12-18T00:16:35.0637043Z randomness (str): Specifies whether the randomness in this 2024-12-18T00:16:35.0637560Z vmap should be the same or different across batches. If 'different', 2024-12-18T00:16:35.0638080Z the randomness for each batch will be different. If 'same', the 2024-12-18T00:16:35.0638718Z randomness will be the same across batches. If 'error', any calls to 2024-12-18T00:16:35.0639275Z random functions will error. Default: 'error'. WARNING: this flag 2024-12-18T00:16:35.0639815Z only applies to random PyTorch operations and does not apply to 2024-12-18T00:16:35.0640278Z Python's random module or numpy randomness. 2024-12-18T00:16:35.0640760Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-12-18T00:16:35.0641318Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-12-18T00:16:35.0641923Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-12-18T00:16:35.0642574Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-12-18T00:16:35.0642970Z 2024-12-18T00:16:35.0643060Z Returns: 2024-12-18T00:16:35.0643379Z Returns a new "batched" function. It takes the same inputs as 2024-12-18T00:16:35.0643870Z ``func``, except each input has an extra dimension at the index 2024-12-18T00:16:35.0644354Z specified by ``in_dims``. It takes returns the same outputs as 2024-12-18T00:16:35.0644933Z ``func``, except each output has an extra dimension at the index 2024-12-18T00:16:35.0645407Z specified by ``out_dims``. 2024-12-18T00:16:35.0645596Z 2024-12-18T00:16:35.0645701Z .. warning: 2024-12-18T00:16:35.0646033Z :func:`vmap` works best with functional-style code. Please do not 2024-12-18T00:16:35.0646531Z perform any side-effects in ``func``, with the exception of 2024-12-18T00:16:35.0647148Z in-place PyTorch operations. Examples of side-effects include mutating 2024-12-18T00:16:35.0647736Z Python data structures and assigning values to variables not captured 2024-12-18T00:16:35.0648181Z in ``func``. 2024-12-18T00:16:35.0648323Z 2024-12-18T00:16:35.0648573Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-12-18T00:16:35.0649157Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-12-18T00:16:35.0649710Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-12-18T00:16:35.0650057Z 2024-12-18T00:16:35.0650222Z >>> torch.dot # [D], [D] -> [] 2024-12-18T00:16:35.0650697Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-12-18T00:16:35.0651153Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T00:16:35.0651502Z >>> batched_dot(x, y) 2024-12-18T00:16:35.0651670Z 2024-12-18T00:16:35.0651917Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-12-18T00:16:35.0652371Z model authoring experience. 2024-12-18T00:16:35.0652569Z 2024-12-18T00:16:35.0652681Z >>> batch_size, feature_size = 3, 5 2024-12-18T00:16:35.0653075Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-12-18T00:16:35.0653456Z >>> 2024-12-18T00:16:35.0653682Z >>> def model(feature_vec): 2024-12-18T00:16:35.0653996Z >>> # Very simple linear model with activation 2024-12-18T00:16:35.0654371Z >>> return feature_vec.dot(weights).relu() 2024-12-18T00:16:35.0654702Z >>> 2024-12-18T00:16:35.0654979Z >>> examples = torch.randn(batch_size, feature_size) 2024-12-18T00:16:35.0655374Z >>> result = torch.vmap(model)(examples) 2024-12-18T00:16:35.0655599Z 2024-12-18T00:16:35.0655848Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-12-18T00:16:35.0656456Z or impossible to batch. One example is higher-order gradient computation. 2024-12-18T00:16:35.0657045Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-12-18T00:16:35.0657623Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-12-18T00:16:35.0658220Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-12-18T00:16:35.0658826Z we can vectorize the whole computation, computing the Jacobian in a single 2024-12-18T00:16:35.0659275Z call to ``autograd.grad``. 2024-12-18T00:16:35.0659463Z 2024-12-18T00:16:35.0659552Z >>> # Setup 2024-12-18T00:16:35.0659782Z >>> N = 5 2024-12-18T00:16:35.0660014Z >>> f = lambda x: x ** 2 2024-12-18T00:16:35.0660324Z >>> x = torch.randn(N, requires_grad=True) 2024-12-18T00:16:35.0660641Z >>> y = f(x) 2024-12-18T00:16:35.0660881Z >>> I_N = torch.eye(N) 2024-12-18T00:16:35.0661143Z >>> 2024-12-18T00:16:35.0661369Z >>> # Sequential approach 2024-12-18T00:16:35.0661785Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-12-18T00:16:35.0662228Z >>> for v in I_N.unbind()] 2024-12-18T00:16:35.0662589Z >>> jacobian = torch.stack(jacobian_rows) 2024-12-18T00:16:35.0662914Z >>> 2024-12-18T00:16:35.0663160Z >>> # vectorized gradient computation 2024-12-18T00:16:35.0663492Z >>> def get_vjp(v): 2024-12-18T00:16:35.0663776Z >>> return torch.autograd.grad(y, x, v) 2024-12-18T00:16:35.0664137Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-12-18T00:16:35.0664373Z 2024-12-18T00:16:35.0664637Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-12-18T00:16:35.0665070Z 2024-12-18T00:16:35.0665268Z >>> torch.dot # [D], [D] -> [] 2024-12-18T00:16:35.0665799Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-12-18T00:16:35.0666328Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-12-18T00:16:35.0666701Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-12-18T00:16:35.0666951Z 2024-12-18T00:16:35.0667249Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-12-18T00:16:35.0667773Z the dimension that each inputs are batched along as 2024-12-18T00:16:35.0668050Z 2024-12-18T00:16:35.0668193Z >>> torch.dot # [N], [N] -> [] 2024-12-18T00:16:35.0668680Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-12-18T00:16:35.0669154Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T00:16:35.0669631Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-12-18T00:16:35.0670009Z 2024-12-18T00:16:35.0670266Z If there are multiple inputs each of which is batched along different dimensions, 2024-12-18T00:16:35.0670850Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-12-18T00:16:35.0671174Z 2024-12-18T00:16:35.0671315Z >>> torch.dot # [D], [D] -> [] 2024-12-18T00:16:35.0671813Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-12-18T00:16:35.0672297Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T00:16:35.0672762Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-12-18T00:16:35.0673133Z 2024-12-18T00:16:35.0673369Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-12-18T00:16:35.0674140Z matching the shape of the input: 2024-12-18T00:16:35.0674359Z 2024-12-18T00:16:35.0674501Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-12-18T00:16:35.0674886Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T00:16:35.0675229Z >>> input = {'x': x, 'y': y} 2024-12-18T00:16:35.0675592Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-12-18T00:16:35.0675994Z >>> batched_dot(input) 2024-12-18T00:16:35.0676179Z 2024-12-18T00:16:35.0676452Z By default, the output is batched along the first dimension. However, it can be batched 2024-12-18T00:16:35.0676978Z along any dimension by using ``out_dims`` 2024-12-18T00:16:35.0677206Z 2024-12-18T00:16:35.0677320Z >>> f = lambda x: x ** 2 2024-12-18T00:16:35.0677594Z >>> x = torch.randn(2, 5) 2024-12-18T00:16:35.0677908Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-12-18T00:16:35.0678248Z >>> batched_pow(x) # [5, 2] 2024-12-18T00:16:35.0678512Z 2024-12-18T00:16:35.0678802Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-12-18T00:16:35.0679309Z accept kwargs 2024-12-18T00:16:35.0679442Z 2024-12-18T00:16:35.0679559Z >>> x = torch.randn([2, 5]) 2024-12-18T00:16:35.0679840Z >>> def fn(x, scale=4.): 2024-12-18T00:16:35.0680124Z >>> return x * scale 2024-12-18T00:16:35.0680389Z >>> 2024-12-18T00:16:35.0680638Z >>> batched_pow = torch.vmap(fn) 2024-12-18T00:16:35.0680986Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-12-18T00:16:35.0681473Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-12-18T00:16:35.0681831Z 2024-12-18T00:16:35.0681926Z .. note:: 2024-12-18T00:16:35.0682278Z vmap does not provide general autobatching or handle variable-length 2024-12-18T00:16:35.0682730Z sequences out of the box. 2024-12-18T00:16:35.0682915Z 2024-12-18T00:16:35.0683181Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:35.0683551Z 2024-12-18T00:16:36.3347829Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=20. 2024-12-18T00:16:36.3348955Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.3349691Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-12-18T00:16:36.3350064Z 2024-12-18T00:16:36.3350289Z This is a more structured way of using triton kernels with PyTorch. 2024-12-18T00:16:36.3350934Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2024-12-18T00:16:36.3351613Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2024-12-18T00:16:36.3352089Z that is simpler; 2024-12-18T00:16:36.3352506Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2024-12-18T00:16:36.3353114Z want to create an operator that behaves like PyTorch built-in operators. 2024-12-18T00:16:36.3353679Z For example, you may use a ``torch.library`` wrapper API to define the 2024-12-18T00:16:36.3354293Z behavior of the triton kernel when passed a tensor subclass or under 2024-12-18T00:16:36.3355032Z a TorchDispatchMode. 2024-12-18T00:16:36.3355349Z 2024-12-18T00:16:36.3355602Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2024-12-18T00:16:36.3356087Z when the implementation 2024-12-18T00:16:36.3356491Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-12-18T00:16:36.3357001Z custom operators as opaque (:func:`torch.compile` and 2024-12-18T00:16:36.3357512Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-12-18T00:16:36.3358095Z makes the implementation visible to these subsystems, allowing them 2024-12-18T00:16:36.3358621Z to optimize the triton kernel(s). 2024-12-18T00:16:36.3358835Z 2024-12-18T00:16:36.3359040Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-12-18T00:16:36.3359583Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-12-18T00:16:36.3360135Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2024-12-18T00:16:36.3360456Z 2024-12-18T00:16:36.3360546Z Args: 2024-12-18T00:16:36.3360920Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-12-18T00:16:36.3361481Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-12-18T00:16:36.3361988Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-12-18T00:16:36.3362513Z To avoid name collisions, please use your project name as the namespace; 2024-12-18T00:16:36.3363085Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-12-18T00:16:36.3363676Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-12-18T00:16:36.3364312Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-12-18T00:16:36.3364934Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-12-18T00:16:36.3365514Z schema (None | str): A schema string for the operator. If None 2024-12-18T00:16:36.3366032Z (recommended) we'll infer a schema for the operator from its type 2024-12-18T00:16:36.3366567Z annotations. We recommend letting us infer a schema unless you 2024-12-18T00:16:36.3366995Z have a specific reason not to. 2024-12-18T00:16:36.3367381Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-12-18T00:16:36.3367660Z 2024-12-18T00:16:36.3367753Z Example:: 2024-12-18T00:16:36.3367883Z 2024-12-18T00:16:36.3368035Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:36.3368384Z >>> import torch 2024-12-18T00:16:36.3368700Z >>> from torch._library import triton_op, wrap_triton 2024-12-18T00:16:36.3369063Z >>> 2024-12-18T00:16:36.3369291Z >>> import triton 2024-12-18T00:16:36.3369582Z >>> from triton import language as tl 2024-12-18T00:16:36.3369903Z >>> 2024-12-18T00:16:36.3370184Z >>> @triton.jit 2024-12-18T00:16:36.3370481Z >>> def add_kernel( 2024-12-18T00:16:36.3370751Z >>> in_ptr0, 2024-12-18T00:16:36.3371006Z >>> in_ptr1, 2024-12-18T00:16:36.3371263Z >>> out_ptr, 2024-12-18T00:16:36.3371510Z >>> n_elements, 2024-12-18T00:16:36.3371798Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T00:16:36.3372150Z >>> ): 2024-12-18T00:16:36.3372437Z >>> pid = tl.program_id(axis=0) 2024-12-18T00:16:36.3372781Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T00:16:36.3373151Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T00:16:36.3373533Z >>> mask = offsets < n_elements 2024-12-18T00:16:36.3374220Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T00:16:36.3374601Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T00:16:36.3374946Z >>> output = x + y 2024-12-18T00:16:36.3375269Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T00:16:36.3375629Z >>> 2024-12-18T00:16:36.3375893Z >>> @triton_op("mylib::add", mutates_args={}) 2024-12-18T00:16:36.3376328Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-12-18T00:16:36.3376747Z >>> output = torch.empty_like(x) 2024-12-18T00:16:36.3377079Z >>> n_elements = output.numel() 2024-12-18T00:16:36.3377394Z >>> 2024-12-18T00:16:36.3377623Z >>> def grid(meta): 2024-12-18T00:16:36.3377984Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T00:16:36.3378370Z >>> 2024-12-18T00:16:36.3378671Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2024-12-18T00:16:36.3379170Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-12-18T00:16:36.3379578Z >>> return output 2024-12-18T00:16:36.3379850Z >>> 2024-12-18T00:16:36.3380075Z >>> @torch.compile 2024-12-18T00:16:36.3380337Z >>> def f(x, y): 2024-12-18T00:16:36.3380605Z >>> return add(x, y) 2024-12-18T00:16:36.3380884Z >>> 2024-12-18T00:16:36.3381134Z >>> x = torch.randn(3, device="cuda") 2024-12-18T00:16:36.3381481Z >>> y = torch.randn(3, device="cuda") 2024-12-18T00:16:36.3381784Z >>> 2024-12-18T00:16:36.3382003Z >>> z = f(x, y) 2024-12-18T00:16:36.3382289Z >>> assert torch.allclose(z, x + y) 2024-12-18T00:16:36.3382513Z 2024-12-18T00:16:36.3382613Z 2024-12-18T00:16:36.3382990Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.3383357Z 2024-12-18T00:16:36.3383903Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=183. 2024-12-18T00:16:36.3384783Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.3385342Z Allows capture of a triton kernel into a graph via make_fx or 2024-12-18T00:16:36.3385766Z non-strict ``torch.export``. 2024-12-18T00:16:36.3386068Z 2024-12-18T00:16:36.3386256Z These technologies perform Dispatcher-based tracing (via 2024-12-18T00:16:36.3386757Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-12-18T00:16:36.3387276Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2024-12-18T00:16:36.3387721Z can actually be traced into a graph. 2024-12-18T00:16:36.3387940Z 2024-12-18T00:16:36.3388150Z Please use this API together with :func:`torch.library.triton_op`. 2024-12-18T00:16:36.3388489Z 2024-12-18T00:16:36.3388580Z Examples: 2024-12-18T00:16:36.3388723Z 2024-12-18T00:16:36.3388822Z >>> # xdoctest: +SKIP 2024-12-18T00:16:36.3389105Z >>> import torch 2024-12-18T00:16:36.3389371Z >>> import triton 2024-12-18T00:16:36.3389664Z >>> from triton import language as tl 2024-12-18T00:16:36.3390068Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T00:16:36.3391100Z >>> from torch.library import wrap_triton 2024-12-18T00:16:36.3391436Z >>> 2024-12-18T00:16:36.3391664Z >>> @triton.jit 2024-12-18T00:16:36.3391930Z >>> def add_kernel( 2024-12-18T00:16:36.3392194Z >>> in_ptr0, 2024-12-18T00:16:36.3392454Z >>> in_ptr1, 2024-12-18T00:16:36.3392785Z >>> out_ptr, 2024-12-18T00:16:36.3393080Z >>> n_elements, 2024-12-18T00:16:36.3393360Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T00:16:36.3393676Z >>> ): 2024-12-18T00:16:36.3393927Z >>> pid = tl.program_id(axis=0) 2024-12-18T00:16:36.3394268Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T00:16:36.3394659Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T00:16:36.3395029Z >>> mask = offsets < n_elements 2024-12-18T00:16:36.3395391Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T00:16:36.3395776Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T00:16:36.3396124Z >>> output = x + y 2024-12-18T00:16:36.3396460Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T00:16:36.3396813Z >>> 2024-12-18T00:16:36.3397026Z >>> def add(x, y): 2024-12-18T00:16:36.3397317Z >>> output = torch.empty_like(x) 2024-12-18T00:16:36.3397660Z >>> n_elements = output.numel() 2024-12-18T00:16:36.3397977Z >>> 2024-12-18T00:16:36.3398203Z >>> def grid_fn(meta): 2024-12-18T00:16:36.3398660Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T00:16:36.3399053Z >>> 2024-12-18T00:16:36.3399380Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-12-18T00:16:36.3399803Z >>> return output 2024-12-18T00:16:36.3400079Z >>> 2024-12-18T00:16:36.3400310Z >>> x = torch.randn(3, device="cuda") 2024-12-18T00:16:36.3400656Z >>> y = torch.randn(3, device="cuda") 2024-12-18T00:16:36.3400995Z >>> gm = make_fx(add)(x, y) 2024-12-18T00:16:36.3401360Z >>> print(gm.code) 2024-12-18T00:16:36.3401651Z >>> # def forward(self, x_1, y_1): 2024-12-18T00:16:36.3402096Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-12-18T00:16:36.3402702Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-12-18T00:16:36.3403196Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-12-18T00:16:36.3403557Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-12-18T00:16:36.3403943Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-12-18T00:16:36.3404333Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-12-18T00:16:36.3404667Z >>> # }) 2024-12-18T00:16:36.3404935Z >>> # return empty_like 2024-12-18T00:16:36.3405135Z 2024-12-18T00:16:36.3405235Z 2024-12-18T00:16:36.3405606Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.3405976Z 2024-12-18T00:16:36.4074071Z 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=330. 2024-12-18T00:16:36.4075230Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.4075609Z 2024-12-18T00:16:36.4075862Z Raises an AssertionError if two items are not equal up to desired 2024-12-18T00:16:36.4076548Z precision. 2024-12-18T00:16:36.4076801Z 2024-12-18T00:16:36.4077067Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T00:16:36.4077539Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T00:16:36.4078034Z instead of this function for more consistent floating point 2024-12-18T00:16:36.4078496Z comparisons. 2024-12-18T00:16:36.4078655Z 2024-12-18T00:16:36.4079078Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-12-18T00:16:36.4079491Z 2024-12-18T00:16:36.4079650Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-12-18T00:16:36.4079938Z 2024-12-18T00:16:36.4080167Z That is a looser test than originally documented, but agrees with what the 2024-12-18T00:16:36.4081151Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-12-18T00:16:36.4081882Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-12-18T00:16:36.4082366Z delegates to assert_array_almost_equal 2024-12-18T00:16:36.4082587Z 2024-12-18T00:16:36.4082693Z Parameters 2024-12-18T00:16:36.4082903Z ---------- 2024-12-18T00:16:36.4083141Z actual : array_like 2024-12-18T00:16:36.4083398Z The object to check. 2024-12-18T00:16:36.4083670Z desired : array_like 2024-12-18T00:16:36.4083934Z The expected object. 2024-12-18T00:16:36.4084196Z decimal : int, optional 2024-12-18T00:16:36.4084480Z Desired precision, default is 7. 2024-12-18T00:16:36.4084938Z err_msg : str, optional 2024-12-18T00:16:36.4085260Z The error message to be printed in case of failure. 2024-12-18T00:16:36.4085634Z verbose : bool, optional 2024-12-18T00:16:36.4086007Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:36.4086344Z 2024-12-18T00:16:36.4086432Z Raises 2024-12-18T00:16:36.4086646Z ------ 2024-12-18T00:16:36.4086871Z AssertionError 2024-12-18T00:16:36.4087216Z If actual and desired are not equal up to specified precision. 2024-12-18T00:16:36.4087522Z 2024-12-18T00:16:36.4087611Z See Also 2024-12-18T00:16:36.4087832Z -------- 2024-12-18T00:16:36.4088199Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:36.4088694Z relative and/or absolute precision. 2024-12-18T00:16:36.4089158Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:36.4089481Z 2024-12-18T00:16:36.4089572Z Examples 2024-12-18T00:16:36.4089795Z -------- 2024-12-18T00:16:36.4090093Z >>> from torch._numpy.testing import assert_almost_equal 2024-12-18T00:16:36.4090515Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-12-18T00:16:36.4090942Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-12-18T00:16:36.4091336Z Traceback (most recent call last): 2024-12-18T00:16:36.4091633Z ... 2024-12-18T00:16:36.4091851Z AssertionError: 2024-12-18T00:16:36.4092121Z Arrays are not almost equal to 10 decimals 2024-12-18T00:16:36.4092452Z ACTUAL: 2.3333333333333 2024-12-18T00:16:36.4092702Z DESIRED: 2.33333334 2024-12-18T00:16:36.4092865Z 2024-12-18T00:16:36.4093013Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-12-18T00:16:36.4093405Z ... np.array([1.0,2.33333334]), decimal=9) 2024-12-18T00:16:36.4093760Z Traceback (most recent call last): 2024-12-18T00:16:36.4094057Z ... 2024-12-18T00:16:36.4094265Z AssertionError: 2024-12-18T00:16:36.4094530Z Arrays are not almost equal to 9 decimals 2024-12-18T00:16:36.4094859Z 2024-12-18T00:16:36.4095099Z Mismatched elements: 1 / 2 (50%) 2024-12-18T00:16:36.4095431Z Max absolute difference: 6.666699636781459e-09 2024-12-18T00:16:36.4095796Z Max relative difference: 2.8571569790287484e-09 2024-12-18T00:16:36.4096182Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T00:16:36.4096569Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T00:16:36.4096818Z 2024-12-18T00:16:36.4096822Z 2024-12-18T00:16:36.4097088Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.4097452Z 2024-12-18T00:16:36.4098080Z 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=455. 2024-12-18T00:16:36.4099015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.4099406Z 2024-12-18T00:16:36.4099626Z Raises an AssertionError if two items are not equal up to significant 2024-12-18T00:16:36.4100120Z digits. 2024-12-18T00:16:36.4100274Z 2024-12-18T00:16:36.4100467Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T00:16:36.4100937Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T00:16:36.4101415Z instead of this function for more consistent floating point 2024-12-18T00:16:36.4101817Z comparisons. 2024-12-18T00:16:36.4102028Z 2024-12-18T00:16:36.4102248Z Given two numbers, check that they are approximately equal. 2024-12-18T00:16:36.4102773Z Approximately equal is defined as the number of significant digits 2024-12-18T00:16:36.4103202Z that agree. 2024-12-18T00:16:36.4103329Z 2024-12-18T00:16:36.4103421Z Parameters 2024-12-18T00:16:36.4103645Z ---------- 2024-12-18T00:16:36.4103870Z actual : scalar 2024-12-18T00:16:36.4104114Z The object to check. 2024-12-18T00:16:36.4104380Z desired : scalar 2024-12-18T00:16:36.4104612Z The expected object. 2024-12-18T00:16:36.4104887Z significant : int, optional 2024-12-18T00:16:36.4105184Z Desired precision, default is 7. 2024-12-18T00:16:36.4105499Z err_msg : str, optional 2024-12-18T00:16:36.4105816Z The error message to be printed in case of failure. 2024-12-18T00:16:36.4106170Z verbose : bool, optional 2024-12-18T00:16:36.4106547Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:36.4106878Z 2024-12-18T00:16:36.4106965Z Raises 2024-12-18T00:16:36.4107170Z ------ 2024-12-18T00:16:36.4107388Z AssertionError 2024-12-18T00:16:36.4107714Z If actual and desired are not equal up to specified precision. 2024-12-18T00:16:36.4108029Z 2024-12-18T00:16:36.4108115Z See Also 2024-12-18T00:16:36.4108326Z -------- 2024-12-18T00:16:36.4108683Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:36.4109167Z relative and/or absolute precision. 2024-12-18T00:16:36.4109606Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:36.4109940Z 2024-12-18T00:16:36.4110027Z Examples 2024-12-18T00:16:36.4110241Z -------- 2024-12-18T00:16:36.4110634Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-12-18T00:16:36.4111279Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-12-18T00:16:36.4111762Z ... significant=8) 2024-12-18T00:16:36.4112245Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-12-18T00:16:36.4112737Z ... significant=8) 2024-12-18T00:16:36.4113082Z Traceback (most recent call last): 2024-12-18T00:16:36.4113381Z ... 2024-12-18T00:16:36.4113600Z AssertionError: 2024-12-18T00:16:36.4113869Z Items are not equal to 8 significant digits: 2024-12-18T00:16:36.4114209Z ACTUAL: 1.234567e-21 2024-12-18T00:16:36.4114470Z DESIRED: 1.2345672e-21 2024-12-18T00:16:36.4114629Z 2024-12-18T00:16:36.4114801Z the evaluated condition that raises the exception is 2024-12-18T00:16:36.4115069Z 2024-12-18T00:16:36.4115257Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-12-18T00:16:36.4115619Z True 2024-12-18T00:16:36.4123082Z 2024-12-18T00:16:36.4123090Z 2024-12-18T00:16:36.4123469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.4123873Z 2024-12-18T00:16:36.4124455Z 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=734. 2024-12-18T00:16:36.4125397Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.4125776Z 2024-12-18T00:16:36.4125998Z Raises an AssertionError if two array_like objects are not equal. 2024-12-18T00:16:36.4126321Z 2024-12-18T00:16:36.4126543Z Given two array_like objects, check that the shape is equal and all 2024-12-18T00:16:36.4127083Z elements of these objects are equal (but see the Notes for the special 2024-12-18T00:16:36.4127629Z handling of a scalar). An exception is raised at shape mismatch or 2024-12-18T00:16:36.4128311Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-12-18T00:16:36.4128879Z are compared like numbers, no assertion is raised if both objects have 2024-12-18T00:16:36.4129329Z NaNs in the same positions. 2024-12-18T00:16:36.4129510Z 2024-12-18T00:16:36.4129788Z The usual caution for verifying equality with floating point numbers is 2024-12-18T00:16:36.4130245Z advised. 2024-12-18T00:16:36.4130383Z 2024-12-18T00:16:36.4130478Z Parameters 2024-12-18T00:16:36.4130702Z ---------- 2024-12-18T00:16:36.4130929Z x : array_like 2024-12-18T00:16:36.4131180Z The actual object to check. 2024-12-18T00:16:36.4131456Z y : array_like 2024-12-18T00:16:36.4131711Z The desired, expected object. 2024-12-18T00:16:36.4132025Z err_msg : str, optional 2024-12-18T00:16:36.4132350Z The error message to be printed in case of failure. 2024-12-18T00:16:36.4132713Z verbose : bool, optional 2024-12-18T00:16:36.4133097Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:36.4133524Z strict : bool, optional 2024-12-18T00:16:36.4133889Z If True, raise an AssertionError when either the shape or the data 2024-12-18T00:16:36.4134386Z type of the array_like objects does not match. The special 2024-12-18T00:16:36.4134891Z handling for scalars mentioned in the Notes section is disabled. 2024-12-18T00:16:36.4135287Z 2024-12-18T00:16:36.4135405Z Raises 2024-12-18T00:16:36.4135619Z ------ 2024-12-18T00:16:36.4135838Z AssertionError 2024-12-18T00:16:36.4136114Z If actual and desired objects are not equal. 2024-12-18T00:16:36.4136355Z 2024-12-18T00:16:36.4136460Z See Also 2024-12-18T00:16:36.4136661Z -------- 2024-12-18T00:16:36.4137025Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:36.4137516Z relative and/or absolute precision. 2024-12-18T00:16:36.4137972Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:36.4138298Z 2024-12-18T00:16:36.4138400Z Notes 2024-12-18T00:16:36.4138597Z ----- 2024-12-18T00:16:36.4138906Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-12-18T00:16:36.4139435Z function checks that each element of the array_like object is equal to 2024-12-18T00:16:36.4140007Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-12-18T00:16:36.4140363Z 2024-12-18T00:16:36.4140501Z Examples 2024-12-18T00:16:36.4140742Z -------- 2024-12-18T00:16:36.4141006Z The first assert does not raise an exception: 2024-12-18T00:16:36.4141262Z 2024-12-18T00:16:36.4141416Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-12-18T00:16:36.4141814Z ... [np.exp(0),2.33333, np.nan]) 2024-12-18T00:16:36.4142052Z 2024-12-18T00:16:36.4142290Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-12-18T00:16:36.4142752Z functions for these cases instead: 2024-12-18T00:16:36.4142959Z 2024-12-18T00:16:36.4143108Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-12-18T00:16:36.4143504Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-12-18T00:16:36.4143869Z ... rtol=1e-10, atol=0) 2024-12-18T00:16:36.4144105Z 2024-12-18T00:16:36.4144309Z As mentioned in the Notes section, `assert_array_equal` has special 2024-12-18T00:16:36.4144863Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-12-18T00:16:36.4145199Z 2024-12-18T00:16:36.4145321Z >>> x = np.full((2, 5), fill_value=3) 2024-12-18T00:16:36.4145640Z >>> np.testing.assert_array_equal(x, 3) 2024-12-18T00:16:36.4145873Z 2024-12-18T00:16:36.4146087Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-12-18T00:16:36.4146516Z array: 2024-12-18T00:16:36.4146647Z 2024-12-18T00:16:36.4146798Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-12-18T00:16:36.4147175Z Traceback (most recent call last): 2024-12-18T00:16:36.4147475Z ... 2024-12-18T00:16:36.4147757Z AssertionError: 2024-12-18T00:16:36.4148038Z Arrays are not equal 2024-12-18T00:16:36.4148298Z 2024-12-18T00:16:36.4148533Z (shapes (2, 5), () mismatch) 2024-12-18T00:16:36.4148814Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-12-18T00:16:36.4149119Z [3, 3, 3, 3, 3]]) 2024-12-18T00:16:36.4149384Z y: torch.ndarray(3) 2024-12-18T00:16:36.4149542Z 2024-12-18T00:16:36.4149826Z The `strict` parameter also ensures that the array data types match: 2024-12-18T00:16:36.4150156Z 2024-12-18T00:16:36.4150272Z >>> x = np.array([2, 2, 2]) 2024-12-18T00:16:36.4150581Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-12-18T00:16:36.4150960Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-12-18T00:16:36.4151339Z Traceback (most recent call last): 2024-12-18T00:16:36.4151641Z ... 2024-12-18T00:16:36.4151861Z AssertionError: 2024-12-18T00:16:36.4152096Z Arrays are not equal 2024-12-18T00:16:36.4152348Z 2024-12-18T00:16:36.4152629Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-12-18T00:16:36.4153001Z x: torch.ndarray([2, 2, 2]) 2024-12-18T00:16:36.4153295Z y: torch.ndarray([2., 2., 2.]) 2024-12-18T00:16:36.4153480Z 2024-12-18T00:16:36.4153745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.4154116Z 2024-12-18T00:16:36.4154706Z 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=840. 2024-12-18T00:16:36.4155674Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.4156064Z 2024-12-18T00:16:36.4156280Z Raises an AssertionError if two objects are not equal up to desired 2024-12-18T00:16:36.4156709Z precision. 2024-12-18T00:16:36.4156837Z 2024-12-18T00:16:36.4157030Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T00:16:36.4157508Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T00:16:36.4157992Z instead of this function for more consistent floating point 2024-12-18T00:16:36.4158516Z comparisons. 2024-12-18T00:16:36.4158691Z 2024-12-18T00:16:36.4158930Z The test verifies identical shapes and that the elements of ``actual`` and 2024-12-18T00:16:36.4159390Z ``desired`` satisfy. 2024-12-18T00:16:36.4159547Z 2024-12-18T00:16:36.4159699Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-12-18T00:16:36.4159943Z 2024-12-18T00:16:36.4160187Z That is a looser test than originally documented, but agrees with what the 2024-12-18T00:16:36.4160768Z actual implementation did up to rounding vagaries. An exception is raised 2024-12-18T00:16:36.4161374Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-12-18T00:16:36.4161952Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-12-18T00:16:36.4162419Z objects have NaNs in the same positions. 2024-12-18T00:16:36.4162647Z 2024-12-18T00:16:36.4162756Z Parameters 2024-12-18T00:16:36.4162967Z ---------- 2024-12-18T00:16:36.4163194Z x : array_like 2024-12-18T00:16:36.4163445Z The actual object to check. 2024-12-18T00:16:36.4163735Z y : array_like 2024-12-18T00:16:36.4163986Z The desired, expected object. 2024-12-18T00:16:36.4164281Z decimal : int, optional 2024-12-18T00:16:36.4164566Z Desired precision, default is 6. 2024-12-18T00:16:36.4164886Z err_msg : str, optional 2024-12-18T00:16:36.4165211Z The error message to be printed in case of failure. 2024-12-18T00:16:36.4165582Z verbose : bool, optional 2024-12-18T00:16:36.4165956Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:36.4166284Z 2024-12-18T00:16:36.4166370Z Raises 2024-12-18T00:16:36.4166580Z ------ 2024-12-18T00:16:36.4166797Z AssertionError 2024-12-18T00:16:36.4167141Z If actual and desired are not equal up to specified precision. 2024-12-18T00:16:36.4167446Z 2024-12-18T00:16:36.4167535Z See Also 2024-12-18T00:16:36.4167747Z -------- 2024-12-18T00:16:36.4168109Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:36.4168706Z relative and/or absolute precision. 2024-12-18T00:16:36.4169168Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:36.4169503Z 2024-12-18T00:16:36.4169593Z Examples 2024-12-18T00:16:36.4169810Z -------- 2024-12-18T00:16:36.4170054Z the first assert does not raise an exception 2024-12-18T00:16:36.4170339Z 2024-12-18T00:16:36.4170545Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-12-18T00:16:36.4170954Z ... [1.0,2.333,np.nan]) 2024-12-18T00:16:36.4171199Z 2024-12-18T00:16:36.4171376Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T00:16:36.4171803Z ... [1.0,2.33339,np.nan], decimal=5) 2024-12-18T00:16:36.4172174Z Traceback (most recent call last): 2024-12-18T00:16:36.4172465Z ... 2024-12-18T00:16:36.4172693Z AssertionError: 2024-12-18T00:16:36.4172964Z Arrays are not almost equal to 5 decimals 2024-12-18T00:16:36.4173292Z 2024-12-18T00:16:36.4173533Z Mismatched elements: 1 / 3 (33.3%) 2024-12-18T00:16:36.4174116Z Max absolute difference: 5.999999999994898e-05 2024-12-18T00:16:36.4174495Z Max relative difference: 2.5713661239633743e-05 2024-12-18T00:16:36.4174909Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T00:16:36.4175358Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-12-18T00:16:36.4175628Z 2024-12-18T00:16:36.4175818Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T00:16:36.4176237Z ... [1.0,2.33333, 5], decimal=5) 2024-12-18T00:16:36.4176585Z Traceback (most recent call last): 2024-12-18T00:16:36.4176880Z ... 2024-12-18T00:16:36.4177097Z AssertionError: 2024-12-18T00:16:36.4177363Z Arrays are not almost equal to 5 decimals 2024-12-18T00:16:36.4177677Z 2024-12-18T00:16:36.4177913Z x and y nan location mismatch: 2024-12-18T00:16:36.4178274Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T00:16:36.4178711Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-12-18T00:16:36.4178980Z 2024-12-18T00:16:36.4178984Z 2024-12-18T00:16:36.4179245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.4179612Z 2024-12-18T00:16:36.4180240Z 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=1790. 2024-12-18T00:16:36.4181195Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.4181795Z Context manager that resets warning registry for catching warnings 2024-12-18T00:16:36.4182121Z 2024-12-18T00:16:36.4182367Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-12-18T00:16:36.4182953Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-12-18T00:16:36.4183542Z it impossible to retrigger the warning in this module, whatever you put in 2024-12-18T00:16:36.4184142Z the warnings filters. This context manager accepts a sequence of `modules` 2024-12-18T00:16:36.4184643Z as a keyword argument to its constructor and: 2024-12-18T00:16:36.4184889Z 2024-12-18T00:16:36.4185128Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-12-18T00:16:36.4185553Z on entry; 2024-12-18T00:16:36.4185888Z * resets ``__warningregistry__`` to its previous state on exit. 2024-12-18T00:16:36.4186199Z 2024-12-18T00:16:36.4186420Z This makes it possible to trigger any warning afresh inside the context 2024-12-18T00:16:36.4186952Z manager without disturbing the state of warnings outside. 2024-12-18T00:16:36.4187246Z 2024-12-18T00:16:36.4187493Z For compatibility with Python 3.0, please consider all arguments to be 2024-12-18T00:16:36.4187949Z keyword-only. 2024-12-18T00:16:36.4188095Z 2024-12-18T00:16:36.4188302Z Parameters 2024-12-18T00:16:36.4188582Z ---------- 2024-12-18T00:16:36.4188823Z record : bool, optional 2024-12-18T00:16:36.4189198Z Specifies whether warnings should be captured by a custom 2024-12-18T00:16:36.4189743Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-12-18T00:16:36.4190337Z returned by the context manager. Otherwise None is returned by the 2024-12-18T00:16:36.4190931Z context manager. The objects appended to the list are arguments whose 2024-12-18T00:16:36.4191454Z attributes mirror the arguments to ``showwarning()``. 2024-12-18T00:16:36.4191856Z modules : sequence, optional 2024-12-18T00:16:36.4192281Z Sequence of modules for which to reset warnings registry on entry and 2024-12-18T00:16:36.4192807Z restore on exit. To work correctly, all 'ignore' filters should 2024-12-18T00:16:36.4193234Z filter by one of these modules. 2024-12-18T00:16:36.4193461Z 2024-12-18T00:16:36.4193553Z Examples 2024-12-18T00:16:36.4193781Z -------- 2024-12-18T00:16:36.4194013Z >>> import warnings 2024-12-18T00:16:36.4194365Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-12-18T00:16:36.4194785Z ... modules=[np.core.fromnumeric]): 2024-12-18T00:16:36.4195147Z ... warnings.simplefilter('always') 2024-12-18T00:16:36.4195605Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-12-18T00:16:36.4196127Z ... # do something that raises a warning but ignore those in 2024-12-18T00:16:36.4196524Z ... # np.core.fromnumeric 2024-12-18T00:16:36.4196815Z 2024-12-18T00:16:36.4197190Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.4197561Z 2024-12-18T00:16:36.5698600Z 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. 2024-12-18T00:16:36.5699565Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.5700184Z Applies a 1D convolution over a quantized input signal composed of 2024-12-18T00:16:36.5700636Z several quantized input planes. 2024-12-18T00:16:36.5700867Z 2024-12-18T00:16:36.5701084Z For details on input arguments, parameters, and implementation see 2024-12-18T00:16:36.5701532Z :class:`~torch.nn.Conv1d`. 2024-12-18T00:16:36.5701728Z 2024-12-18T00:16:36.5701855Z .. note:: 2024-12-18T00:16:36.5702182Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-12-18T00:16:36.5702514Z 2024-12-18T00:16:36.5702603Z .. note:: 2024-12-18T00:16:36.5702916Z Only `torch.quint8` is supported for the input data type. 2024-12-18T00:16:36.5703224Z 2024-12-18T00:16:36.5703228Z 2024-12-18T00:16:36.5703320Z Attributes: 2024-12-18T00:16:36.5703676Z weight (Tensor): packed tensor derived from the learnable weight 2024-12-18T00:16:36.5704115Z parameter. 2024-12-18T00:16:36.5704488Z scale (Tensor): scalar for the output scale 2024-12-18T00:16:36.5704903Z zero_point (Tensor): scalar for the output zero point 2024-12-18T00:16:36.5705190Z 2024-12-18T00:16:36.5705341Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-12-18T00:16:36.5705615Z 2024-12-18T00:16:36.5705708Z Examples:: 2024-12-18T00:16:36.5705856Z 2024-12-18T00:16:36.5706007Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-12-18T00:16:36.5706405Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-12-18T00:16:36.5706768Z >>> input = torch.randn(20, 16, 100) 2024-12-18T00:16:36.5707092Z >>> # quantize input to quint8 2024-12-18T00:16:36.5707409Z >>> # xdoctest: +SKIP 2024-12-18T00:16:36.5707807Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-12-18T00:16:36.5708273Z ... dtype=torch.quint8) 2024-12-18T00:16:36.5708622Z >>> output = m(q_input) 2024-12-18T00:16:36.5709048Z 2024-12-18T00:16:36.5709133Z 2024-12-18T00:16:36.5709506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.5709888Z 2024-12-18T00:16:36.5884722Z 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. 2024-12-18T00:16:36.5885980Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.5886500Z A quantized long short-term memory (LSTM). 2024-12-18T00:16:36.5886733Z 2024-12-18T00:16:36.5887020Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-12-18T00:16:36.5887415Z 2024-12-18T00:16:36.5887523Z Attributes: 2024-12-18T00:16:36.5887776Z layers : instances of the `_LSTMLayer` 2024-12-18T00:16:36.5888021Z 2024-12-18T00:16:36.5888120Z .. note:: 2024-12-18T00:16:36.5888468Z To access the weights and biases, you need to access them per layer. 2024-12-18T00:16:36.5888986Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-12-18T00:16:36.5889272Z 2024-12-18T00:16:36.5889379Z Examples:: 2024-12-18T00:16:36.5889622Z >>> # xdoctest: +SKIP 2024-12-18T00:16:36.5889905Z >>> custom_module_config = { 2024-12-18T00:16:36.5890256Z ... 'float_to_observed_custom_module_class': { 2024-12-18T00:16:36.5890637Z ... nn.LSTM: nn.quantizable.LSTM, 2024-12-18T00:16:36.5890966Z ... }, 2024-12-18T00:16:36.5891264Z ... 'observed_to_quantized_custom_module_class': { 2024-12-18T00:16:36.5891666Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-12-18T00:16:36.5892016Z ... } 2024-12-18T00:16:36.5892247Z ... } 2024-12-18T00:16:36.5892602Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-12-18T00:16:36.5893158Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-12-18T00:16:36.5893573Z 2024-12-18T00:16:36.5893945Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.5894324Z 2024-12-18T00:16:36.6700984Z 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. 2024-12-18T00:16:36.6702131Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.6702692Z Squashes the sparse masks into the appropriate tensors. 2024-12-18T00:16:36.6702977Z 2024-12-18T00:16:36.6703201Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-12-18T00:16:36.6703727Z the module will have a `sparse_params` dict attached to it. 2024-12-18T00:16:36.6704023Z 2024-12-18T00:16:36.6704124Z Args: 2024-12-18T00:16:36.6704434Z params_to_keep: List of keys to save in the module or a dict 2024-12-18T00:16:36.6704907Z representing the modules and keys that will have 2024-12-18T00:16:36.6705332Z sparsity parameters saved 2024-12-18T00:16:36.6705785Z params_to_keep_per_layer: Dict to specify the params that should be 2024-12-18T00:16:36.6706282Z saved for specific layers. The keys in the dict 2024-12-18T00:16:36.6706711Z should be the module fqn, while the values should 2024-12-18T00:16:36.6707160Z be a list of strings with the names of the variables 2024-12-18T00:16:36.6707574Z to save in the `sparse_params` 2024-12-18T00:16:36.6707825Z 2024-12-18T00:16:36.6707915Z Examples: 2024-12-18T00:16:36.6708198Z >>> # xdoctest: +SKIP("locals are undefined") 2024-12-18T00:16:36.6708567Z >>> # Don't save any sparse params 2024-12-18T00:16:36.6708902Z >>> sparsifier.squash_mask() 2024-12-18T00:16:36.6709266Z >>> hasattr(model.submodule1, 'sparse_params') 2024-12-18T00:16:36.6709874Z False 2024-12-18T00:16:36.6710011Z 2024-12-18T00:16:36.6710143Z >>> # Keep sparse params per layer 2024-12-18T00:16:36.6710490Z >>> sparsifier.squash_mask( 2024-12-18T00:16:36.6710814Z ... params_to_keep_per_layer={ 2024-12-18T00:16:36.6711182Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-12-18T00:16:36.6711690Z ... 'submodule2.linear42': ('baz',) 2024-12-18T00:16:36.6712024Z ... }) 2024-12-18T00:16:36.6712340Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T00:16:36.6712704Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:36.6713065Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T00:16:36.6713434Z {'baz': 0.1} 2024-12-18T00:16:36.6713596Z 2024-12-18T00:16:36.6713734Z >>> # Keep sparse params for all layers 2024-12-18T00:16:36.6714154Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-12-18T00:16:36.6714611Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T00:16:36.6714973Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:36.6715331Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T00:16:36.6715709Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:36.6715900Z 2024-12-18T00:16:36.6716112Z >>> # Keep some sparse params for all layers, and specific ones for 2024-12-18T00:16:36.6716537Z >>> # some other layers 2024-12-18T00:16:36.6716843Z >>> sparsifier.squash_mask( 2024-12-18T00:16:36.6717186Z ... params_to_keep=('foo', 'bar'), 2024-12-18T00:16:36.6717544Z ... params_to_keep_per_layer={ 2024-12-18T00:16:36.6717903Z ... 'submodule2.linear42': ('baz',) 2024-12-18T00:16:36.6718237Z ... }) 2024-12-18T00:16:36.6718628Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T00:16:36.6719014Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:36.6719381Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T00:16:36.6719770Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-12-18T00:16:36.6720087Z 2024-12-18T00:16:36.6720454Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.6720834Z 2024-12-18T00:16:36.7436621Z 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. 2024-12-18T00:16:36.7437691Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.7438070Z 2024-12-18T00:16:36.7438339Z Config object that specifies the supported data types passed as arguments to 2024-12-18T00:16:36.7439010Z quantize ops in the reference model spec, for input and output activations, 2024-12-18T00:16:36.7439456Z weights, and biases. 2024-12-18T00:16:36.7439635Z 2024-12-18T00:16:36.7439793Z For example, consider the following reference model: 2024-12-18T00:16:36.7440081Z 2024-12-18T00:16:36.7440243Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-12-18T00:16:36.7440648Z 2024-12-18T00:16:36.7440868Z The pattern in the square brackets refers to the reference pattern of 2024-12-18T00:16:36.7441439Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-12-18T00:16:36.7442034Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-12-18T00:16:36.7442595Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-12-18T00:16:36.7443164Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-12-18T00:16:36.7443626Z the second quantize op (quant2). 2024-12-18T00:16:36.7443835Z 2024-12-18T00:16:36.7444055Z Note that the dtype here does not refer to the interface dtypes of the 2024-12-18T00:16:36.7444595Z op. For example, the "input dtype" here is not the dtype of the input 2024-12-18T00:16:36.7445304Z tensor passed to the quantized linear op. Though it can still be the 2024-12-18T00:16:36.7445881Z same as the interface dtype, this is not always the case, e.g. the 2024-12-18T00:16:36.7446417Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-12-18T00:16:36.7446968Z specified in the DTypeConfig would still be quint8. The semantics of 2024-12-18T00:16:36.7447617Z dtypes here are the same as the semantics of the dtypes specified in 2024-12-18T00:16:36.7448045Z the observers. 2024-12-18T00:16:36.7448181Z 2024-12-18T00:16:36.7448402Z These dtypes are matched against the ones specified in the user's 2024-12-18T00:16:36.7448933Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-12-18T00:16:36.7449498Z specified in the DTypeConfig (if any), then we will quantize the given 2024-12-18T00:16:36.7450065Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-12-18T00:16:36.7450524Z the pattern will not be quantized. 2024-12-18T00:16:36.7450732Z 2024-12-18T00:16:36.7450876Z Example usage:: 2024-12-18T00:16:36.7451017Z 2024-12-18T00:16:36.7451137Z >>> # xdoctest: +SKIP(failing) 2024-12-18T00:16:36.7451445Z >>> dtype_config1 = DTypeConfig( 2024-12-18T00:16:36.7451771Z ... input_dtype=torch.quint8, 2024-12-18T00:16:36.7452102Z ... output_dtype=torch.quint8, 2024-12-18T00:16:36.7452428Z ... weight_dtype=torch.qint8, 2024-12-18T00:16:36.7452748Z ... bias_dtype=torch.float) 2024-12-18T00:16:36.7452949Z 2024-12-18T00:16:36.7453060Z >>> dtype_config2 = DTypeConfig( 2024-12-18T00:16:36.7453401Z ... input_dtype=DTypeWithConstraints( 2024-12-18T00:16:36.7453746Z ... dtype=torch.quint8, 2024-12-18T00:16:36.7454063Z ... quant_min_lower_bound=0, 2024-12-18T00:16:36.7454396Z ... quant_max_upper_bound=255, 2024-12-18T00:16:36.7454696Z ... ), 2024-12-18T00:16:36.7454988Z ... output_dtype=DTypeWithConstraints( 2024-12-18T00:16:36.7455339Z ... dtype=torch.quint8, 2024-12-18T00:16:36.7455652Z ... quant_min_lower_bound=0, 2024-12-18T00:16:36.7455986Z ... quant_max_upper_bound=255, 2024-12-18T00:16:36.7456287Z ... ), 2024-12-18T00:16:36.7456560Z ... weight_dtype=DTypeWithConstraints( 2024-12-18T00:16:36.7456915Z ... dtype=torch.qint8, 2024-12-18T00:16:36.7457234Z ... quant_min_lower_bound=-128, 2024-12-18T00:16:36.7457572Z ... quant_max_upper_bound=127, 2024-12-18T00:16:36.7457871Z ... ), 2024-12-18T00:16:36.7458117Z ... bias_dtype=torch.float) 2024-12-18T00:16:36.7458331Z 2024-12-18T00:16:36.7458441Z >>> dtype_config1.input_dtype 2024-12-18T00:16:36.7458738Z torch.quint8 2024-12-18T00:16:36.7458880Z 2024-12-18T00:16:36.7458996Z >>> dtype_config2.input_dtype 2024-12-18T00:16:36.7459286Z torch.quint8 2024-12-18T00:16:36.7459437Z 2024-12-18T00:16:36.7459575Z >>> dtype_config2.input_dtype_with_constraints 2024-12-18T00:16:36.7460375Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-12-18T00:16:36.7461038Z 2024-12-18T00:16:36.7461290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.7461671Z 2024-12-18T00:16:36.8454069Z 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. 2024-12-18T00:16:36.8455828Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.8456215Z 2024-12-18T00:16:36.8456483Z Takes in optional filter values and generates two tables with desired information. 2024-12-18T00:16:36.8456883Z 2024-12-18T00:16:36.8457093Z The generated tables are presented in both a list-of-lists format 2024-12-18T00:16:36.8457451Z 2024-12-18T00:16:36.8457657Z The reason for the two tables are that they handle different things: 2024-12-18T00:16:36.8458421Z 1.) the first table handles all tensor level information 2024-12-18T00:16:36.8458962Z 2.) the second table handles and displays all channel based information 2024-12-18T00:16:36.8459515Z 2024-12-18T00:16:36.8460112Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T00:16:36.8461004Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T00:16:36.8461836Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T00:16:36.8462740Z 2024-12-18T00:16:36.8462855Z Tensor table columns: 2024-12-18T00:16:36.8463220Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:36.8463686Z ---- --------- --------- --------- --------- --------- 2024-12-18T00:16:36.8463975Z 2024-12-18T00:16:36.8464086Z Per-Channel table columns: 2024-12-18T00:16:36.8464504Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:36.8465018Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T00:16:36.8465303Z 2024-12-18T00:16:36.8465405Z Args: 2024-12-18T00:16:36.8465786Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T00:16:36.8466300Z contain this filter substring 2024-12-18T00:16:36.8466684Z Default = "", results in all the features being printed 2024-12-18T00:16:36.8467232Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:36.8467874Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:36.8468240Z 2024-12-18T00:16:36.8468368Z Returns a dictionary with two keys: 2024-12-18T00:16:36.8468749Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-12-18T00:16:36.8469169Z "tensor_level_info", "channel_level_info" 2024-12-18T00:16:36.8469526Z Each key maps to a tuple with: 2024-12-18T00:16:36.8469863Z A list of the headers of each table 2024-12-18T00:16:36.8470280Z A list of lists containing the table information row by row 2024-12-18T00:16:36.8470758Z The 0th index row will contain the headers of the columns 2024-12-18T00:16:36.8471161Z The rest of the rows will contain data 2024-12-18T00:16:36.8471410Z 2024-12-18T00:16:36.8471502Z Example Use: 2024-12-18T00:16:36.8471763Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:36.8472149Z >>> mod_report_visualizer.generate_filtered_tables( 2024-12-18T00:16:36.8472537Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:36.8472869Z ... module_fqn_filter = "block1" 2024-12-18T00:16:36.8473355Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-12-18T00:16:36.8473997Z 2024-12-18T00:16:36.8474255Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.8474641Z 2024-12-18T00:16:36.8475547Z 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. 2024-12-18T00:16:36.8476822Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.8477198Z 2024-12-18T00:16:36.8477479Z Takes in optional filter values and prints out formatted tables of the information. 2024-12-18T00:16:36.8477866Z 2024-12-18T00:16:36.8478222Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-12-18T00:16:36.8478914Z 1.) the first table handles all tensor level information 2024-12-18T00:16:36.8479415Z 2.) the second table handles and displays all channel based information 2024-12-18T00:16:36.8479759Z 2024-12-18T00:16:36.8480182Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T00:16:36.8481002Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T00:16:36.8481813Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T00:16:36.8482283Z 2024-12-18T00:16:36.8482443Z Tensor table columns: 2024-12-18T00:16:36.8482843Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:36.8483296Z ---- --------- --------- --------- --------- --------- 2024-12-18T00:16:36.8483573Z 2024-12-18T00:16:36.8483680Z Per-Channel table columns: 2024-12-18T00:16:36.8483870Z 2024-12-18T00:16:36.8484088Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:36.8484591Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T00:16:36.8484870Z 2024-12-18T00:16:36.8484973Z Args: 2024-12-18T00:16:36.8485367Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T00:16:36.8485853Z contain this filter substring 2024-12-18T00:16:36.8486236Z Default = "", results in all the features being printed 2024-12-18T00:16:36.8486780Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:36.8487419Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:36.8487782Z 2024-12-18T00:16:36.8487887Z Example Use: 2024-12-18T00:16:36.8488141Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:36.8488547Z >>> mod_report_visualizer.generate_table_visualization( 2024-12-18T00:16:36.8488955Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:36.8489303Z ... module_fqn_filter = "block1" 2024-12-18T00:16:36.8489609Z ... ) 2024-12-18T00:16:36.8489914Z >>> # prints out neatly formatted table with per_channel_min info 2024-12-18T00:16:36.8490351Z >>> # for all modules in block 1 of the model 2024-12-18T00:16:36.8490594Z 2024-12-18T00:16:36.8490843Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.8491220Z 2024-12-18T00:16:36.8492153Z 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. 2024-12-18T00:16:36.8493420Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.8493805Z 2024-12-18T00:16:36.8494039Z Takes in a feature and optional module_filter and plots of the desired data. 2024-12-18T00:16:36.8494405Z 2024-12-18T00:16:36.8494675Z For per channel features, it averages the value across the channels and plots a point 2024-12-18T00:16:36.8495332Z per module. The reason for this is that for models with hundreds of channels, it can 2024-12-18T00:16:36.8495993Z be hard to differentiate one channel line from another, and so the point of generating 2024-12-18T00:16:36.8496659Z a single average point per module is to give a sense of general trends that encourage 2024-12-18T00:16:36.8497138Z further deep dives. 2024-12-18T00:16:36.8497297Z 2024-12-18T00:16:36.8497382Z Note: 2024-12-18T00:16:36.8497766Z Only features in the report that have tensor value data are plottable by this class 2024-12-18T00:16:36.8498328Z When the tensor information is plotted, it will plot: 2024-12-18T00:16:36.8498741Z idx as the x val, feature value as the y_val 2024-12-18T00:16:36.8499147Z When the channel information is plotted, it will plot: 2024-12-18T00:16:36.8499690Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-12-18T00:16:36.8500307Z The reason for this is that we want to be able to compare values across the 2024-12-18T00:16:36.8500897Z channels for same layer, and it will be hard if values are staggered by idx 2024-12-18T00:16:36.8501499Z This means each module is represented by only 1 x value 2024-12-18T00:16:36.8501873Z Args: 2024-12-18T00:16:36.8502209Z feature_filter (str): Filters the features presented to only those that 2024-12-18T00:16:36.8502669Z contain this filter substring 2024-12-18T00:16:36.8503177Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:36.8503841Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:36.8504207Z 2024-12-18T00:16:36.8504315Z Example Use: 2024-12-18T00:16:36.8504574Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:36.8504984Z >>> mod_report_visualizer.generate_plot_visualization( 2024-12-18T00:16:36.8505387Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:36.8505738Z ... module_fqn_filter = "block1" 2024-12-18T00:16:36.8506041Z ... ) 2024-12-18T00:16:36.8506336Z >>> # outputs line plot of per_channel_min information for all 2024-12-18T00:16:36.8506823Z >>> # modules in block1 of model each channel gets it's own line, 2024-12-18T00:16:36.8507305Z >>> # and it's plotted across the in-order modules on the x-axis 2024-12-18T00:16:36.8507604Z 2024-12-18T00:16:36.8507855Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.8508220Z 2024-12-18T00:16:36.8509154Z 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. 2024-12-18T00:16:36.8510448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:36.8510825Z 2024-12-18T00:16:36.8511113Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-12-18T00:16:36.8511508Z 2024-12-18T00:16:36.8511609Z Note: 2024-12-18T00:16:36.8511988Z Only features in the report that have tensor value data can be viewed as a histogram 2024-12-18T00:16:36.8512649Z If you want to plot a histogram from all the channel values of a specific feature for 2024-12-18T00:16:36.8513288Z a specific model, make sure to specify both the model and the feature properly 2024-12-18T00:16:36.8513915Z in the filters and you should be able to see a distribution of the channel data 2024-12-18T00:16:36.8514284Z 2024-12-18T00:16:36.8514384Z Args: 2024-12-18T00:16:36.8514760Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T00:16:36.8515265Z contain this filter substring 2024-12-18T00:16:36.8515647Z Default = "", results in all the features being printed 2024-12-18T00:16:36.8516194Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:36.8516832Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:36.8517440Z num_bins (int, optional): The number of bins to create the histogram with 2024-12-18T00:16:36.8517966Z Default = 10, the values will be split into 10 equal sized bins 2024-12-18T00:16:36.8518281Z 2024-12-18T00:16:36.8518475Z Example Use: 2024-12-18T00:16:36.8518724Z >>> # xdoctest: +SKIP 2024-12-18T00:16:36.8519202Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-12-18T00:16:36.8519754Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:36.8520090Z ... module_fqn_filter = "block1" 2024-12-18T00:16:36.8520401Z ... ) 2024-12-18T00:16:36.8520803Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-12-18T00:16:36.8521463Z information is gathered across all channels for all modules in block 1 for the 2024-12-18T00:16:36.8522061Z per_channel_min and is displayed in a histogram of equally sized bins 2024-12-18T00:16:36.8522453Z 2024-12-18T00:16:36.8522716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:36.8523111Z 2024-12-18T00:16:37.0760095Z 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=660. 2024-12-18T00:16:37.0761321Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:37.0761704Z 2024-12-18T00:16:37.0762038Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-12-18T00:16:37.0762704Z The submesh created consists of the dimensions and the communicators indicated by 2024-12-18T00:16:37.0763193Z ``mesh_dim_names`` 2024-12-18T00:16:37.0763341Z 2024-12-18T00:16:37.0763429Z Args: 2024-12-18T00:16:37.0763790Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-12-18T00:16:37.0764336Z mesh dimension of the DeviceMesh to create the submesh for. 2024-12-18T00:16:37.0764738Z Returns: 2024-12-18T00:16:37.0764982Z A :class:`DeviceMesh` object 2024-12-18T00:16:37.0765178Z 2024-12-18T00:16:37.0765458Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-12-18T00:16:37.0765970Z In the first example: 2024-12-18T00:16:37.0766389Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-12-18T00:16:37.0767028Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-12-18T00:16:37.0767624Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-12-18T00:16:37.0768193Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-12-18T00:16:37.0768762Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-12-18T00:16:37.0769318Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-12-18T00:16:37.0769670Z 2024-12-18T00:16:37.0769772Z In the second example: 2024-12-18T00:16:37.0770209Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-12-18T00:16:37.0770859Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-12-18T00:16:37.0771506Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-12-18T00:16:37.0772154Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-12-18T00:16:37.0772532Z 2024-12-18T00:16:37.0772649Z Example:: 2024-12-18T00:16:37.0772891Z >>> # xdoctest: +SKIP("no rank") 2024-12-18T00:16:37.0773276Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-12-18T00:16:37.0773869Z >>> 2024-12-18T00:16:37.0774246Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-12-18T00:16:37.0774702Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-12-18T00:16:37.0775219Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T00:16:37.0775697Z >>> tp_mesh = mesh_2d["tp"] 2024-12-18T00:16:37.0775988Z >>> dp_mesh = mesh_2d["dp"] 2024-12-18T00:16:37.0776262Z >>> 2024-12-18T00:16:37.0776472Z >>> # Initialize a 3D mesh. 2024-12-18T00:16:37.0776949Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-12-18T00:16:37.0777652Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-12-18T00:16:37.0778196Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-12-18T00:16:37.0778531Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-12-18T00:16:37.0778746Z 2024-12-18T00:16:37.0779429Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-12-18T00:16:37.0780305Z 2024-12-18T00:16:37.0780618Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T00:16:37.0781100Z ^ 2024-12-18T00:16:37.1105845Z 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=3063. 2024-12-18T00:16:37.1106994Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.1107394Z 2024-12-18T00:16:37.1107619Z Gathers picklable objects from the whole group in a single process. 2024-12-18T00:16:37.1107969Z 2024-12-18T00:16:37.1108203Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-12-18T00:16:37.1108711Z object must be picklable in order to be gathered. 2024-12-18T00:16:37.1108971Z 2024-12-18T00:16:37.1109070Z Args: 2024-12-18T00:16:37.1109309Z obj (Any): Input object. Must be picklable. 2024-12-18T00:16:37.1109770Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-12-18T00:16:37.1110291Z should be correctly sized as the size of the group for this 2024-12-18T00:16:37.1110812Z collective and will contain the output. Must be ``None`` on non-dst 2024-12-18T00:16:37.1111261Z ranks. (default is ``None``) 2024-12-18T00:16:37.1111799Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2024-12-18T00:16:37.1112425Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2024-12-18T00:16:37.1112976Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T00:16:37.1113519Z the default process group will be used. Default is ``None``. 2024-12-18T00:16:37.1114170Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2024-12-18T00:16:37.1114626Z 2024-12-18T00:16:37.1114736Z Returns: 2024-12-18T00:16:37.1115052Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-12-18T00:16:37.1115469Z output of the collective. 2024-12-18T00:16:37.1115656Z 2024-12-18T00:16:37.1115903Z .. note:: Note that this API differs slightly from the gather collective 2024-12-18T00:16:37.1116458Z since it does not provide an async_op handle and thus will be a blocking 2024-12-18T00:16:37.1116878Z call. 2024-12-18T00:16:37.1117015Z 2024-12-18T00:16:37.1117250Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-12-18T00:16:37.1117817Z of objects must be moved to the GPU device before communication takes 2024-12-18T00:16:37.1118322Z place. In this case, the device used is given by 2024-12-18T00:16:37.1118879Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-12-18T00:16:37.1119432Z ensure that this is set so that each rank has an individual GPU, via 2024-12-18T00:16:37.1119877Z ``torch.cuda.set_device()``. 2024-12-18T00:16:37.1120076Z 2024-12-18T00:16:37.1120174Z .. warning:: 2024-12-18T00:16:37.1120514Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-12-18T00:16:37.1121050Z known to be insecure. It is possible to construct malicious pickle data 2024-12-18T00:16:37.1121612Z which will execute arbitrary code during unpickling. Only call this 2024-12-18T00:16:37.1122065Z function with data you trust. 2024-12-18T00:16:37.1122264Z 2024-12-18T00:16:37.1122359Z .. warning:: 2024-12-18T00:16:37.1122706Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-12-18T00:16:37.1123273Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-12-18T00:16:37.1123788Z pickled. Please consider using :func:`gather` instead. 2024-12-18T00:16:37.1124068Z 2024-12-18T00:16:37.1124170Z Example:: 2024-12-18T00:16:37.1124433Z >>> # xdoctest: +SKIP("need process group init") 2024-12-18T00:16:37.1124851Z >>> # Note: Process group initialization omitted on each rank. 2024-12-18T00:16:37.1125397Z >>> import torch.distributed as dist 2024-12-18T00:16:37.1125733Z >>> # Assumes world_size of 3. 2024-12-18T00:16:37.1126113Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-12-18T00:16:37.1126532Z >>> output = [None for _ in gather_objects] 2024-12-18T00:16:37.1126858Z >>> dist.gather_object( 2024-12-18T00:16:37.1127201Z ... gather_objects[dist.get_rank()], 2024-12-18T00:16:37.1127607Z ... output if dist.get_rank() == 0 else None, 2024-12-18T00:16:37.1127945Z ... dst=0 2024-12-18T00:16:37.1128181Z ... ) 2024-12-18T00:16:37.1128390Z >>> # On rank 0 2024-12-18T00:16:37.1128636Z >>> output 2024-12-18T00:16:37.1128865Z ['foo', 12, {1: 2}] 2024-12-18T00:16:37.1129020Z 2024-12-18T00:16:37.1129283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.1129649Z 2024-12-18T00:16:37.1283241Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2024-12-18T00:16:37.1284476Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.1285251Z 2024-12-18T00:16:37.1285403Z Module ``torch.distributed.launch``. 2024-12-18T00:16:37.1285622Z 2024-12-18T00:16:37.1285886Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-12-18T00:16:37.1286417Z training processes on each of the training nodes. 2024-12-18T00:16:37.1286678Z 2024-12-18T00:16:37.1286783Z .. warning:: 2024-12-18T00:16:37.1286923Z 2024-12-18T00:16:37.1287179Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-12-18T00:16:37.1287565Z 2024-12-18T00:16:37.1287804Z The utility can be used for single-node distributed training, in which one or 2024-12-18T00:16:37.1288401Z more processes per node will be spawned. The utility can be used for either 2024-12-18T00:16:37.1288979Z CPU training or GPU training. If the utility is used for GPU training, 2024-12-18T00:16:37.1289567Z each distributed process will be operating on a single GPU. This can achieve 2024-12-18T00:16:37.1290158Z well-improved single-node training performance. It can also be used in 2024-12-18T00:16:37.1290779Z multi-node distributed training, by spawning up multiple processes on each node 2024-12-18T00:16:37.1291399Z for well-improved multi-node distributed training performance as well. 2024-12-18T00:16:37.1291981Z This will especially be beneficial for systems with multiple Infiniband 2024-12-18T00:16:37.1292585Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-12-18T00:16:37.1293079Z aggregated communication bandwidth. 2024-12-18T00:16:37.1293287Z 2024-12-18T00:16:37.1293519Z In both cases of single-node distributed training or multi-node distributed 2024-12-18T00:16:37.1294114Z training, this utility will launch the given number of processes per node 2024-12-18T00:16:37.1294699Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-12-18T00:16:37.1295279Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-12-18T00:16:37.1295820Z and each process will be operating on a single GPU from *GPU 0 to 2024-12-18T00:16:37.1296251Z GPU (nproc_per_node - 1)*. 2024-12-18T00:16:37.1296429Z 2024-12-18T00:16:37.1296533Z **How to use this module:** 2024-12-18T00:16:37.1296722Z 2024-12-18T00:16:37.1296882Z 1. Single-Node multi-process distributed training 2024-12-18T00:16:37.1297155Z 2024-12-18T00:16:37.1297243Z :: 2024-12-18T00:16:37.1297364Z 2024-12-18T00:16:37.1297601Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T00:16:37.1298147Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-12-18T00:16:37.1298585Z arguments of your training script) 2024-12-18T00:16:37.1298818Z 2024-12-18T00:16:37.1299028Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-12-18T00:16:37.1299479Z 2024-12-18T00:16:37.1299482Z 2024-12-18T00:16:37.1299677Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-12-18T00:16:37.1299939Z 2024-12-18T00:16:37.1300025Z :: 2024-12-18T00:16:37.1300148Z 2024-12-18T00:16:37.1300382Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T00:16:37.1300900Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-12-18T00:16:37.1301486Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T00:16:37.1301961Z and all other arguments of your training script) 2024-12-18T00:16:37.1302238Z 2024-12-18T00:16:37.1302325Z Node 2: 2024-12-18T00:16:37.1302453Z 2024-12-18T00:16:37.1302538Z :: 2024-12-18T00:16:37.1302662Z 2024-12-18T00:16:37.1302896Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T00:16:37.1303414Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-12-18T00:16:37.1303901Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T00:16:37.1304379Z and all other arguments of your training script) 2024-12-18T00:16:37.1304655Z 2024-12-18T00:16:37.1304817Z 3. To look up what optional arguments this module offers: 2024-12-18T00:16:37.1305102Z 2024-12-18T00:16:37.1305188Z :: 2024-12-18T00:16:37.1305311Z 2024-12-18T00:16:37.1305452Z python -m torch.distributed.launch --help 2024-12-18T00:16:37.1305695Z 2024-12-18T00:16:37.1305701Z 2024-12-18T00:16:37.1305813Z **Important Notices:** 2024-12-18T00:16:37.1305979Z 2024-12-18T00:16:37.1306177Z 1. This utility and multi-process distributed (single-node or 2024-12-18T00:16:37.1306733Z multi-node) GPU training currently only achieves the best performance using 2024-12-18T00:16:37.1307339Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-12-18T00:16:37.1307815Z use for GPU training. 2024-12-18T00:16:37.1307989Z 2024-12-18T00:16:37.1308206Z 2. In your training program, you must parse the command-line argument: 2024-12-18T00:16:37.1308778Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-12-18T00:16:37.1309364Z If your training program uses GPUs, you should ensure that your code only 2024-12-18T00:16:37.1309896Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-12-18T00:16:37.1310216Z 2024-12-18T00:16:37.1310330Z Parsing the local_rank argument 2024-12-18T00:16:37.1310537Z 2024-12-18T00:16:37.1310625Z :: 2024-12-18T00:16:37.1310751Z 2024-12-18T00:16:37.1310854Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.1311129Z >>> import argparse 2024-12-18T00:16:37.1311428Z >>> parser = argparse.ArgumentParser() 2024-12-18T00:16:37.1311850Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-12-18T00:16:37.1312281Z >>> args = parser.parse_args() 2024-12-18T00:16:37.1312500Z 2024-12-18T00:16:37.1312626Z Set your device to local rank using either 2024-12-18T00:16:37.1312856Z 2024-12-18T00:16:37.1312962Z :: 2024-12-18T00:16:37.1313071Z 2024-12-18T00:16:37.1313291Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-12-18T00:16:37.1313611Z 2024-12-18T00:16:37.1313710Z or 2024-12-18T00:16:37.1313817Z 2024-12-18T00:16:37.1313906Z :: 2024-12-18T00:16:37.1314024Z 2024-12-18T00:16:37.1314157Z >>> with torch.cuda.device(args.local_rank): 2024-12-18T00:16:37.1314508Z >>> # your code to run 2024-12-18T00:16:37.1314788Z >>> ... 2024-12-18T00:16:37.1314920Z 2024-12-18T00:16:37.1315042Z .. versionchanged:: 2.0.0 2024-12-18T00:16:37.1315211Z 2024-12-18T00:16:37.1315456Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-12-18T00:16:37.1316067Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-12-18T00:16:37.1316573Z previously used underscored ``--local_rank``. 2024-12-18T00:16:37.1316834Z 2024-12-18T00:16:37.1317069Z For backward compatibility, it may be necessary for users to handle both 2024-12-18T00:16:37.1317726Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-12-18T00:16:37.1318499Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-12-18T00:16:37.1319079Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-12-18T00:16:37.1319728Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-12-18T00:16:37.1320264Z including ``"--local-rank"`` should be sufficient. 2024-12-18T00:16:37.1320536Z 2024-12-18T00:16:37.1320768Z 3. In your training program, you are supposed to call the following function 2024-12-18T00:16:37.1321361Z at the beginning to start the distributed backend. It is strongly recommended 2024-12-18T00:16:37.1323453Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-12-18T00:16:37.1324000Z but ``env://`` is the one that is officially supported by this module. 2024-12-18T00:16:37.1324322Z 2024-12-18T00:16:37.1324425Z :: 2024-12-18T00:16:37.1324551Z 2024-12-18T00:16:37.1324758Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-12-18T00:16:37.1325228Z >>> init_method='env://') 2024-12-18T00:16:37.1325469Z 2024-12-18T00:16:37.1325723Z 4. In your training program, you can either use regular distributed functions 2024-12-18T00:16:37.1326338Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-12-18T00:16:37.1326903Z training program uses GPUs for training and you would like to use 2024-12-18T00:16:37.1327412Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-12-18T00:16:37.1327824Z here is how to configure it. 2024-12-18T00:16:37.1328020Z 2024-12-18T00:16:37.1328108Z :: 2024-12-18T00:16:37.1328218Z 2024-12-18T00:16:37.1328425Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-12-18T00:16:37.1328881Z >>> device_ids=[args.local_rank], 2024-12-18T00:16:37.1329284Z >>> output_device=args.local_rank) 2024-12-18T00:16:37.1329562Z 2024-12-18T00:16:37.1329807Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-12-18T00:16:37.1330402Z that your code will be operating on. This is generally the local rank of the 2024-12-18T00:16:37.1330994Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-12-18T00:16:37.1331577Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-12-18T00:16:37.1332007Z utility 2024-12-18T00:16:37.1332123Z 2024-12-18T00:16:37.1332368Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-12-18T00:16:37.1332952Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-12-18T00:16:37.1333511Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-12-18T00:16:37.1334051Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-12-18T00:16:37.1334543Z will not pass ``--local-rank`` when you specify this flag. 2024-12-18T00:16:37.1334825Z 2024-12-18T00:16:37.1334930Z .. warning:: 2024-12-18T00:16:37.1335059Z 2024-12-18T00:16:37.1335260Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-12-18T00:16:37.1335772Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-12-18T00:16:37.1336200Z write to a networked filesystem. See 2024-12-18T00:16:37.1336648Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-12-18T00:16:37.1337148Z how things can go wrong if you don't do this correctly. 2024-12-18T00:16:37.1337420Z 2024-12-18T00:16:37.1337424Z 2024-12-18T00:16:37.1337427Z 2024-12-18T00:16:37.1337431Z 2024-12-18T00:16:37.1337692Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.1338057Z 2024-12-18T00:16:37.1703515Z 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. 2024-12-18T00:16:37.1704846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.1705222Z 2024-12-18T00:16:37.1705477Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-12-18T00:16:37.1706025Z Needs to be called on all ranks in an SPMD fashion. 2024-12-18T00:16:37.1706295Z 2024-12-18T00:16:37.1706431Z Args: 2024-12-18T00:16:37.1706826Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-12-18T00:16:37.1707402Z of shards that represent the local shards on this rank. 2024-12-18T00:16:37.1707918Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-12-18T00:16:37.1708374Z shape of the overall sharded tensor. 2024-12-18T00:16:37.1708610Z 2024-12-18T00:16:37.1708702Z Keyword args: 2024-12-18T00:16:37.1709105Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T00:16:37.1709629Z the default process group will be used. 2024-12-18T00:16:37.1710048Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T00:16:37.1710549Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T00:16:37.1711068Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T00:16:37.1711484Z Default: ``False``. 2024-12-18T00:16:37.1711669Z 2024-12-18T00:16:37.1711758Z Returns: 2024-12-18T00:16:37.1712037Z A :class:`ShardedTensor` object handle on this rank 2024-12-18T00:16:37.1712299Z 2024-12-18T00:16:37.1712303Z 2024-12-18T00:16:37.1712409Z Examples: 2024-12-18T00:16:37.1712779Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-12-18T00:16:37.1713342Z each shard have a (5, 5) local tensor, we can do it like below: 2024-12-18T00:16:37.1713655Z 2024-12-18T00:16:37.1713746Z on rank 0: 2024-12-18T00:16:37.1714011Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T00:16:37.1714364Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T00:16:37.1714706Z >>> shard_offsets=[0, 0], 2024-12-18T00:16:37.1714994Z >>> shard_lengths=[5, 5], 2024-12-18T00:16:37.1715302Z >>> placement="rank:0/cuda:0" 2024-12-18T00:16:37.1715601Z >>> ) 2024-12-18T00:16:37.1715926Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T00:16:37.1716454Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T00:16:37.1716779Z 2024-12-18T00:16:37.1716871Z on rank 1: 2024-12-18T00:16:37.1717134Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T00:16:37.1717493Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T00:16:37.1717834Z >>> shard_offsets=[5, 0], 2024-12-18T00:16:37.1718140Z >>> shard_lengths=[5, 5], 2024-12-18T00:16:37.1718522Z >>> placement="rank:1/cuda:1" 2024-12-18T00:16:37.1718854Z >>> ) 2024-12-18T00:16:37.1719180Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T00:16:37.1719697Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T00:16:37.1720006Z 2024-12-18T00:16:37.1720276Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.1720642Z 2024-12-18T00:16:37.1801855Z 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=784. 2024-12-18T00:16:37.1802971Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.1803349Z 2024-12-18T00:16:37.1803624Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-12-18T00:16:37.1804106Z size and sharding spec on each rank. 2024-12-18T00:16:37.1804333Z 2024-12-18T00:16:37.1804419Z Args: 2024-12-18T00:16:37.1804773Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-12-18T00:16:37.1805579Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-12-18T00:16:37.1806135Z The specification describing how to shard the Tensor. 2024-12-18T00:16:37.1806587Z global_size (Sequence[int]): Size of the sharded tensor. 2024-12-18T00:16:37.1807179Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-12-18T00:16:37.1807700Z Default: None 2024-12-18T00:16:37.1808033Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T00:16:37.1808635Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T00:16:37.1809511Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T00:16:37.1809910Z Default: ``False``. 2024-12-18T00:16:37.1810096Z 2024-12-18T00:16:37.1810185Z Returns: 2024-12-18T00:16:37.1810557Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-12-18T00:16:37.1811047Z tensor stored in the current rank. 2024-12-18T00:16:37.1811269Z 2024-12-18T00:16:37.1811375Z Examples: 2024-12-18T00:16:37.1811591Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.1811908Z >>> # All tensors below are of torch.int64 type. 2024-12-18T00:16:37.1812281Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T00:16:37.1812695Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T00:16:37.1813196Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-12-18T00:16:37.1813600Z >>> local_tensor 2024-12-18T00:16:37.1813858Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-12-18T00:16:37.1814157Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-12-18T00:16:37.1814456Z >>> sharding_dim = 0 2024-12-18T00:16:37.1814775Z >>> sharding_spec = ChunkShardingSpec( 2024-12-18T00:16:37.1815111Z dim=sharding_dim, 2024-12-18T00:16:37.1815395Z placements=[ 2024-12-18T00:16:37.1815652Z "rank:0/cuda:0", 2024-12-18T00:16:37.1815940Z "rank:1/cuda:1", 2024-12-18T00:16:37.1816219Z ], 2024-12-18T00:16:37.1816447Z ) 2024-12-18T00:16:37.1816819Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-12-18T00:16:37.1817295Z >>> st 2024-12-18T00:16:37.1817518Z ShardedTensor( 2024-12-18T00:16:37.1817784Z ShardedTensorMetadata( 2024-12-18T00:16:37.1818081Z shards_metadata=[ 2024-12-18T00:16:37.1818521Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-12-18T00:16:37.1819168Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-12-18T00:16:37.1819641Z ], 2024-12-18T00:16:37.1819881Z size=torch.Size([2, 4]) 2024-12-18T00:16:37.1820173Z ) 2024-12-18T00:16:37.1820378Z >>> st.local_tensor() 2024-12-18T00:16:37.1820648Z tensor([1, 2, 3, 4]) # Rank 0 2024-12-18T00:16:37.1820948Z tensor([3, 4, 5, 6]) # Rank 1 2024-12-18T00:16:37.1821149Z 2024-12-18T00:16:37.1821416Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-12-18T00:16:37.1822052Z rank validations, and we only validate the local shard on the current rank. 2024-12-18T00:16:37.1822635Z We fully rely on the user to ensure local tensor is sharded based on the 2024-12-18T00:16:37.1823058Z sharding spec. 2024-12-18T00:16:37.1823232Z 2024-12-18T00:16:37.1823485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.1823860Z 2024-12-18T00:16:37.1824535Z 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=1023. 2024-12-18T00:16:37.1825579Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.1825953Z 2024-12-18T00:16:37.1826213Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-12-18T00:16:37.1826783Z single local shard. 2024-12-18T00:16:37.1826930Z 2024-12-18T00:16:37.1827167Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-12-18T00:16:37.1827738Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-12-18T00:16:37.1828214Z we swap local shards directly. 2024-12-18T00:16:37.1828760Z For more generic cases, we merge different shards across different ranks and split 2024-12-18T00:16:37.1829400Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-12-18T00:16:37.1829768Z 2024-12-18T00:16:37.1829866Z Args: 2024-12-18T00:16:37.1830265Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-12-18T00:16:37.1830844Z specification describing how the tensor is sharded. 2024-12-18T00:16:37.1831132Z 2024-12-18T00:16:37.1831220Z Returns: 2024-12-18T00:16:37.1831557Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-12-18T00:16:37.1831878Z 2024-12-18T00:16:37.1831983Z Examples: 2024-12-18T00:16:37.1832220Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.1832503Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T00:16:37.1832917Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T00:16:37.1833336Z >>> tensor = torch.stack([tensor, tensor]) 2024-12-18T00:16:37.1833662Z >>> tensor 2024-12-18T00:16:37.1842237Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-12-18T00:16:37.1842713Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-12-18T00:16:37.1843079Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-12-18T00:16:37.1843440Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-12-18T00:16:37.1843782Z >>> sharding_dim = 0 2024-12-18T00:16:37.1844059Z >>> spec = ChunkShardingSpec( 2024-12-18T00:16:37.1844369Z dim=sharding_dim, 2024-12-18T00:16:37.1844658Z placements=[ 2024-12-18T00:16:37.1844938Z "rank:0/cuda:0", 2024-12-18T00:16:37.1845232Z "rank:1/cuda:1", 2024-12-18T00:16:37.1845509Z "rank:2/cuda:2", 2024-12-18T00:16:37.1845799Z "rank:3/cuda:3", 2024-12-18T00:16:37.1846081Z ], 2024-12-18T00:16:37.1846310Z ) 2024-12-18T00:16:37.1846542Z >>> current_offsets = [0] * 2 2024-12-18T00:16:37.1846842Z >>> current_offsets[0] = rank * 2 2024-12-18T00:16:37.1847175Z >>> shard_metadata = ShardMetadata( 2024-12-18T00:16:37.1847552Z shard_offsets=copy.deepcopy(current_offsets), 2024-12-18T00:16:37.1847933Z shard_sizes=tensor.size(), 2024-12-18T00:16:37.1848279Z placement=spec.placements[rank], 2024-12-18T00:16:37.1848592Z ) 2024-12-18T00:16:37.1848817Z >>> local_shards = [ 2024-12-18T00:16:37.1849081Z Shard( 2024-12-18T00:16:37.1849320Z tensor=tensor, 2024-12-18T00:16:37.1849616Z metadata=shard_metadata, 2024-12-18T00:16:37.1849917Z ) 2024-12-18T00:16:37.1850143Z ] 2024-12-18T00:16:37.1850490Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-12-18T00:16:37.1850945Z >>> sharding_dim = 1 2024-12-18T00:16:37.1851242Z >>> resharding_spec = ChunkShardingSpec( 2024-12-18T00:16:37.1851569Z dim=sharding_dim, 2024-12-18T00:16:37.1851859Z placements=[ 2024-12-18T00:16:37.1852131Z "rank:0/cuda:0", 2024-12-18T00:16:37.1852421Z "rank:1/cuda:1", 2024-12-18T00:16:37.1852709Z "rank:2/cuda:2", 2024-12-18T00:16:37.1852981Z "rank:3/cuda:3", 2024-12-18T00:16:37.1853260Z ], 2024-12-18T00:16:37.1853486Z ) 2024-12-18T00:16:37.1853726Z >>> st.reshard(resharding_spec) 2024-12-18T00:16:37.1854042Z >>> tensor = st.local_shards()[0].tensor 2024-12-18T00:16:37.1854363Z >>> tensor 2024-12-18T00:16:37.1854649Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-12-18T00:16:37.1855153Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-12-18T00:16:37.1855591Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-12-18T00:16:37.1855994Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-12-18T00:16:37.1856248Z 2024-12-18T00:16:37.1856504Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.1856926Z 2024-12-18T00:16:37.1933195Z 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. 2024-12-18T00:16:37.1934208Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.1934604Z 2024-12-18T00:16:37.1934824Z Representation of a sharding plan, describes how to shard a module 2024-12-18T00:16:37.1935449Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-12-18T00:16:37.1936133Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-12-18T00:16:37.1936801Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-12-18T00:16:37.1937169Z 2024-12-18T00:16:37.1937270Z Args: 2024-12-18T00:16:37.1937649Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-12-18T00:16:37.1938222Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-12-18T00:16:37.1938783Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-12-18T00:16:37.1939432Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-12-18T00:16:37.1939942Z a parameter to a `ShardingSpec`. 2024-12-18T00:16:37.1940444Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-12-18T00:16:37.1940997Z to a `Sharder` object. 2024-12-18T00:16:37.1941548Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-12-18T00:16:37.1942277Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-12-18T00:16:37.1942897Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-12-18T00:16:37.1943361Z Default: `None` 2024-12-18T00:16:37.1943776Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-12-18T00:16:37.1944402Z a module's sharded output to be returned as a Tensor from its local shards to 2024-12-18T00:16:37.1945020Z ensure further processing in a data parallel fashion. ("" in list means the 2024-12-18T00:16:37.1945488Z root module). 2024-12-18T00:16:37.1945744Z Default: None 2024-12-18T00:16:37.1945995Z Example: 2024-12-18T00:16:37.1946395Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-12-18T00:16:37.1947090Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-12-18T00:16:37.1947511Z 2024-12-18T00:16:37.1947683Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-12-18T00:16:37.1948085Z >>> class MyModule(nn.Module): 2024-12-18T00:16:37.1948413Z >>> def __init__(self) -> None: 2024-12-18T00:16:37.1948720Z >>> super().__init__() 2024-12-18T00:16:37.1949028Z >>> self.fc1 = nn.Linear() 2024-12-18T00:16:37.1949345Z >>> self.gelu = nn.GELU() 2024-12-18T00:16:37.1949657Z >>> self.fc2 = nn.Linear() 2024-12-18T00:16:37.1949976Z >>> self.relu = nn.Linear() 2024-12-18T00:16:37.1950268Z >>> 2024-12-18T00:16:37.1950503Z >>> def forward(self, input): 2024-12-18T00:16:37.1950894Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-12-18T00:16:37.1951188Z 2024-12-18T00:16:37.1951206Z 2024-12-18T00:16:37.1951338Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-12-18T00:16:37.1951791Z >>> sharding_plan = ShardingPlan( 2024-12-18T00:16:37.1952148Z >>> plan={ 2024-12-18T00:16:37.1952386Z >>> "fc1.weight": spec1, 2024-12-18T00:16:37.1952699Z >>> "fc2.weight": spec2 2024-12-18T00:16:37.1952993Z >>> }, 2024-12-18T00:16:37.1953234Z >>> output_plan={ 2024-12-18T00:16:37.1953510Z >>> "fc2": output_spec 2024-12-18T00:16:37.1953828Z >>> }, 2024-12-18T00:16:37.1954111Z >>> return_local_tensor=["fc2"] 2024-12-18T00:16:37.1954420Z >>> ) 2024-12-18T00:16:37.1954540Z 2024-12-18T00:16:37.1954809Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.1955177Z 2024-12-18T00:16:37.2579097Z 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. 2024-12-18T00:16:37.2580233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2580625Z 2024-12-18T00:16:37.2580739Z Run post-localSGD algorithm. 2024-12-18T00:16:37.2580945Z 2024-12-18T00:16:37.2581181Z This DDP communication hook is used for running post-localSGD algorithm, 2024-12-18T00:16:37.2581698Z by combining with a model averaging component (e.g., 2024-12-18T00:16:37.2582303Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-12-18T00:16:37.2582872Z that runs after the optimizer step. 2024-12-18T00:16:37.2583085Z 2024-12-18T00:16:37.2583185Z Args: 2024-12-18T00:16:37.2583522Z state (PostLocalSGDState): State information to run post-localSGD. 2024-12-18T00:16:37.2584144Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-12-18T00:16:37.2584967Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T00:16:37.2585762Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T00:16:37.2586295Z only exactly one tensor is stored in this bucket. 2024-12-18T00:16:37.2586557Z 2024-12-18T00:16:37.2586662Z Returns: 2024-12-18T00:16:37.2587024Z Future handler of the communication, which updates the gradients in place. 2024-12-18T00:16:37.2587407Z 2024-12-18T00:16:37.2587524Z Example:: 2024-12-18T00:16:37.2587755Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.2588190Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-12-18T00:16:37.2588685Z start_localSGD_iter=10) 2024-12-18T00:16:37.2589111Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:37.2589743Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-12-18T00:16:37.2590547Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-12-18T00:16:37.2591010Z 2024-12-18T00:16:37.2591265Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2591645Z 2024-12-18T00:16:37.2624518Z 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=343. 2024-12-18T00:16:37.2625630Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2626020Z 2024-12-18T00:16:37.2626135Z Implement PowerSGD algorithm. 2024-12-18T00:16:37.2626341Z 2024-12-18T00:16:37.2626569Z This DDP communication hook implements PowerSGD gradient compression 2024-12-18T00:16:37.2627172Z algorithm described in the `paper `_. 2024-12-18T00:16:37.2627774Z Once gradient tensors are aggregated across all workers, this hook applies 2024-12-18T00:16:37.2628244Z compression as follows: 2024-12-18T00:16:37.2628410Z 2024-12-18T00:16:37.2628851Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-12-18T00:16:37.2629603Z 2024-12-18T00:16:37.2630029Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-12-18T00:16:37.2630580Z 2024-12-18T00:16:37.2631036Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-12-18T00:16:37.2631574Z 2024-12-18T00:16:37.2631726Z 2. Handles uncompressed tensors: 2024-12-18T00:16:37.2631942Z 2024-12-18T00:16:37.2632454Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-12-18T00:16:37.2633094Z 2024-12-18T00:16:37.2633434Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-12-18T00:16:37.2633889Z 2024-12-18T00:16:37.2634135Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-12-18T00:16:37.2634483Z 2024-12-18T00:16:37.2634737Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-12-18T00:16:37.2635426Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-12-18T00:16:37.2635846Z 2024-12-18T00:16:37.2635992Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-12-18T00:16:37.2636259Z 2024-12-18T00:16:37.2636370Z 3.3. Allreduces Ps as a batch; 2024-12-18T00:16:37.2636582Z 2024-12-18T00:16:37.2636697Z 3.4. Orthogonalizes each P in Ps; 2024-12-18T00:16:37.2636925Z 2024-12-18T00:16:37.2637120Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-12-18T00:16:37.2637426Z 2024-12-18T00:16:37.2637549Z 3.6. Allreduces Qs as a batch; 2024-12-18T00:16:37.2637747Z 2024-12-18T00:16:37.2638054Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-12-18T00:16:37.2638533Z 2024-12-18T00:16:37.2638954Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-12-18T00:16:37.2639755Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-12-18T00:16:37.2640669Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-12-18T00:16:37.2641236Z 2024-12-18T00:16:37.2641327Z Args: 2024-12-18T00:16:37.2641886Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-12-18T00:16:37.2642799Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-12-18T00:16:37.2643394Z and ``min_compression_rate``. 2024-12-18T00:16:37.2644015Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T00:16:37.2644803Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T00:16:37.2645330Z only exactly one tensor is stored in this bucket. 2024-12-18T00:16:37.2645601Z 2024-12-18T00:16:37.2645690Z Returns: 2024-12-18T00:16:37.2646060Z Future handler of the communication, which updates the gradients in place. 2024-12-18T00:16:37.2646418Z 2024-12-18T00:16:37.2646529Z Example:: 2024-12-18T00:16:37.2646749Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.2647197Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-12-18T00:16:37.2647761Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-12-18T00:16:37.2648213Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-12-18T00:16:37.2648483Z 2024-12-18T00:16:37.2648750Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2649116Z 2024-12-18T00:16:37.2661242Z 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=37. 2024-12-18T00:16:37.2662475Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2662872Z 2024-12-18T00:16:37.2663076Z Averages parameters periodically after the warm-up stage. 2024-12-18T00:16:37.2663373Z 2024-12-18T00:16:37.2663733Z This can be used for running `post-local SGD `_, 2024-12-18T00:16:37.2664304Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-12-18T00:16:37.2664851Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-12-18T00:16:37.2665216Z 2024-12-18T00:16:37.2665301Z Args: 2024-12-18T00:16:37.2665590Z period (int): The number of steps per model averaging. 2024-12-18T00:16:37.2666148Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-12-18T00:16:37.2666662Z Otherwise, only DDP needs to be used. 2024-12-18T00:16:37.2667126Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-12-18T00:16:37.2667577Z model averaging is skipped. 2024-12-18T00:16:37.2668005Z process_group: The process group to be used for all-reduce. 2024-12-18T00:16:37.2668455Z If ``None``, the default process group, which 2024-12-18T00:16:37.2668900Z is created by :func:`torch.distributed.init_process_group`, 2024-12-18T00:16:37.2669339Z will be used. (default: ``None``) 2024-12-18T00:16:37.2669581Z 2024-12-18T00:16:37.2669676Z Example:: 2024-12-18T00:16:37.2669810Z 2024-12-18T00:16:37.2669935Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.2670269Z >>> import torch 2024-12-18T00:16:37.2670542Z >>> import torch.distributed as dist 2024-12-18T00:16:37.2671091Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-12-18T00:16:37.2671801Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T00:16:37.2672304Z >>> import torch.nn as nn 2024-12-18T00:16:37.2672572Z >>> 2024-12-18T00:16:37.2672881Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T00:16:37.2673293Z >>> torch.cuda.set_device(rank) 2024-12-18T00:16:37.2673833Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-12-18T00:16:37.2674326Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T00:16:37.2674726Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T00:16:37.2675082Z >>> ) 2024-12-18T00:16:37.2675365Z >>> # Register a post-localSGD communication hook. 2024-12-18T00:16:37.2676041Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T00:16:37.2677025Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:37.2677528Z >>> 2024-12-18T00:16:37.2677912Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T00:16:37.2678527Z >>> # After 100 steps, run model averaging every 4 steps. 2024-12-18T00:16:37.2679123Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T00:16:37.2679815Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T00:16:37.2680295Z >>> for step in range(0, 200): 2024-12-18T00:16:37.2680596Z >>> optimizer.zero_grad() 2024-12-18T00:16:37.2680912Z >>> loss = loss_fn(output, labels) 2024-12-18T00:16:37.2681237Z >>> loss.backward() 2024-12-18T00:16:37.2681514Z >>> optimizer.step() 2024-12-18T00:16:37.2681889Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-12-18T00:16:37.2682395Z >>> # inter-node communication only occurs every 4 iterations after 2024-12-18T00:16:37.2682847Z >>> # the initial ``warmup_steps`` period. 2024-12-18T00:16:37.2683247Z >>> averager.average_parameters(model.parameters()) 2024-12-18T00:16:37.2683679Z 2024-12-18T00:16:37.2683928Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2684294Z 2024-12-18T00:16:37.2685225Z 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=18. 2024-12-18T00:16:37.2686490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2686879Z 2024-12-18T00:16:37.2687220Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-12-18T00:16:37.2687671Z 2024-12-18T00:16:37.2687991Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-12-18T00:16:37.2688631Z by using different periods concurrently after the warm-up stage. 2024-12-18T00:16:37.2689346Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-12-18T00:16:37.2690215Z that supports `post-local SGD `_, which essentially only supports 2024-12-18T00:16:37.2690980Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-12-18T00:16:37.2691767Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-12-18T00:16:37.2692546Z Similarly, the process groups within this class do not have such an intra-machine process 2024-12-18T00:16:37.2693239Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-12-18T00:16:37.2693632Z 2024-12-18T00:16:37.2693730Z Args: 2024-12-18T00:16:37.2694103Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-12-18T00:16:37.2694692Z process group size, used for initializing process groups of 2024-12-18T00:16:37.2695252Z different sizes in a hierarchy to average parameters concurrently. 2024-12-18T00:16:37.2695824Z Particularly, at each iteration, there will be at most a single 2024-12-18T00:16:37.2696405Z process group that runs averaging -- the period of such group should 2024-12-18T00:16:37.2696984Z have the largest period which the current step can be divided by. 2024-12-18T00:16:37.2697487Z For example, if the dict has three keys: 2, 4, and 8, 2024-12-18T00:16:37.2697990Z then this means totally three process groups will be created to 2024-12-18T00:16:37.2698537Z average parameters every 2, 4, and 8 iterations, respectively. 2024-12-18T00:16:37.2699061Z At the 4th iteration, only the second process group will run 2024-12-18T00:16:37.2699561Z averaging, because the first process group should be a 2024-12-18T00:16:37.2700091Z subset of the second process group, and no need to execute the first 2024-12-18T00:16:37.2700555Z process group redundantly. 2024-12-18T00:16:37.2700996Z On the other hand, the third process group can only be triggered 2024-12-18T00:16:37.2701553Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-12-18T00:16:37.2702206Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-12-18T00:16:37.2703074Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-12-18T00:16:37.2703817Z If ``None``, the default process group, which is created 2024-12-18T00:16:37.2704317Z by :func:`torch.distributed.init_process_group`, will be used. 2024-12-18T00:16:37.2704813Z (default: ``None``) 2024-12-18T00:16:37.2705091Z 2024-12-18T00:16:37.2705188Z Example:: 2024-12-18T00:16:37.2705437Z >>> # xdoctest: +SKIP('undefined rank') 2024-12-18T00:16:37.2705793Z >>> from collections import OrderedDict 2024-12-18T00:16:37.2706120Z >>> import torch 2024-12-18T00:16:37.2706412Z >>> import torch.distributed as dist 2024-12-18T00:16:37.2706941Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T00:16:37.2707442Z >>> PostLocalSGDState, 2024-12-18T00:16:37.2707738Z >>> post_localSGD_hook, 2024-12-18T00:16:37.2708019Z >>> ) 2024-12-18T00:16:37.2708508Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-12-18T00:16:37.2709110Z >>> import torch.nn as nn 2024-12-18T00:16:37.2709390Z >>> 2024-12-18T00:16:37.2709691Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T00:16:37.2710100Z >>> torch.cuda.set_device(rank) 2024-12-18T00:16:37.2710441Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T00:16:37.2710848Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T00:16:37.2711259Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T00:16:37.2711603Z >>> ) 2024-12-18T00:16:37.2711879Z >>> # Register a post-localSGD communication hook. 2024-12-18T00:16:37.2712408Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-12-18T00:16:37.2712926Z >>> subgroup, _ = dist.new_subgroups() 2024-12-18T00:16:37.2713475Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-12-18T00:16:37.2714077Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:37.2714448Z >>> 2024-12-18T00:16:37.2714840Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-12-18T00:16:37.2715374Z >>> # the 16 processes every 16 iterations. 2024-12-18T00:16:37.2715795Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-12-18T00:16:37.2716329Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-12-18T00:16:37.2717011Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T00:16:37.2717723Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T00:16:37.2718264Z >>> # After 100 steps, run model averaging at two levels. 2024-12-18T00:16:37.2718741Z >>> for step in range(0, 200): 2024-12-18T00:16:37.2719062Z >>> optimizer.zero_grad() 2024-12-18T00:16:37.2719380Z >>> loss = loss_fn(output, labels) 2024-12-18T00:16:37.2719710Z >>> loss.backward() 2024-12-18T00:16:37.2719983Z >>> optimizer.step() 2024-12-18T00:16:37.2720326Z >>> # Average parameters after ``optimizer.step()``. 2024-12-18T00:16:37.2720895Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-12-18T00:16:37.2721473Z >>> averager.average_parameters(model.parameters()) 2024-12-18T00:16:37.2721749Z 2024-12-18T00:16:37.2721865Z .. warning :: 2024-12-18T00:16:37.2722260Z The last group size in the dict must be the size of the provided ``process_group``, 2024-12-18T00:16:37.2722867Z which indicates model averaging at the highest level of the hierarchy. 2024-12-18T00:16:37.2723532Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-12-18T00:16:37.2723971Z 2024-12-18T00:16:37.2724064Z .. warning :: 2024-12-18T00:16:37.2724439Z `HierarchicalModelAverager` is experimental and subject to change. 2024-12-18T00:16:37.2724787Z 2024-12-18T00:16:37.2725053Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2725420Z 2024-12-18T00:16:37.2871828Z 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. 2024-12-18T00:16:37.2873116Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2873494Z 2024-12-18T00:16:37.2874070Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-12-18T00:16:37.2874762Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-12-18T00:16:37.2875138Z 2024-12-18T00:16:37.2875318Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-12-18T00:16:37.2875672Z 2024-12-18T00:16:37.2875770Z .. warning:: 2024-12-18T00:16:37.2876077Z Current implementation only supports loading Tensors. 2024-12-18T00:16:37.2876358Z 2024-12-18T00:16:37.2876488Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2876808Z >>> sd = {"mode": model} 2024-12-18T00:16:37.2877060Z >>> dcp.load( 2024-12-18T00:16:37.2877291Z >>> sd, 2024-12-18T00:16:37.2877572Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T00:16:37.2877960Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T00:16:37.2878300Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T00:16:37.2878666Z >>> ) 2024-12-18T00:16:37.2878792Z 2024-12-18T00:16:37.2879058Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2879441Z 2024-12-18T00:16:37.2880123Z 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. 2024-12-18T00:16:37.2881179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2881554Z 2024-12-18T00:16:37.2881930Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-12-18T00:16:37.2882737Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-12-18T00:16:37.2883311Z metadata file, like Torch Save files. 2024-12-18T00:16:37.2883528Z 2024-12-18T00:16:37.2883711Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-12-18T00:16:37.2884010Z 2024-12-18T00:16:37.2884103Z .. warning:: 2024-12-18T00:16:37.2884417Z Current implementation only supports loading Tensors. 2024-12-18T00:16:37.2884697Z 2024-12-18T00:16:37.2884826Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2885151Z >>> sd = {"mode": model} 2024-12-18T00:16:37.2885399Z >>> dcp.load( 2024-12-18T00:16:37.2885623Z >>> sd, 2024-12-18T00:16:37.2885900Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T00:16:37.2886298Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T00:16:37.2886647Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T00:16:37.2886945Z >>> ) 2024-12-18T00:16:37.2887071Z 2024-12-18T00:16:37.2887322Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2887703Z 2024-12-18T00:16:37.2925350Z 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=220. 2024-12-18T00:16:37.2926429Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2926821Z 2024-12-18T00:16:37.2927033Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-12-18T00:16:37.2927353Z 2024-12-18T00:16:37.2927530Z This is the current recommended way to checkpoint FSDP. 2024-12-18T00:16:37.2927906Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.2928210Z >>> import torch.distributed.checkpoint as dist_cp 2024-12-18T00:16:37.2928564Z >>> # Save 2024-12-18T00:16:37.2928793Z >>> model: torch.nn.Model 2024-12-18T00:16:37.2929086Z >>> optim_params = model.parameters() 2024-12-18T00:16:37.2929447Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-12-18T00:16:37.2929779Z >>> # Save 2024-12-18T00:16:37.2930122Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-12-18T00:16:37.2930719Z >>> state_dict = { 2024-12-18T00:16:37.2931045Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-12-18T00:16:37.2931433Z >>> "model": model.state_dict() 2024-12-18T00:16:37.2931728Z >>> } 2024-12-18T00:16:37.2931965Z >>> dist_cp.save_state_dict( 2024-12-18T00:16:37.2932266Z >>> state_dict=optim_state, 2024-12-18T00:16:37.2932707Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-12-18T00:16:37.2933151Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-12-18T00:16:37.2933486Z >>> ) 2024-12-18T00:16:37.2933682Z >>> 2024-12-18T00:16:37.2933892Z >>> # Load 2024-12-18T00:16:37.2934248Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-12-18T00:16:37.2934724Z >>> model_state_dict = model_tp.state_dict() 2024-12-18T00:16:37.2935064Z >>> checkpoint = { 2024-12-18T00:16:37.2935325Z >>> "model": model_state_dict 2024-12-18T00:16:37.2935630Z >>> } 2024-12-18T00:16:37.2935863Z >>> dist_cp.load_state_dict( 2024-12-18T00:16:37.2936169Z >>> state_dict=checkpoint, 2024-12-18T00:16:37.2936564Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-12-18T00:16:37.2936993Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-12-18T00:16:37.2937328Z >>> ) 2024-12-18T00:16:37.2937607Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-12-18T00:16:37.2937961Z >>> 2024-12-18T00:16:37.2938255Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-12-18T00:16:37.2938636Z >>> model_state_dict, 2024-12-18T00:16:37.2938932Z >>> optimizer_key="optimizer", 2024-12-18T00:16:37.2939332Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-12-18T00:16:37.2939714Z >>> ) 2024-12-18T00:16:37.2939943Z >>> 2024-12-18T00:16:37.2940202Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-12-18T00:16:37.2940659Z >>> model, optim, optim_state["optimizer"] 2024-12-18T00:16:37.2941006Z >>> ) 2024-12-18T00:16:37.2941222Z >>> 2024-12-18T00:16:37.2941450Z >>> optim.load_state_dict(flattened_osd) 2024-12-18T00:16:37.2941694Z 2024-12-18T00:16:37.2941949Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2942331Z 2024-12-18T00:16:37.2947988Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-12-18T00:16:37.2948988Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2949383Z 2024-12-18T00:16:37.2949668Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-12-18T00:16:37.2950087Z 2024-12-18T00:16:37.2950382Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-12-18T00:16:37.2950809Z 2024-12-18T00:16:37.2951087Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T00:16:37.2951612Z will be visible to the whole process. 2024-12-18T00:16:37.2951826Z 2024-12-18T00:16:37.2952116Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-12-18T00:16:37.2952510Z 2024-12-18T00:16:37.2952647Z 1) set_up_planner - called on all ranks. 2024-12-18T00:16:37.2952988Z Signals the start of a checkpoint save. 2024-12-18T00:16:37.2953238Z 2024-12-18T00:16:37.2953363Z 2) create_local_plan - called on all ranks. 2024-12-18T00:16:37.2953884Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-12-18T00:16:37.2954299Z 2024-12-18T00:16:37.2954478Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T00:16:37.2955124Z Takes the SavePlan from all ranks and make any global decision. 2024-12-18T00:16:37.2955515Z 2024-12-18T00:16:37.2955645Z 4) finish_plan - called on all ranks. 2024-12-18T00:16:37.2956071Z This gives each rank a chance to adjust to global planning decisions. 2024-12-18T00:16:37.2956523Z 2024-12-18T00:16:37.2956677Z 5) resolve_data - called multiple times on each rank 2024-12-18T00:16:37.2957147Z Lookups a value on the `state_dict` for the storage layer to write. 2024-12-18T00:16:37.2957478Z 2024-12-18T00:16:37.2957779Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-12-18T00:16:37.2958540Z most changes can be expressed by changes in a single method. 2024-12-18T00:16:37.2958838Z 2024-12-18T00:16:37.2958976Z There are 3 usual patterns of extension: 2024-12-18T00:16:37.2959201Z 2024-12-18T00:16:37.2959453Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-12-18T00:16:37.2960054Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-12-18T00:16:37.2960405Z 2024-12-18T00:16:37.2960518Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2960883Z >>> class RenamePlanner(DefaultSavePlanner): 2024-12-18T00:16:37.2961234Z >>> def set_up_planner( 2024-12-18T00:16:37.2961509Z >>> self, 2024-12-18T00:16:37.2961759Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T00:16:37.2962105Z >>> storage_meta: Optional[StorageMeta], 2024-12-18T00:16:37.2962449Z >>> is_coordinator: bool, 2024-12-18T00:16:37.2962743Z >>> ) -> None: 2024-12-18T00:16:37.2963005Z >>> # prefix all keys with `foo_`` 2024-12-18T00:16:37.2963513Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-12-18T00:16:37.2963926Z 2024-12-18T00:16:37.2964260Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-12-18T00:16:37.2964720Z 2024-12-18T00:16:37.2964834Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2965183Z >>> class FP16Planner(DefaultSavePlanner): 2024-12-18T00:16:37.2965533Z >>> def create_local_plan(self): 2024-12-18T00:16:37.2965866Z >>> plan = super().create_local_plan() 2024-12-18T00:16:37.2966192Z >>> for p in plan: 2024-12-18T00:16:37.2966494Z >>> if p.tensor_data is not None: 2024-12-18T00:16:37.2966887Z >>> p.tensor_data.properties.dtype = torch.float16 2024-12-18T00:16:37.2967259Z >>> return plan 2024-12-18T00:16:37.2967510Z >>> 2024-12-18T00:16:37.2967732Z >>> def resolve_data(self, write_item): 2024-12-18T00:16:37.2968088Z >>> item = super().resolve_data(write_item) 2024-12-18T00:16:37.2968607Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-12-18T00:16:37.2968994Z 2024-12-18T00:16:37.2969344Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-12-18T00:16:37.2969801Z 2024-12-18T00:16:37.2969926Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2970266Z >>> from itertools import zip_longest 2024-12-18T00:16:37.2970581Z >>> from dataclasses import replace 2024-12-18T00:16:37.2970959Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-12-18T00:16:37.2971523Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-12-18T00:16:37.2972054Z >>> # This sample doesn't handle ShardedTensors 2024-12-18T00:16:37.2972435Z >>> def create_global_plan(self, all_plans): 2024-12-18T00:16:37.2972817Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2024-12-18T00:16:37.2973194Z >>> items_per_rank = [ 2024-12-18T00:16:37.2973520Z >>> [item for item in items if item is not None] 2024-12-18T00:16:37.2974216Z >>> for items in zip(*zip_longest(*iters), strict=True) 2024-12-18T00:16:37.2974586Z >>> ] 2024-12-18T00:16:37.2974804Z >>> all_plans = [ 2024-12-18T00:16:37.2975092Z >>> replace(plan, items=items) 2024-12-18T00:16:37.2975508Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2024-12-18T00:16:37.2975908Z >>> ] 2024-12-18T00:16:37.2976182Z >>> return super().create_global_plan(all_plans) 2024-12-18T00:16:37.2976563Z 2024-12-18T00:16:37.2976826Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-12-18T00:16:37.2977494Z accomplished by having each rank contribute their data items in the local plan and 2024-12-18T00:16:37.2978002Z the global planner aggregate them: 2024-12-18T00:16:37.2978222Z 2024-12-18T00:16:37.2978373Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2978791Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-12-18T00:16:37.2979194Z >>> def create_local_plan(self) -> SavePlan: 2024-12-18T00:16:37.2979547Z >>> plan = super().create_local_plan() 2024-12-18T00:16:37.2979944Z >>> return replace(plan, planner_data="per-rank-data") 2024-12-18T00:16:37.2980311Z >>> 2024-12-18T00:16:37.2980737Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-12-18T00:16:37.2981360Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-12-18T00:16:37.2981832Z >>> merged_data = [p.planner_data for p in global_plan] 2024-12-18T00:16:37.2982281Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-12-18T00:16:37.2982680Z >>> return global_plan, metadata 2024-12-18T00:16:37.2982907Z 2024-12-18T00:16:37.2983160Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.2983526Z 2024-12-18T00:16:37.2984163Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=272. 2024-12-18T00:16:37.2985127Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.2985503Z 2024-12-18T00:16:37.2985802Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-12-18T00:16:37.2986207Z 2024-12-18T00:16:37.2986509Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-12-18T00:16:37.2986922Z 2024-12-18T00:16:37.2987214Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T00:16:37.2987719Z will be visible to the whole process. 2024-12-18T00:16:37.2987947Z 2024-12-18T00:16:37.2988221Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-12-18T00:16:37.2988629Z 2024-12-18T00:16:37.2988754Z 1) set_up_planner - called on all ranks. 2024-12-18T00:16:37.2989119Z Signals the start of loading a checkpoint. 2024-12-18T00:16:37.2989357Z 2024-12-18T00:16:37.2989497Z 2) create_local_plan - called on all ranks. 2024-12-18T00:16:37.2990021Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-12-18T00:16:37.2990422Z 2024-12-18T00:16:37.2990617Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T00:16:37.2991101Z Takes the LoadPlan from all ranks and make any global decision. 2024-12-18T00:16:37.2991429Z 2024-12-18T00:16:37.2991573Z 4) load_bytes - called multiple times on each rank 2024-12-18T00:16:37.2992004Z This is called once per non-tensor value in state_dict. 2024-12-18T00:16:37.2992280Z 2024-12-18T00:16:37.2992516Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-12-18T00:16:37.2993048Z They are called in pair for each Tensor value in state_dict. 2024-12-18T00:16:37.2993344Z 2024-12-18T00:16:37.2993665Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-12-18T00:16:37.2994262Z most changes can be expressed by changes in a single method. 2024-12-18T00:16:37.2994567Z 2024-12-18T00:16:37.2994697Z There are two usual patterns of extension: 2024-12-18T00:16:37.2994940Z 2024-12-18T00:16:37.2995194Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-12-18T00:16:37.2995825Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-12-18T00:16:37.2996430Z to keep a reference to the original state_dict as load happens in place so 2024-12-18T00:16:37.2996964Z we need to be able to perform it in place 2024-12-18T00:16:37.2997191Z 2024-12-18T00:16:37.2997304Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.2997659Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-12-18T00:16:37.2998005Z >>> def set_up_planner( 2024-12-18T00:16:37.2998277Z >>> self, 2024-12-18T00:16:37.2998669Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T00:16:37.2998980Z >>> metadata: Metadata, 2024-12-18T00:16:37.2999275Z >>> is_coordinator: bool, 2024-12-18T00:16:37.2999569Z >>> ) -> None: 2024-12-18T00:16:37.2999842Z >>> self.original_state_dict = state_dict 2024-12-18T00:16:37.3000255Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-12-18T00:16:37.3000627Z >>> 2024-12-18T00:16:37.3000861Z >>> if self.flatten_sharded_tensors: 2024-12-18T00:16:37.3001247Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-12-18T00:16:37.3001612Z >>> 2024-12-18T00:16:37.3001839Z >>> if self.flatten_state_dict: 2024-12-18T00:16:37.3002232Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-12-18T00:16:37.3002630Z >>> 2024-12-18T00:16:37.3002861Z >>> self.state_dict = state_dict 2024-12-18T00:16:37.3003189Z >>> self.metadata = metadata 2024-12-18T00:16:37.3003526Z >>> self.is_coordinator = is_coordinator 2024-12-18T00:16:37.3003842Z >>> 2024-12-18T00:16:37.3004083Z >>> def load_bytes(self, read_item, value): 2024-12-18T00:16:37.3004428Z >>> # Remove the "foo_" prefix 2024-12-18T00:16:37.3004966Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-12-18T00:16:37.3005401Z 2024-12-18T00:16:37.3005405Z 2024-12-18T00:16:37.3005673Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-12-18T00:16:37.3006046Z 2024-12-18T00:16:37.3006172Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.3006543Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-12-18T00:16:37.3006935Z >>> def resolve_tensor(self, read_item): 2024-12-18T00:16:37.3007304Z >>> tensor = super().resolve_tensor(read_item) 2024-12-18T00:16:37.3007705Z >>> return torch.empty_like(tensor, device="cpu") 2024-12-18T00:16:37.3008054Z >>> 2024-12-18T00:16:37.3008293Z >>> def commit_tensor(self, read_item, tensor): 2024-12-18T00:16:37.3008698Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-12-18T00:16:37.3008977Z 2024-12-18T00:16:37.3009227Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.3009604Z 2024-12-18T00:16:37.3130628Z 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=61. 2024-12-18T00:16:37.3131642Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.3132037Z 2024-12-18T00:16:37.3132182Z Load a distributed ``state_dict`` in SPMD style. 2024-12-18T00:16:37.3132430Z 2024-12-18T00:16:37.3132624Z Each rank will try to read the least amount of data necessary 2024-12-18T00:16:37.3133165Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-12-18T00:16:37.3133775Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-12-18T00:16:37.3134136Z 2024-12-18T00:16:37.3134397Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T00:16:37.3135035Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-12-18T00:16:37.3135583Z ``load_state_dict`` once the deserialization is complete. 2024-12-18T00:16:37.3136121Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2024-12-18T00:16:37.3136655Z it in the ``state_dict`` with the deserialized object. 2024-12-18T00:16:37.3136913Z 2024-12-18T00:16:37.3137205Z .. warning:: 2024-12-18T00:16:37.3137544Z All tensors in ``state_dict`` must be allocated on their 2024-12-18T00:16:37.3137995Z destination device *prior to* calling this function. 2024-12-18T00:16:37.3138282Z 2024-12-18T00:16:37.3138508Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-12-18T00:16:37.3138954Z on state_dict. 2024-12-18T00:16:37.3139097Z 2024-12-18T00:16:37.3139239Z .. warning:: 2024-12-18T00:16:37.3139607Z Users must call `load_state_dict` on the root module to ensure load 2024-12-18T00:16:37.3140122Z pos-processing and non-tensor data properly propagates. 2024-12-18T00:16:37.3140425Z 2024-12-18T00:16:37.3140542Z .. note: 2024-12-18T00:16:37.3140931Z If no process group is initialized, this function will assume the intent 2024-12-18T00:16:37.3141503Z is to load a checkpoint into the local process. This can be useful in the 2024-12-18T00:16:37.3142098Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-12-18T00:16:37.3142562Z or ShardedTensor) 2024-12-18T00:16:37.3142732Z 2024-12-18T00:16:37.3142820Z .. note: 2024-12-18T00:16:37.3143086Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T00:16:37.3143333Z 2024-12-18T00:16:37.3143433Z Args: 2024-12-18T00:16:37.3143708Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T00:16:37.3144113Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T00:16:37.3144585Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T00:16:37.3145125Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T00:16:37.3145618Z It can also be a key if the storage is a key-value store. 2024-12-18T00:16:37.3146004Z (Default: ``None``) 2024-12-18T00:16:37.3146320Z storage_reader (Optional[StorageReader]): 2024-12-18T00:16:37.3146757Z Instance of StorageWriter used to perform reads. If this is not 2024-12-18T00:16:37.3147289Z specified, DCP will automatically infer the reader based on the 2024-12-18T00:16:37.3147835Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T00:16:37.3148270Z be raised. (Default: ``None``) 2024-12-18T00:16:37.3148604Z planner (Optional[LoadPlanner]): 2024-12-18T00:16:37.3149014Z Instance of LoadPlanner. If this is not specificed, the default 2024-12-18T00:16:37.3149474Z planner will be used. (Default: ``None``) 2024-12-18T00:16:37.3149850Z process_group (Optional[ProcessGroup]): 2024-12-18T00:16:37.3150275Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T00:16:37.3150679Z (Default: ``None``) 2024-12-18T00:16:37.3150853Z 2024-12-18T00:16:37.3150956Z Returns: 2024-12-18T00:16:37.3151162Z None. 2024-12-18T00:16:37.3151299Z 2024-12-18T00:16:37.3151388Z Examples 2024-12-18T00:16:37.3151622Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.3151905Z >>> my_model = MyModule() 2024-12-18T00:16:37.3152218Z >>> optimizer = Adagrad(my_model.parameters()) 2024-12-18T00:16:37.3152600Z >>> model_state_dict = my_model.state_dict() 2024-12-18T00:16:37.3153246Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-12-18T00:16:37.3153836Z 2024-12-18T00:16:37.3154023Z >>> torch.distributed.checkpoint.load_state_dict( 2024-12-18T00:16:37.3154515Z >>> state_dict=model_state_dict, 2024-12-18T00:16:37.3154864Z >>> storage_reader=fs_storage_reader, 2024-12-18T00:16:37.3155175Z >>> ) 2024-12-18T00:16:37.3155307Z 2024-12-18T00:16:37.3155504Z >>> # module.load_state_dict() function might have customized steps 2024-12-18T00:16:37.3155954Z >>> # to flush the state_dict, must call it to 2024-12-18T00:16:37.3156302Z >>> # ensure correct behavior. 2024-12-18T00:16:37.3156634Z >>> my_model.load_state_dict(model_state_dict) 2024-12-18T00:16:37.3156873Z 2024-12-18T00:16:37.3156967Z .. note:: 2024-12-18T00:16:37.3157303Z load_state_dict uses collectives to coordinate reads across ranks. 2024-12-18T00:16:37.3157965Z For NCCL-based process groups, internal tensor representations of 2024-12-18T00:16:37.3158642Z objects must be moved to the GPU device before communication takes place. 2024-12-18T00:16:37.3159217Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T00:16:37.3159911Z and it is the user's responsibility to ensure that this is set so that each 2024-12-18T00:16:37.3160535Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T00:16:37.3160844Z 2024-12-18T00:16:37.3161093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.3161471Z 2024-12-18T00:16:37.3162105Z 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=67. 2024-12-18T00:16:37.3163074Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.3163465Z 2024-12-18T00:16:37.3163587Z Save a distributed model in SPMD style. 2024-12-18T00:16:37.3163823Z 2024-12-18T00:16:37.3164012Z This function is different from ``torch.save()`` as it handles 2024-12-18T00:16:37.3164578Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-12-18T00:16:37.3164949Z 2024-12-18T00:16:37.3165221Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T00:16:37.3165752Z save will call ``state_dict`` before serialization. 2024-12-18T00:16:37.3166009Z 2024-12-18T00:16:37.3166104Z .. warning:: 2024-12-18T00:16:37.3166475Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-12-18T00:16:37.3166934Z for saved state_dicts. 2024-12-18T00:16:37.3167108Z 2024-12-18T00:16:37.3167210Z .. warning:: 2024-12-18T00:16:37.3167553Z If using the `process_group` argument, make sure that only its ranks 2024-12-18T00:16:37.3168075Z call `save_state_dict` and that all data in state_dict belong to it. 2024-12-18T00:16:37.3168414Z 2024-12-18T00:16:37.3168503Z .. note:: 2024-12-18T00:16:37.3168892Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-12-18T00:16:37.3169527Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-12-18T00:16:37.3170022Z group needs to be passed in. 2024-12-18T00:16:37.3170221Z 2024-12-18T00:16:37.3170326Z .. note:: 2024-12-18T00:16:37.3170715Z If no process group is available, this function assumes the intention is to save the 2024-12-18T00:16:37.3171217Z state_dict in the local process. 2024-12-18T00:16:37.3171441Z 2024-12-18T00:16:37.3171528Z .. note: 2024-12-18T00:16:37.3171789Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T00:16:37.3172033Z 2024-12-18T00:16:37.3172037Z 2024-12-18T00:16:37.3172134Z Args: 2024-12-18T00:16:37.3172405Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T00:16:37.3172804Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T00:16:37.3173279Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T00:16:37.3174059Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T00:16:37.3174559Z It can also be a key if the storage is a key-value store. 2024-12-18T00:16:37.3174948Z (Default: ``None``) 2024-12-18T00:16:37.3175247Z storage_writer (Optional[StorageWriter]): 2024-12-18T00:16:37.3175703Z Instance of StorageWriter used to perform writes. If this is not 2024-12-18T00:16:37.3176237Z specified, DCP will automatically infer the writer based on the 2024-12-18T00:16:37.3176761Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T00:16:37.3177194Z be raised. (Default: ``None``) 2024-12-18T00:16:37.3177524Z planner (Optional[SavePlanner]): 2024-12-18T00:16:37.3177930Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T00:16:37.3178471Z planner will be used. (Default: ``None``) 2024-12-18T00:16:37.3178881Z process_group (Optional[ProcessGroup]): 2024-12-18T00:16:37.3179301Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T00:16:37.3179701Z (Default: ``None``) 2024-12-18T00:16:37.3179873Z 2024-12-18T00:16:37.3179959Z Returns: 2024-12-18T00:16:37.3180282Z Metadata: Metadata object for the saved checkpoint. 2024-12-18T00:16:37.3180565Z 2024-12-18T00:16:37.3180689Z Example: 2024-12-18T00:16:37.3180918Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.3181196Z >>> my_model = MyModule() 2024-12-18T00:16:37.3181377Z 2024-12-18T00:16:37.3181489Z >>> state_dict = {"model": my_model} 2024-12-18T00:16:37.3181717Z 2024-12-18T00:16:37.3182017Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T00:16:37.3182576Z >>> torch.distributed.checkpoint.save( 2024-12-18T00:16:37.3182929Z >>> state_dict=state_dict, 2024-12-18T00:16:37.3183252Z >>> storage_writer=fs_storage_writer, 2024-12-18T00:16:37.3183573Z >>> ) 2024-12-18T00:16:37.3183691Z 2024-12-18T00:16:37.3183783Z .. note:: 2024-12-18T00:16:37.3184121Z save_state_dict uses collectives to coordinate writes across ranks. 2024-12-18T00:16:37.3184672Z For NCCL-based process groups, internal tensor representations of 2024-12-18T00:16:37.3185238Z objects must be moved to the GPU device before communication takes place. 2024-12-18T00:16:37.3185814Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T00:16:37.3186352Z and it is the user's responsibility to ensure that this is set so that 2024-12-18T00:16:37.3186886Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T00:16:37.3187209Z 2024-12-18T00:16:37.3187460Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.3187841Z 2024-12-18T00:16:37.3188472Z 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=170. 2024-12-18T00:16:37.3189481Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.3190134Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-12-18T00:16:37.3190818Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-12-18T00:16:37.3191226Z 2024-12-18T00:16:37.3191323Z .. warning:: 2024-12-18T00:16:37.3191637Z This feature is experimental and subject to change. 2024-12-18T00:16:37.3191925Z 2024-12-18T00:16:37.3192014Z Args: 2024-12-18T00:16:37.3192304Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T00:16:37.3192736Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T00:16:37.3193201Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T00:16:37.3193742Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T00:16:37.3194246Z It can also be a key if the storage is a key-value store. 2024-12-18T00:16:37.3194639Z (Default: ``None``) 2024-12-18T00:16:37.3194963Z storage_writer (Optional[StorageWriter]): 2024-12-18T00:16:37.3195430Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-12-18T00:16:37.3195995Z this is not specified, DCP will automatically infer the writer based on the 2024-12-18T00:16:37.3196570Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T00:16:37.3197009Z be raised. (Default: ``None``) 2024-12-18T00:16:37.3197351Z planner (Optional[SavePlanner]): 2024-12-18T00:16:37.3197786Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T00:16:37.3198230Z planner will be used. (Default: ``None``) 2024-12-18T00:16:37.3198689Z process_group (Optional[ProcessGroup]): 2024-12-18T00:16:37.3199204Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T00:16:37.3199611Z (Default: ``None``) 2024-12-18T00:16:37.3199791Z 2024-12-18T00:16:37.3199892Z Returns: 2024-12-18T00:16:37.3200243Z Future: A future holding the resultant Metadata object from `save`. 2024-12-18T00:16:37.3200578Z 2024-12-18T00:16:37.3200695Z Example: 2024-12-18T00:16:37.3200979Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.3201271Z >>> my_model = MyModule() 2024-12-18T00:16:37.3201463Z 2024-12-18T00:16:37.3201587Z >>> state_dict = {"model": my_model} 2024-12-18T00:16:37.3201810Z 2024-12-18T00:16:37.3202127Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T00:16:37.3202765Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-12-18T00:16:37.3203194Z >>> state_dict=state_dict, 2024-12-18T00:16:37.3203530Z >>> storage_writer=fs_storage_writer, 2024-12-18T00:16:37.3203861Z >>> ) 2024-12-18T00:16:37.3204084Z >>> 2024-12-18T00:16:37.3204310Z >>> # ... do some work ... 2024-12-18T00:16:37.3204584Z >>> 2024-12-18T00:16:37.3204826Z >>> checkpoint_future.result() 2024-12-18T00:16:37.3205050Z 2024-12-18T00:16:37.3205135Z 2024-12-18T00:16:37.3205504Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.3205874Z 2024-12-18T00:16:37.3249147Z 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=91. 2024-12-18T00:16:37.3250232Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.3250611Z 2024-12-18T00:16:37.3250828Z Initialize rendezvous event object and record its operations. 2024-12-18T00:16:37.3251143Z 2024-12-18T00:16:37.3251230Z Args: 2024-12-18T00:16:37.3251497Z run_id (str): The run id of the rendezvous. 2024-12-18T00:16:37.3251896Z message (str): The message describing the event. 2024-12-18T00:16:37.3252414Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-12-18T00:16:37.3252980Z name (str): Event name. (E.g. Current action being performed). 2024-12-18T00:16:37.3253391Z hostname (str): Hostname of the node. 2024-12-18T00:16:37.3253769Z pid (Optional[int]): The process id of the node. 2024-12-18T00:16:37.3254271Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-12-18T00:16:37.3254908Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-12-18T00:16:37.3255459Z rank (Optional[int]): The rank of the node, if known. 2024-12-18T00:16:37.3255821Z Returns: 2024-12-18T00:16:37.3256022Z None 2024-12-18T00:16:37.3256235Z Example: 2024-12-18T00:16:37.3256487Z >>> # See DynamicRendezvousHandler class 2024-12-18T00:16:37.3256822Z >>> def _record( 2024-12-18T00:16:37.3257057Z ... self, 2024-12-18T00:16:37.3257299Z ... message: str, 2024-12-18T00:16:37.3257611Z ... node_state: NodeState = NodeState.RUNNING, 2024-12-18T00:16:37.3257977Z ... rank: Optional[int] = None, 2024-12-18T00:16:37.3258288Z ... ) -> None: 2024-12-18T00:16:37.3258546Z ... construct_and_record_rdzv_event( 2024-12-18T00:16:37.3258944Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-12-18T00:16:37.3259348Z ... run_id=self._settings.run_id, 2024-12-18T00:16:37.3259677Z ... message=message, 2024-12-18T00:16:37.3259973Z ... node_state=node_state, 2024-12-18T00:16:37.3260303Z ... hostname=self._this_node.addr, 2024-12-18T00:16:37.3260635Z ... pid=self._this_node.pid, 2024-12-18T00:16:37.3260977Z ... local_id=self._this_node.local_id, 2024-12-18T00:16:37.3261309Z ... rank=rank, 2024-12-18T00:16:37.3261567Z ... ) 2024-12-18T00:16:37.3261773Z 2024-12-18T00:16:37.3262071Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.3262439Z 2024-12-18T00:16:37.4764142Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=113. 2024-12-18T00:16:37.4765282Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.4765734Z 2024-12-18T00:16:37.4765928Z This configures FSDP-native mixed precision training. 2024-12-18T00:16:37.4766207Z 2024-12-18T00:16:37.4766303Z Attributes: 2024-12-18T00:16:37.4766716Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-12-18T00:16:37.4767284Z parameters during forward and backward and thus the dtype for 2024-12-18T00:16:37.4767833Z forward and backward computation. Outside forward and backward, the 2024-12-18T00:16:37.4768378Z *sharded* parameters are kept in full precision (e.g. for the 2024-12-18T00:16:37.4768917Z optimizer step), and for model checkpointing, the parameters are 2024-12-18T00:16:37.4769409Z always saved in full precision. (Default: ``None``) 2024-12-18T00:16:37.4769892Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T00:16:37.4770453Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-12-18T00:16:37.4770981Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-12-18T00:16:37.4771490Z the ``param_dtype`` value, still running gradient reduction in low 2024-12-18T00:16:37.4772036Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-12-18T00:16:37.4772575Z to force gradient reduction to run in full precision. (Default: 2024-12-18T00:16:37.4772978Z ``None``) 2024-12-18T00:16:37.4773342Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T00:16:37.4774140Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-12-18T00:16:37.4774671Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-12-18T00:16:37.4775209Z dtype thereafter. For model checkpointing, the buffers are saved 2024-12-18T00:16:37.4775715Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-12-18T00:16:37.4776107Z ``None``) 2024-12-18T00:16:37.4776455Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-12-18T00:16:37.4776986Z gradients to full precision after the backward pass in preparation 2024-12-18T00:16:37.4777530Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-12-18T00:16:37.4778044Z in the dtype used for gradient reduction, which can save memory if 2024-12-18T00:16:37.4778577Z using a custom optimizer that supports running in low precision. 2024-12-18T00:16:37.4779001Z (Default: ``False``) 2024-12-18T00:16:37.4779397Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-12-18T00:16:37.4779932Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-12-18T00:16:37.4780470Z that parameter and input dtypes match for forward computation, as 2024-12-18T00:16:37.4781000Z required by many ops. This may need to be set to ``True`` when only 2024-12-18T00:16:37.4781551Z applying mixed precision to some but not all FSDP modules, in which 2024-12-18T00:16:37.4782106Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-12-18T00:16:37.4782536Z (Default: ``False``) 2024-12-18T00:16:37.4782940Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-12-18T00:16:37.4783483Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-12-18T00:16:37.4783981Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-12-18T00:16:37.4784433Z this does not do anything. (Default: ``True``) 2024-12-18T00:16:37.4785005Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-12-18T00:16:37.4785601Z module classes to ignore for mixed precision when using an 2024-12-18T00:16:37.4786089Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-12-18T00:16:37.4786594Z applied to them separately with mixed precision disabled (meaning 2024-12-18T00:16:37.4787233Z that the final FSDP construction would deviate from the specified 2024-12-18T00:16:37.4787759Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-12-18T00:16:37.4788281Z not do anything. This API is experimental and subject to change. 2024-12-18T00:16:37.4788714Z (Default: ``(_BatchNorm,)``) 2024-12-18T00:16:37.4788921Z 2024-12-18T00:16:37.4789123Z .. note:: This API is experimental and subject to change. 2024-12-18T00:16:37.4789397Z 2024-12-18T00:16:37.4789621Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-12-18T00:16:37.4789974Z 2024-12-18T00:16:37.4790162Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-12-18T00:16:37.4790576Z precision, but buffers are not. 2024-12-18T00:16:37.4790795Z 2024-12-18T00:16:37.4790999Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-12-18T00:16:37.4791534Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-12-18T00:16:37.4792100Z Disabling FSDP's mixed precision for those norm modules only means that 2024-12-18T00:16:37.4792651Z the affine parameters are kept in ``float32``. However, this incurs 2024-12-18T00:16:37.4793221Z separate all-gathers and reduce-scatters for those norm modules, which 2024-12-18T00:16:37.4793792Z may be inefficient, so if the workload permits, the user should prefer 2024-12-18T00:16:37.4794278Z to still apply mixed precision to those modules. 2024-12-18T00:16:37.4794535Z 2024-12-18T00:16:37.4794752Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-12-18T00:16:37.4795297Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-12-18T00:16:37.4795836Z modules will have FSDP applied to them separately with mixed precision 2024-12-18T00:16:37.4796358Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-12-18T00:16:37.4796657Z 2024-12-18T00:16:37.4796867Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-12-18T00:16:37.4797415Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-12-18T00:16:37.4797923Z its ``cast_root_forward_inputs`` takes precedence over its 2024-12-18T00:16:37.4798461Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-12-18T00:16:37.4798964Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-12-18T00:16:37.4799523Z sufficient for the typical case where each FSDP instance has the same 2024-12-18T00:16:37.4800093Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-12-18T00:16:37.4800629Z ``param_dtype`` at the beginning of the model's forward pass. 2024-12-18T00:16:37.4800923Z 2024-12-18T00:16:37.4801144Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-12-18T00:16:37.4801708Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-12-18T00:16:37.4802257Z values to configure casting inputs or not before each instance's 2024-12-18T00:16:37.4802779Z forward. In such a case, since the casts happen before each FSDP 2024-12-18T00:16:37.4803314Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-12-18T00:16:37.4803884Z submodules run before its FSDP submodules to avoid the activation dtype 2024-12-18T00:16:37.4804449Z being changed due to a different ``MixedPrecision`` configuration. 2024-12-18T00:16:37.4804771Z 2024-12-18T00:16:37.4804879Z Example:: 2024-12-18T00:16:37.4805012Z 2024-12-18T00:16:37.4805147Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.4805651Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-12-18T00:16:37.4806043Z >>> model[1] = FSDP( 2024-12-18T00:16:37.4806323Z >>> model[1], 2024-12-18T00:16:37.4806800Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-12-18T00:16:37.4807310Z >>> ) 2024-12-18T00:16:37.4816990Z >>> model = FSDP( 2024-12-18T00:16:37.4817426Z >>> model, 2024-12-18T00:16:37.4817912Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-12-18T00:16:37.4818439Z >>> ) 2024-12-18T00:16:37.4818570Z 2024-12-18T00:16:37.4818787Z The above shows a working example. On the other hand, if ``model[1]`` 2024-12-18T00:16:37.4819327Z were replaced with ``model[0]``, meaning that the submodule using 2024-12-18T00:16:37.4819880Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-12-18T00:16:37.4820457Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-12-18T00:16:37.4820897Z ones. 2024-12-18T00:16:37.4821021Z 2024-12-18T00:16:37.4821025Z 2024-12-18T00:16:37.4821296Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.4821664Z 2024-12-18T00:16:37.4899320Z 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=649. 2024-12-18T00:16:37.4900505Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.4901146Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T00:16:37.4901534Z 2024-12-18T00:16:37.4901800Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-12-18T00:16:37.4902403Z The target module does not have to be a FSDP module. If the target 2024-12-18T00:16:37.4902959Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-12-18T00:16:37.4903347Z 2024-12-18T00:16:37.4903576Z .. note:: This API should be called for only the top-level (root) 2024-12-18T00:16:37.4903988Z module. 2024-12-18T00:16:37.4904131Z 2024-12-18T00:16:37.4904346Z .. note:: This API enables users to transparently use the conventional 2024-12-18T00:16:37.4904871Z ``state_dict`` API to take model checkpoints in cases where the 2024-12-18T00:16:37.4905400Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-12-18T00:16:37.4905945Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-12-18T00:16:37.4906517Z instances, while dispatching into `sharded_state_dict` implementation 2024-12-18T00:16:37.4906969Z for FSDP: 2024-12-18T00:16:37.4907121Z 2024-12-18T00:16:37.4907214Z Example:: 2024-12-18T00:16:37.4907373Z 2024-12-18T00:16:37.4907510Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.4907871Z >>> model = DDP(FSDP(...)) 2024-12-18T00:16:37.4908201Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:37.4908518Z >>> model, 2024-12-18T00:16:37.4908813Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T00:16:37.4909281Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-12-18T00:16:37.4909852Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-12-18T00:16:37.4910295Z >>> ) 2024-12-18T00:16:37.4910572Z >>> param_state_dict = model.state_dict() 2024-12-18T00:16:37.4910995Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T00:16:37.4911286Z 2024-12-18T00:16:37.4911375Z Args: 2024-12-18T00:16:37.4911640Z module (torch.nn.Module): Root module. 2024-12-18T00:16:37.4912118Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T00:16:37.4912957Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-12-18T00:16:37.4913445Z target ``state_dict_type``. 2024-12-18T00:16:37.4913928Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-12-18T00:16:37.4914485Z for the optimizer state dict. 2024-12-18T00:16:37.4914770Z 2024-12-18T00:16:37.4914864Z Returns: 2024-12-18T00:16:37.4915237Z A StateDictSettings that include the previous state_dict type and 2024-12-18T00:16:37.4915701Z configuration for the module. 2024-12-18T00:16:37.4916011Z 2024-12-18T00:16:37.4916379Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.4916762Z 2024-12-18T00:16:37.4917564Z 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=805. 2024-12-18T00:16:37.4918826Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.4919465Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T00:16:37.4919848Z 2024-12-18T00:16:37.4920168Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-12-18T00:16:37.4920742Z :meth:`set_state_dict_type` for the detail. 2024-12-18T00:16:37.4920983Z 2024-12-18T00:16:37.4921096Z Example:: 2024-12-18T00:16:37.4921239Z 2024-12-18T00:16:37.4921385Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.4921732Z >>> model = DDP(FSDP(...)) 2024-12-18T00:16:37.4922064Z >>> with FSDP.state_dict_type( 2024-12-18T00:16:37.4922386Z >>> model, 2024-12-18T00:16:37.4922693Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T00:16:37.4923033Z >>> ): 2024-12-18T00:16:37.4923291Z >>> checkpoint = model.state_dict() 2024-12-18T00:16:37.4923537Z 2024-12-18T00:16:37.4923626Z Args: 2024-12-18T00:16:37.4923890Z module (torch.nn.Module): Root module. 2024-12-18T00:16:37.4924371Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T00:16:37.4924974Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-12-18T00:16:37.4925486Z configuration for the target ``state_dict_type``. 2024-12-18T00:16:37.4926002Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-12-18T00:16:37.4926570Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-12-18T00:16:37.4926981Z 2024-12-18T00:16:37.4927357Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.4927726Z 2024-12-18T00:16:37.4956936Z 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=1818. 2024-12-18T00:16:37.4958129Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.4958599Z 2024-12-18T00:16:37.4958841Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-12-18T00:16:37.4959214Z 2024-12-18T00:16:37.4959406Z The given state-dict can be transformed to one of three types: 2024-12-18T00:16:37.4960021Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-12-18T00:16:37.4960570Z 2024-12-18T00:16:37.4960870Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-12-18T00:16:37.4961448Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-12-18T00:16:37.4961877Z avoid OOM. 2024-12-18T00:16:37.4962003Z 2024-12-18T00:16:37.4962363Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-12-18T00:16:37.4962967Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-12-18T00:16:37.4963369Z memory. 2024-12-18T00:16:37.4963501Z 2024-12-18T00:16:37.4963721Z For local state_dict, no transformation will be performed. But a state 2024-12-18T00:16:37.4964333Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-12-18T00:16:37.4964842Z nature (this is not supported yet). 2024-12-18T00:16:37.4965056Z 2024-12-18T00:16:37.4965168Z Example:: 2024-12-18T00:16:37.4965292Z 2024-12-18T00:16:37.4965434Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.4965908Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T00:16:37.4966431Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T00:16:37.4966889Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T00:16:37.4967397Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T00:16:37.4967825Z >>> # Save a checkpoint 2024-12-18T00:16:37.4968098Z >>> model, optim = ... 2024-12-18T00:16:37.4968390Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:37.4968685Z >>> model, 2024-12-18T00:16:37.4968955Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:37.4969320Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4969704Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4970058Z >>> ) 2024-12-18T00:16:37.4970298Z >>> state_dict = model.state_dict() 2024-12-18T00:16:37.4970695Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T00:16:37.4971129Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T00:16:37.4971481Z >>> # Load a checkpoint 2024-12-18T00:16:37.4971768Z >>> model, optim = ... 2024-12-18T00:16:37.4972100Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T00:16:37.4972482Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:37.4972780Z >>> model, 2024-12-18T00:16:37.4973034Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:37.4973396Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4974007Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4974364Z >>> ) 2024-12-18T00:16:37.4974616Z >>> model.load_state_dict(state_dict) 2024-12-18T00:16:37.4974991Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T00:16:37.4975374Z >>> model, optim, optim_state_dict 2024-12-18T00:16:37.4975686Z >>> ) 2024-12-18T00:16:37.4975952Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T00:16:37.4976204Z 2024-12-18T00:16:37.4976290Z Args: 2024-12-18T00:16:37.4976609Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T00:16:37.4977129Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T00:16:37.4977569Z were passed into the optimizer ``optim``. 2024-12-18T00:16:37.4978003Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T00:16:37.4978399Z parameters. 2024-12-18T00:16:37.4978765Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-12-18T00:16:37.4979313Z transform. If the value is None, optim.state_dict() will be used. ( 2024-12-18T00:16:37.4979745Z Default: ``None``) 2024-12-18T00:16:37.4980154Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T00:16:37.4980707Z are sharded or ``None`` if using the default process group. ( 2024-12-18T00:16:37.4981108Z Default: ``None``) 2024-12-18T00:16:37.4981278Z 2024-12-18T00:16:37.4981379Z Returns: 2024-12-18T00:16:37.4981698Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-12-18T00:16:37.4982166Z ``model``. The sharding of the optimizer state is based on 2024-12-18T00:16:37.4982554Z ``state_dict_type``. 2024-12-18T00:16:37.4982837Z 2024-12-18T00:16:37.4983088Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.4983519Z 2024-12-18T00:16:37.4984358Z 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=1916. 2024-12-18T00:16:37.4985653Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.4986031Z 2024-12-18T00:16:37.4986396Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-12-18T00:16:37.4986870Z 2024-12-18T00:16:37.4987051Z Given a ``optim_state_dict`` that is transformed through 2024-12-18T00:16:37.4987540Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-12-18T00:16:37.4988084Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-12-18T00:16:37.4988609Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-12-18T00:16:37.4988928Z 2024-12-18T00:16:37.4989054Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.4989533Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T00:16:37.4990050Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T00:16:37.4990487Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T00:16:37.4990987Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T00:16:37.4991410Z >>> # Save a checkpoint 2024-12-18T00:16:37.4991692Z >>> model, optim = ... 2024-12-18T00:16:37.4991977Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:37.4992257Z >>> model, 2024-12-18T00:16:37.4992522Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:37.4992882Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4993270Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4993620Z >>> ) 2024-12-18T00:16:37.4993849Z >>> state_dict = model.state_dict() 2024-12-18T00:16:37.4994188Z >>> original_osd = optim.state_dict() 2024-12-18T00:16:37.4994544Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-12-18T00:16:37.4994877Z >>> model, 2024-12-18T00:16:37.4995112Z >>> optim, 2024-12-18T00:16:37.4995360Z >>> optim_state_dict=original_osd 2024-12-18T00:16:37.4995679Z >>> ) 2024-12-18T00:16:37.4995954Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T00:16:37.4996316Z >>> # Load a checkpoint 2024-12-18T00:16:37.4996595Z >>> model, optim = ... 2024-12-18T00:16:37.4996910Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T00:16:37.4997286Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:37.4997578Z >>> model, 2024-12-18T00:16:37.4997840Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:37.4998197Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4998681Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:37.4999037Z >>> ) 2024-12-18T00:16:37.4999282Z >>> model.load_state_dict(state_dict) 2024-12-18T00:16:37.4999667Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T00:16:37.5000053Z >>> model, optim, optim_state_dict 2024-12-18T00:16:37.5000356Z >>> ) 2024-12-18T00:16:37.5000610Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T00:16:37.5000859Z 2024-12-18T00:16:37.5000949Z Args: 2024-12-18T00:16:37.5001269Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T00:16:37.5001790Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T00:16:37.5002229Z were passed into the optimizer ``optim``. 2024-12-18T00:16:37.5002661Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T00:16:37.5003056Z parameters. 2024-12-18T00:16:37.5003436Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-12-18T00:16:37.5004013Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-12-18T00:16:37.5004568Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-12-18T00:16:37.5005064Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-12-18T00:16:37.5005561Z load_directly (bool): If this is set to True, this API will also 2024-12-18T00:16:37.5006110Z call optim.load_state_dict(result) before returning the result. 2024-12-18T00:16:37.5006674Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-12-18T00:16:37.5007129Z (Default: ``False``) 2024-12-18T00:16:37.5007564Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T00:16:37.5008115Z are sharded or ``None`` if using the default process group. ( 2024-12-18T00:16:37.5008524Z Default: ``None``) 2024-12-18T00:16:37.5008695Z 2024-12-18T00:16:37.5008960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5009328Z 2024-12-18T00:16:37.5392737Z 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=137. 2024-12-18T00:16:37.5393756Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5394150Z 2024-12-18T00:16:37.5394387Z RemoteModule instance can only be created after RPC initialization. 2024-12-18T00:16:37.5394746Z 2024-12-18T00:16:37.5394940Z It creates a user-specified module on a specified remote node. 2024-12-18T00:16:37.5395483Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T00:16:37.5395947Z executed on the remote node. 2024-12-18T00:16:37.5396376Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T00:16:37.5396876Z gradients back to the corresponding remote module. 2024-12-18T00:16:37.5397503Z It can be shared across processors using `RPC framework `__, 2024-12-18T00:16:37.5398179Z without incurring any overheads of copying the actual module, 2024-12-18T00:16:37.5398806Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-12-18T00:16:37.5399247Z pointing to the remote module. 2024-12-18T00:16:37.5399439Z 2024-12-18T00:16:37.5399651Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-12-18T00:16:37.5400165Z the ``forward`` method of the module returned by the ``module_cls``. 2024-12-18T00:16:37.5400490Z 2024-12-18T00:16:37.5400877Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-12-18T00:16:37.5401423Z 2024-12-18T00:16:37.5401678Z Particularly, to create a hybrid model, typically the local modules should be 2024-12-18T00:16:37.5402421Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-12-18T00:16:37.5403010Z Hybrid Example: 2024-12-18T00:16:37.5403314Z >>> class HybridModel(nn.Module): 2024-12-18T00:16:37.5403666Z >>> def __init__(self) -> None: 2024-12-18T00:16:37.5404007Z >>> nn.Module.__init__(self) 2024-12-18T00:16:37.5404376Z >>> self.remote_embedding = RemoteModule(...) 2024-12-18T00:16:37.5404754Z >>> self.local_linear = nn.Linear(...) 2024-12-18T00:16:37.5405003Z 2024-12-18T00:16:37.5405204Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T00:16:37.5405771Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T00:16:37.5406352Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-12-18T00:16:37.5406809Z ``def forward(input: Tensor) -> Tensor:`` and 2024-12-18T00:16:37.5407205Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-12-18T00:16:37.5407487Z 2024-12-18T00:16:37.5407595Z .. note:: 2024-12-18T00:16:37.5407865Z If the remote module is placed on a cuda device, 2024-12-18T00:16:37.5408510Z any input CPU tensors will be automatically moved to the same cuda device, 2024-12-18T00:16:37.5409459Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-12-18T00:16:37.5409999Z 2024-12-18T00:16:37.5410106Z Args: 2024-12-18T00:16:37.5410890Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T00:16:37.5411722Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T00:16:37.5412239Z formats: 2024-12-18T00:16:37.5412392Z 2024-12-18T00:16:37.5412536Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T00:16:37.5412954Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T00:16:37.5413218Z 2024-12-18T00:16:37.5413476Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T00:16:37.5413954Z module_cls (nn.Module): For example, 2024-12-18T00:16:37.5414299Z >>> class MyModule(nn.Module): 2024-12-18T00:16:37.5414619Z >>> def forward(input): 2024-12-18T00:16:37.5414928Z >>> return input + 1 2024-12-18T00:16:37.5415221Z >>> 2024-12-18T00:16:37.5415442Z >>> module_cls = MyModule 2024-12-18T00:16:37.5415839Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T00:16:37.5416355Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T00:16:37.5416943Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T00:16:37.5417581Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T00:16:37.5418161Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T00:16:37.5418726Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T00:16:37.5419088Z 2024-12-18T00:16:37.5419180Z Returns: 2024-12-18T00:16:37.5419551Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T00:16:37.5420148Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T00:16:37.5420770Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T00:16:37.5421309Z on the user-provided module on the remote side. 2024-12-18T00:16:37.5421564Z 2024-12-18T00:16:37.5421662Z Example:: 2024-12-18T00:16:37.5421943Z Run the following code in two different processes: 2024-12-18T00:16:37.5422216Z 2024-12-18T00:16:37.5422330Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.5422648Z >>> # On worker 0: 2024-12-18T00:16:37.5422900Z >>> import torch 2024-12-18T00:16:37.5423173Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5423519Z >>> from torch import nn, Tensor 2024-12-18T00:16:37.5423950Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T00:16:37.5424383Z >>> 2024-12-18T00:16:37.5424653Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T00:16:37.5425020Z >>> remote_linear_module = RemoteModule( 2024-12-18T00:16:37.5425380Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T00:16:37.5425714Z >>> ) 2024-12-18T00:16:37.5425948Z >>> input = torch.randn(128, 20) 2024-12-18T00:16:37.5426315Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T00:16:37.5426675Z >>> ret = ret_fut.wait() 2024-12-18T00:16:37.5426961Z >>> rpc.shutdown() 2024-12-18T00:16:37.5427133Z 2024-12-18T00:16:37.5427227Z >>> # On worker 1: 2024-12-18T00:16:37.5427479Z >>> import torch 2024-12-18T00:16:37.5427759Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5428073Z >>> 2024-12-18T00:16:37.5428336Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T00:16:37.5428690Z >>> rpc.shutdown() 2024-12-18T00:16:37.5428847Z 2024-12-18T00:16:37.5429112Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5429561Z 2024-12-18T00:16:37.5430281Z 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=514. 2024-12-18T00:16:37.5431419Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5431831Z 2024-12-18T00:16:37.5432183Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-12-18T00:16:37.5432638Z 2024-12-18T00:16:37.5432960Z This alternate initialization method can be particularly useful if we want to create multiple 2024-12-18T00:16:37.5433724Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-12-18T00:16:37.5434168Z 2024-12-18T00:16:37.5434446Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-12-18T00:16:37.5435033Z which is not supported. The recommended way is as follows: 2024-12-18T00:16:37.5435326Z 2024-12-18T00:16:37.5435460Z 1. the sender creates a RemoteModule; 2024-12-18T00:16:37.5435842Z 2. the sender sends its ``module_rref`` over RPC; 2024-12-18T00:16:37.5436425Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-12-18T00:16:37.5436893Z 2024-12-18T00:16:37.5436991Z Example:: 2024-12-18T00:16:37.5437284Z Run the following code in two different processes: 2024-12-18T00:16:37.5437561Z 2024-12-18T00:16:37.5437674Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.5437994Z >>> # On worker 0: 2024-12-18T00:16:37.5438248Z >>> import torch 2024-12-18T00:16:37.5438618Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5438968Z >>> from torch import nn, Tensor 2024-12-18T00:16:37.5439400Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T00:16:37.5439828Z >>> 2024-12-18T00:16:37.5440094Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T00:16:37.5440448Z >>> remote_module = RemoteModule( 2024-12-18T00:16:37.5440873Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T00:16:37.5441202Z >>> ) 2024-12-18T00:16:37.5441418Z >>> 2024-12-18T00:16:37.5441636Z >>> remote_module1 = rpc.rpc_sync( 2024-12-18T00:16:37.5441952Z >>> "worker1/cpu", 2024-12-18T00:16:37.5442257Z >>> RemoteModule.init_from_module_rref, 2024-12-18T00:16:37.5442657Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-12-18T00:16:37.5443016Z >>> ) 2024-12-18T00:16:37.5443226Z >>> rpc.shutdown() 2024-12-18T00:16:37.5443399Z 2024-12-18T00:16:37.5443491Z >>> # On worker 1: 2024-12-18T00:16:37.5443747Z >>> import torch 2024-12-18T00:16:37.5444029Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5444354Z >>> 2024-12-18T00:16:37.5444605Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T00:16:37.5444953Z >>> rpc.shutdown() 2024-12-18T00:16:37.5445123Z 2024-12-18T00:16:37.5445208Z Args: 2024-12-18T00:16:37.5445626Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T00:16:37.5446334Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T00:16:37.5446841Z formats: 2024-12-18T00:16:37.5446979Z 2024-12-18T00:16:37.5447124Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T00:16:37.5447534Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T00:16:37.5447810Z 2024-12-18T00:16:37.5448054Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T00:16:37.5448676Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-12-18T00:16:37.5449164Z the created remote module. 2024-12-18T00:16:37.5449648Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T00:16:37.5450353Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T00:16:37.5450945Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T00:16:37.5451529Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T00:16:37.5451893Z 2024-12-18T00:16:37.5451983Z Returns: 2024-12-18T00:16:37.5452416Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T00:16:37.5453018Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-12-18T00:16:37.5453633Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T00:16:37.5454174Z on the user-provided module on the remote side. 2024-12-18T00:16:37.5454442Z 2024-12-18T00:16:37.5454697Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5455077Z 2024-12-18T00:16:37.5455724Z 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=606. 2024-12-18T00:16:37.5456716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5457104Z 2024-12-18T00:16:37.5457333Z A RemoteModule instance can only be created after RPC initialization. 2024-12-18T00:16:37.5457679Z 2024-12-18T00:16:37.5457890Z It creates a user-specified module on a specified remote node. 2024-12-18T00:16:37.5458443Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T00:16:37.5458898Z executed on the remote node. 2024-12-18T00:16:37.5459341Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T00:16:37.5459859Z gradients back to the corresponding remote module. 2024-12-18T00:16:37.5460141Z 2024-12-18T00:16:37.5460362Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-12-18T00:16:37.5460918Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-12-18T00:16:37.5461507Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-12-18T00:16:37.5462064Z and ``forward`` are the same as the ``forward`` method of the module 2024-12-18T00:16:37.5462494Z returned by the ``module_cls``. 2024-12-18T00:16:37.5462710Z 2024-12-18T00:16:37.5462913Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T00:16:37.5463486Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T00:16:37.5464097Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-12-18T00:16:37.5464437Z 2024-12-18T00:16:37.5464574Z | ``def forward(input: Tensor) -> Tensor:`` 2024-12-18T00:16:37.5464968Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-12-18T00:16:37.5465254Z 2024-12-18T00:16:37.5465340Z Args: 2024-12-18T00:16:37.5465758Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T00:16:37.5466519Z The format should be "/", where the device field can be parsed as torch.device type. 2024-12-18T00:16:37.5467124Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-12-18T00:16:37.5467630Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T00:16:37.5468238Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-12-18T00:16:37.5468620Z 2024-12-18T00:16:37.5468731Z >>> class MyModule(nn.Module): 2024-12-18T00:16:37.5469053Z >>> def forward(input): 2024-12-18T00:16:37.5469358Z >>> return input + 1 2024-12-18T00:16:37.5469652Z >>> 2024-12-18T00:16:37.5469870Z >>> module_cls = MyModule 2024-12-18T00:16:37.5470076Z 2024-12-18T00:16:37.5470272Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T00:16:37.5470824Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T00:16:37.5471190Z 2024-12-18T00:16:37.5471280Z Returns: 2024-12-18T00:16:37.5471656Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T00:16:37.5472261Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T00:16:37.5472935Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T00:16:37.5473483Z on the user-provided module on the remote side. 2024-12-18T00:16:37.5474019Z 2024-12-18T00:16:37.5474144Z Example:: 2024-12-18T00:16:37.5474434Z Run the following code in two different processes: 2024-12-18T00:16:37.5474700Z 2024-12-18T00:16:37.5474831Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.5475157Z >>> # On worker 0: 2024-12-18T00:16:37.5475402Z >>> import torch 2024-12-18T00:16:37.5475689Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5476046Z >>> from torch import nn, Tensor 2024-12-18T00:16:37.5476485Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T00:16:37.5476917Z >>> 2024-12-18T00:16:37.5477171Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T00:16:37.5477550Z >>> remote_linear_module = RemoteModule( 2024-12-18T00:16:37.5477913Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T00:16:37.5478245Z >>> ) 2024-12-18T00:16:37.5478543Z >>> input = torch.randn(128, 20) 2024-12-18T00:16:37.5478900Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T00:16:37.5479277Z >>> ret = ret_fut.wait() 2024-12-18T00:16:37.5479561Z >>> rpc.shutdown() 2024-12-18T00:16:37.5479716Z 2024-12-18T00:16:37.5479821Z >>> # On worker 1: 2024-12-18T00:16:37.5480073Z >>> import torch 2024-12-18T00:16:37.5480342Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5480667Z >>> 2024-12-18T00:16:37.5480927Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T00:16:37.5481281Z >>> rpc.shutdown() 2024-12-18T00:16:37.5481436Z 2024-12-18T00:16:37.5481640Z Furthermore, a more practical example that is combined with 2024-12-18T00:16:37.5482429Z `DistributedDataParallel `__ (DDP) 2024-12-18T00:16:37.5483348Z can be found in this `tutorial `__. 2024-12-18T00:16:37.5483806Z 2024-12-18T00:16:37.5484056Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5484436Z 2024-12-18T00:16:37.5552636Z 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. 2024-12-18T00:16:37.5553650Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5554028Z 2024-12-18T00:16:37.5554281Z DistributedOptimizer takes remote references to parameters scattered 2024-12-18T00:16:37.5554888Z across workers and applies the given optimizer locally for each parameter. 2024-12-18T00:16:37.5555242Z 2024-12-18T00:16:37.5555490Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-12-18T00:16:37.5555986Z to retrieve the gradients for specific parameters. 2024-12-18T00:16:37.5556256Z 2024-12-18T00:16:37.5556354Z Concurrent calls to 2024-12-18T00:16:37.5556720Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-12-18T00:16:37.5557182Z either from the same or different clients, will 2024-12-18T00:16:37.5557665Z be serialized on each worker -- as each worker's optimizer can only work 2024-12-18T00:16:37.5558216Z on one set of gradients at a time. However, there is no guarantee that 2024-12-18T00:16:37.5558861Z the full forward-backward-optimizer sequence will execute for one client 2024-12-18T00:16:37.5559444Z at a time. This means that the gradients being applied may not correspond 2024-12-18T00:16:37.5560177Z to the latest forward pass executed on a given worker. Also, there is no 2024-12-18T00:16:37.5560689Z guaranteed ordering across workers. 2024-12-18T00:16:37.5560900Z 2024-12-18T00:16:37.5561175Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-12-18T00:16:37.5561778Z by default, so that optimizer updates are not blocked by the Python Global 2024-12-18T00:16:37.5562449Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-12-18T00:16:37.5563054Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-12-18T00:16:37.5563674Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-12-18T00:16:37.5564166Z for your own custom optimizers. 2024-12-18T00:16:37.5564380Z 2024-12-18T00:16:37.5564481Z Args: 2024-12-18T00:16:37.5565002Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-12-18T00:16:37.5565588Z instantiate on each worker. 2024-12-18T00:16:37.5566015Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-12-18T00:16:37.5566447Z to optimize. 2024-12-18T00:16:37.5566820Z args: arguments to pass to the optimizer constructor on each worker. 2024-12-18T00:16:37.5567381Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-12-18T00:16:37.5567719Z 2024-12-18T00:16:37.5567823Z Example:: 2024-12-18T00:16:37.5568074Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.5568466Z >>> import torch.distributed.autograd as dist_autograd 2024-12-18T00:16:37.5568592Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:37.5568709Z >>> from torch import optim 2024-12-18T00:16:37.5568896Z >>> from torch.distributed.optim import DistributedOptimizer 2024-12-18T00:16:37.5568999Z >>> 2024-12-18T00:16:37.5569134Z >>> with dist_autograd.context() as context_id: 2024-12-18T00:16:37.5569247Z >>> # Forward pass. 2024-12-18T00:16:37.5569449Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-12-18T00:16:37.5569650Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-12-18T00:16:37.5569786Z >>> loss = rref1.to_here() + rref2.to_here() 2024-12-18T00:16:37.5569873Z >>> 2024-12-18T00:16:37.5569983Z >>> # Backward pass. 2024-12-18T00:16:37.5570132Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-12-18T00:16:37.5570221Z >>> 2024-12-18T00:16:37.5570326Z >>> # Optimizer. 2024-12-18T00:16:37.5570449Z >>> dist_optim = DistributedOptimizer( 2024-12-18T00:16:37.5570554Z >>> optim.SGD, 2024-12-18T00:16:37.5570650Z >>> [rref1, rref2], 2024-12-18T00:16:37.5570754Z >>> lr=0.05, 2024-12-18T00:16:37.5570840Z >>> ) 2024-12-18T00:16:37.5570952Z >>> dist_optim.step(context_id) 2024-12-18T00:16:37.5570957Z 2024-12-18T00:16:37.5571126Z __ https://github.com/pytorch/tutorials/pull/1465 2024-12-18T00:16:37.5571130Z 2024-12-18T00:16:37.5571381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5571390Z 2024-12-18T00:16:37.5572105Z 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. 2024-12-18T00:16:37.5572381Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5572388Z 2024-12-18T00:16:37.5572785Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-12-18T00:16:37.5572937Z This optimizer runs local optimizer at every step. 2024-12-18T00:16:37.5573278Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-12-18T00:16:37.5573283Z 2024-12-18T00:16:37.5573368Z Args: 2024-12-18T00:16:37.5573490Z optim: The local optimizer. 2024-12-18T00:16:37.5573945Z averager: A model averager instance to run post-localSGD algorithm. 2024-12-18T00:16:37.5574057Z 2024-12-18T00:16:37.5574226Z Example:: 2024-12-18T00:16:37.5574230Z 2024-12-18T00:16:37.5574356Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:37.5574452Z >>> import torch 2024-12-18T00:16:37.5574586Z >>> import torch.distributed as dist 2024-12-18T00:16:37.5574857Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T00:16:37.5575014Z >>> import torch.nn as nn 2024-12-18T00:16:37.5575249Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-12-18T00:16:37.5575524Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T00:16:37.5575641Z >>> PostLocalSGDState, 2024-12-18T00:16:37.5575742Z >>> post_localSGD_hook, 2024-12-18T00:16:37.5575843Z >>> ) 2024-12-18T00:16:37.5575930Z >>> 2024-12-18T00:16:37.5576100Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T00:16:37.5576242Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T00:16:37.5576332Z >>> ) 2024-12-18T00:16:37.5576433Z >>> 2024-12-18T00:16:37.5576579Z >>> # Register a post-localSGD communication hook. 2024-12-18T00:16:37.5576887Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T00:16:37.5577051Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:37.5577137Z >>> 2024-12-18T00:16:37.5577360Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-12-18T00:16:37.5577610Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-12-18T00:16:37.5577791Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T00:16:37.5578004Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-12-18T00:16:37.5578133Z >>> opt = PostLocalSGDOptimizer( 2024-12-18T00:16:37.5578236Z >>> optim=local_optim, 2024-12-18T00:16:37.5578484Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T00:16:37.5578589Z >>> ) 2024-12-18T00:16:37.5578677Z >>> 2024-12-18T00:16:37.5578913Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-12-18T00:16:37.5579215Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-12-18T00:16:37.5579610Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-12-18T00:16:37.5579720Z >>> for step in range(0, 200): 2024-12-18T00:16:37.5579821Z >>> opt.zero_grad() 2024-12-18T00:16:37.5579952Z >>> loss = loss_fn(output, labels) 2024-12-18T00:16:37.5580055Z >>> loss.backward() 2024-12-18T00:16:37.5580162Z >>> opt.step() 2024-12-18T00:16:37.5580166Z 2024-12-18T00:16:37.5580423Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5580427Z 2024-12-18T00:16:37.5666467Z 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=282. 2024-12-18T00:16:37.5666750Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5666755Z 2024-12-18T00:16:37.5667177Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-12-18T00:16:37.5667182Z 2024-12-18T00:16:37.5667333Z The sharing is done as described by ZeRO_. 2024-12-18T00:16:37.5667338Z 2024-12-18T00:16:37.5667502Z The local optimizer instance in each rank is only 2024-12-18T00:16:37.5667747Z responsible for updating approximately ``1 / world_size`` parameters and 2024-12-18T00:16:37.5667965Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-12-18T00:16:37.5668210Z parameters are updated locally, each rank will broadcast its parameters to 2024-12-18T00:16:37.5668408Z all other peers to keep all model replicas in the same state. 2024-12-18T00:16:37.5668696Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-12-18T00:16:37.5668955Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-12-18T00:16:37.5669072Z memory consumption. 2024-12-18T00:16:37.5669077Z 2024-12-18T00:16:37.5669335Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-12-18T00:16:37.5669635Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-12-18T00:16:37.5669873Z not divided among ranks. The partition is arbitrary and might not match the 2024-12-18T00:16:37.5670017Z the parameter registration or usage order. 2024-12-18T00:16:37.5670022Z 2024-12-18T00:16:37.5670113Z Arguments: 2024-12-18T00:16:37.5670306Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-12-18T00:16:37.5670504Z or :class:`dict` s giving all parameters, which will be sharded 2024-12-18T00:16:37.5670599Z across ranks. 2024-12-18T00:16:37.5670606Z 2024-12-18T00:16:37.5670714Z Keyword Args: 2024-12-18T00:16:37.5670936Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-12-18T00:16:37.5671042Z optimizer. 2024-12-18T00:16:37.5671250Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-12-18T00:16:37.5671446Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-12-18T00:16:37.5671609Z :meth:`torch.distributed.init_process_group`). 2024-12-18T00:16:37.5671834Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-12-18T00:16:37.5672056Z packed into buckets to speed up communication, and ``param.data`` 2024-12-18T00:16:37.5672254Z fields point to bucket views at different offsets; if ``False``, 2024-12-18T00:16:37.5672471Z each individual parameter is communicated separately, and each 2024-12-18T00:16:37.5672621Z ``params.data`` stays intact (default: ``False``). 2024-12-18T00:16:37.5672813Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-12-18T00:16:37.5673027Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-12-18T00:16:37.5673240Z synchronization; this requires (1) either a functional optimizer 2024-12-18T00:16:37.5673430Z for the ``optimizer_class`` argument or one with a functional 2024-12-18T00:16:37.5673792Z equivalent and (2) registering a DDP communication hook 2024-12-18T00:16:37.5674038Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-12-18T00:16:37.5674206Z parameters are packed into buckets matching those in 2024-12-18T00:16:37.5674362Z :class:`DistributedDataParallel`, meaning that the 2024-12-18T00:16:37.5674527Z ``parameters_as_bucket_view`` argument is ignored. 2024-12-18T00:16:37.5674716Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-12-18T00:16:37.5674825Z (per normal). 2024-12-18T00:16:37.5674923Z (default: ``False``) 2024-12-18T00:16:37.5675146Z **defaults: any trailing arguments, which are forwarded to the local 2024-12-18T00:16:37.5675240Z optimizer. 2024-12-18T00:16:37.5675244Z 2024-12-18T00:16:37.5675344Z Example:: 2024-12-18T00:16:37.5675348Z 2024-12-18T00:16:37.5675457Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.5675560Z >>> import torch.nn as nn 2024-12-18T00:16:37.5675778Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-12-18T00:16:37.5675980Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-12-18T00:16:37.5676221Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-12-18T00:16:37.5676337Z >>> ddp = DDP(model, device_ids=[rank]) 2024-12-18T00:16:37.5676456Z >>> opt = ZeroRedundancyOptimizer( 2024-12-18T00:16:37.5676571Z >>> ddp.parameters(), 2024-12-18T00:16:37.5676693Z >>> optimizer_class=torch.optim.Adam, 2024-12-18T00:16:37.5676797Z >>> lr=0.01 2024-12-18T00:16:37.5676958Z >>> ) 2024-12-18T00:16:37.5677102Z >>> ddp(inputs).sum().backward() 2024-12-18T00:16:37.5677207Z >>> opt.step() 2024-12-18T00:16:37.5677212Z 2024-12-18T00:16:37.5677305Z .. warning:: 2024-12-18T00:16:37.5677524Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-12-18T00:16:37.5677671Z passed-in parameters are the same dense type. 2024-12-18T00:16:37.5677675Z 2024-12-18T00:16:37.5677821Z .. warning:: 2024-12-18T00:16:37.5678083Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-12-18T00:16:37.5678287Z the way that overlapping :class:`DistributedDataParallel` with 2024-12-18T00:16:37.5678591Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-12-18T00:16:37.5678809Z two or three training iterations do not perform parameter updates in 2024-12-18T00:16:37.5679015Z the optimizer step, depending on if ``static_graph=False`` or 2024-12-18T00:16:37.5679201Z ``static_graph=True``, respectively. This is because it needs 2024-12-18T00:16:37.5679403Z information about the gradient bucketing strategy used by 2024-12-18T00:16:37.5679624Z :class:`DistributedDataParallel`, which is not finalized until the 2024-12-18T00:16:37.5679828Z second forward pass if ``static_graph=False`` or until the third 2024-12-18T00:16:37.5680053Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-12-18T00:16:37.5680163Z is to prepend dummy inputs. 2024-12-18T00:16:37.5680170Z 2024-12-18T00:16:37.5680438Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-12-18T00:16:37.5680443Z 2024-12-18T00:16:37.5680575Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-12-18T00:16:37.5680579Z 2024-12-18T00:16:37.5680583Z 2024-12-18T00:16:37.5680848Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5680853Z 2024-12-18T00:16:37.5842847Z 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. 2024-12-18T00:16:37.5843137Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.5843142Z 2024-12-18T00:16:37.5843371Z Custom reducer class that can be used to specify a custom operation that 2024-12-18T00:16:37.5843558Z reduces losses of multiple microbatches into one value. 2024-12-18T00:16:37.5843573Z 2024-12-18T00:16:37.5843664Z Example: 2024-12-18T00:16:37.5843780Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.5843893Z >>> sum_reducer = _CustomReducer( 2024-12-18T00:16:37.5843995Z >>> torch.tensor(0.0), 2024-12-18T00:16:37.5844108Z >>> lambda a, b: a + b 2024-12-18T00:16:37.5844194Z >>> ) 2024-12-18T00:16:37.5844198Z 2024-12-18T00:16:37.5844465Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.5844470Z 2024-12-18T00:16:37.6276363Z 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. 2024-12-18T00:16:37.6276921Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.6276931Z 2024-12-18T00:16:37.6277360Z A decorator for a function indicating that the return value of the function 2024-12-18T00:16:37.6277571Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-12-18T00:16:37.6277830Z function can run asynchronously on the RPC callee. More specifically, the 2024-12-18T00:16:37.6278065Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-12-18T00:16:37.6278298Z function and installs subsequent processing steps as a callback to that 2024-12-18T00:16:37.6278592Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-12-18T00:16:37.6278795Z from the :class:`~torch.futures.Future` when completed and send the 2024-12-18T00:16:37.6278986Z value back as the RPC response. That also means the returned 2024-12-18T00:16:37.6279215Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-12-18T00:16:37.6279682Z sent through RPC. This decorator is useful when the wrapped function's 2024-12-18T00:16:37.6279886Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-12-18T00:16:37.6280126Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-12-18T00:16:37.6280132Z 2024-12-18T00:16:37.6280453Z .. note:: To enable asynchronous execution, applications must pass the 2024-12-18T00:16:37.6280685Z function object returned by this decorator to RPC APIs. If RPC detected 2024-12-18T00:16:37.6280918Z attributes installed by this decorator, it knows that this function 2024-12-18T00:16:37.6281104Z returns a ``Future`` object and will handle that accordingly. 2024-12-18T00:16:37.6281331Z However, this does not mean this decorator has to be outmost one when 2024-12-18T00:16:37.6281557Z defining a function. For example, when combined with ``@staticmethod`` 2024-12-18T00:16:37.6281784Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-12-18T00:16:37.6282005Z inner decorator to allow the target function be recognized as a static 2024-12-18T00:16:37.6282234Z or class function. This target function can still execute asynchronously 2024-12-18T00:16:37.6282473Z because, when accessed, the static or class method preserves attributes 2024-12-18T00:16:37.6282630Z installed by ``@rpc.functions.async_execution``. 2024-12-18T00:16:37.6282635Z 2024-12-18T00:16:37.6282639Z 2024-12-18T00:16:37.6282742Z Example:: 2024-12-18T00:16:37.6282940Z The returned :class:`~torch.futures.Future` object can come from 2024-12-18T00:16:37.6283083Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-12-18T00:16:37.6283305Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-12-18T00:16:37.6283479Z constructor. The example below shows directly using the 2024-12-18T00:16:37.6283624Z :class:`~torch.futures.Future` returned by 2024-12-18T00:16:37.6283746Z :meth:`~torch.futures.Future.then`. 2024-12-18T00:16:37.6283752Z 2024-12-18T00:16:37.6283888Z >>> from torch.distributed import rpc 2024-12-18T00:16:37.6284010Z >>> 2024-12-18T00:16:37.6284186Z >>> # omitting setup and shutdown RPC 2024-12-18T00:16:37.6284272Z >>> 2024-12-18T00:16:37.6284367Z >>> # On all workers 2024-12-18T00:16:37.6284496Z >>> @rpc.functions.async_execution 2024-12-18T00:16:37.6284613Z >>> def async_add_chained(to, x, y, z): 2024-12-18T00:16:37.6284819Z >>> # This function runs on "worker1" and returns immediately when 2024-12-18T00:16:37.6285009Z >>> # the callback is installed through the `then(cb)` API. In the 2024-12-18T00:16:37.6285205Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-12-18T00:16:37.6285363Z >>> # When the return value of that `rpc_async` arrives at 2024-12-18T00:16:37.6285549Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-12-18T00:16:37.6285748Z >>> # and set the value for the previously returned `Future`, which 2024-12-18T00:16:37.6285932Z >>> # will then trigger RPC to send the result back to "worker0". 2024-12-18T00:16:37.6286111Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:37.6286223Z >>> lambda fut: fut.wait() + z 2024-12-18T00:16:37.6286324Z >>> ) 2024-12-18T00:16:37.6286413Z >>> 2024-12-18T00:16:37.6286509Z >>> # On worker0 2024-12-18T00:16:37.6286623Z >>> # xdoctest: +SKIP 2024-12-18T00:16:37.6286723Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:37.6286824Z >>> "worker1", 2024-12-18T00:16:37.6286924Z >>> async_add_chained, 2024-12-18T00:16:37.6287047Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-12-18T00:16:37.6287146Z >>> ) 2024-12-18T00:16:37.6287264Z >>> print(ret) # prints tensor([3., 3.]) 2024-12-18T00:16:37.6287269Z 2024-12-18T00:16:37.6287503Z When combined with TorchScript decorators, this decorator must be the 2024-12-18T00:16:37.6287669Z outmost one. 2024-12-18T00:16:37.6287675Z 2024-12-18T00:16:37.6287792Z >>> from torch import Tensor 2024-12-18T00:16:37.6287907Z >>> from torch.futures import Future 2024-12-18T00:16:37.6288028Z >>> from torch.distributed import rpc 2024-12-18T00:16:37.6288127Z >>> 2024-12-18T00:16:37.6288243Z >>> # omitting setup and shutdown RPC 2024-12-18T00:16:37.6288372Z >>> 2024-12-18T00:16:37.6288496Z >>> # On all workers 2024-12-18T00:16:37.6288595Z >>> @torch.jit.script 2024-12-18T00:16:37.6288760Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-12-18T00:16:37.6288853Z >>> return x + y 2024-12-18T00:16:37.6288966Z >>> 2024-12-18T00:16:37.6289082Z >>> @rpc.functions.async_execution 2024-12-18T00:16:37.6289182Z >>> @torch.jit.script 2024-12-18T00:16:37.6289386Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-12-18T00:16:37.6289534Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-12-18T00:16:37.6289638Z >>> 2024-12-18T00:16:37.6289729Z >>> # On worker0 2024-12-18T00:16:37.6289844Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:37.6289936Z >>> "worker1", 2024-12-18T00:16:37.6290029Z >>> async_add, 2024-12-18T00:16:37.6290159Z >>> args=("worker2", torch.ones(2), 1) 2024-12-18T00:16:37.6290245Z >>> ) 2024-12-18T00:16:37.6290376Z >>> print(ret) # prints tensor([2., 2.]) 2024-12-18T00:16:37.6290383Z 2024-12-18T00:16:37.6290601Z When combined with static or class method, this decorator must be the 2024-12-18T00:16:37.6290694Z inner one. 2024-12-18T00:16:37.6290712Z 2024-12-18T00:16:37.6290833Z >>> from torch.distributed import rpc 2024-12-18T00:16:37.6290921Z >>> 2024-12-18T00:16:37.6291052Z >>> # omitting setup and shutdown RPC 2024-12-18T00:16:37.6291140Z >>> 2024-12-18T00:16:37.6291250Z >>> # On all workers 2024-12-18T00:16:37.6291364Z >>> class AsyncExecutionClass: 2024-12-18T00:16:37.6291452Z >>> 2024-12-18T00:16:37.6291567Z >>> @staticmethod 2024-12-18T00:16:37.6291686Z >>> @rpc.functions.async_execution 2024-12-18T00:16:37.6291815Z >>> def static_async_add(to, x, y, z): 2024-12-18T00:16:37.6291986Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:37.6292100Z >>> lambda fut: fut.wait() + z 2024-12-18T00:16:37.6292200Z >>> ) 2024-12-18T00:16:37.6292287Z >>> 2024-12-18T00:16:37.6292395Z >>> @classmethod 2024-12-18T00:16:37.6292511Z >>> @rpc.functions.async_execution 2024-12-18T00:16:37.6292646Z >>> def class_async_add(cls, to, x, y, z): 2024-12-18T00:16:37.6292768Z >>> ret_fut = torch.futures.Future() 2024-12-18T00:16:37.6292918Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:37.6293085Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-12-18T00:16:37.6293171Z >>> ) 2024-12-18T00:16:37.6293282Z >>> return ret_fut 2024-12-18T00:16:37.6293372Z >>> 2024-12-18T00:16:37.6293488Z >>> @rpc.functions.async_execution 2024-12-18T00:16:37.6293627Z >>> def bound_async_add(self, to, x, y, z): 2024-12-18T00:16:37.6293797Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:37.6293923Z >>> lambda fut: fut.wait() + z 2024-12-18T00:16:37.6294011Z >>> ) 2024-12-18T00:16:37.6294112Z >>> 2024-12-18T00:16:37.6294201Z >>> # On worker0 2024-12-18T00:16:37.6294300Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:37.6294404Z >>> "worker1", 2024-12-18T00:16:37.6294534Z >>> AsyncExecutionClass.static_async_add, 2024-12-18T00:16:37.6294699Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T00:16:37.6294818Z >>> ) 2024-12-18T00:16:37.6294980Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:37.6295119Z >>> 2024-12-18T00:16:37.6295244Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:37.6295405Z >>> "worker1", 2024-12-18T00:16:37.6295573Z >>> AsyncExecutionClass.class_async_add, 2024-12-18T00:16:37.6295691Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T00:16:37.6295791Z >>> ) 2024-12-18T00:16:37.6295910Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:37.6295914Z 2024-12-18T00:16:37.6296124Z This decorator also works with RRef helpers, i.e., . 2024-12-18T00:16:37.6296303Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-12-18T00:16:37.6296473Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-12-18T00:16:37.6296611Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-12-18T00:16:37.6296616Z 2024-12-18T00:16:37.6296734Z >>> from torch.distributed import rpc 2024-12-18T00:16:37.6296837Z >>> 2024-12-18T00:16:37.6296994Z >>> # reuse the AsyncExecutionClass class above 2024-12-18T00:16:37.6297162Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T00:16:37.6297376Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-12-18T00:16:37.6297513Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:37.6297599Z >>> 2024-12-18T00:16:37.6297750Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T00:16:37.6297997Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-12-18T00:16:37.6298116Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:37.6298222Z >>> 2024-12-18T00:16:37.6298371Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T00:16:37.6298615Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-12-18T00:16:37.6298729Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:37.6298734Z 2024-12-18T00:16:37.6298987Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.6299003Z 2024-12-18T00:16:37.6313570Z 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. 2024-12-18T00:16:37.6313855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.6313860Z 2024-12-18T00:16:37.6314062Z Set device mapping between each RPC caller and callee pair. This 2024-12-18T00:16:37.6314244Z function can be called multiple times to incrementally add 2024-12-18T00:16:37.6314373Z device placement configurations. 2024-12-18T00:16:37.6314378Z 2024-12-18T00:16:37.6314463Z Args: 2024-12-18T00:16:37.6314576Z to (str): Callee name. 2024-12-18T00:16:37.6314774Z device_map (Dict of int, str, or torch.device): Device placement 2024-12-18T00:16:37.6314967Z mappings from this worker to the callee. This map must be 2024-12-18T00:16:37.6315071Z invertible. 2024-12-18T00:16:37.6315076Z 2024-12-18T00:16:37.6315177Z Example: 2024-12-18T00:16:37.6315291Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.6315386Z >>> # both workers 2024-12-18T00:16:37.6315495Z >>> def add(x, y): 2024-12-18T00:16:37.6315633Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-12-18T00:16:37.6315750Z >>> return x + y, (x + y).to(2) 2024-12-18T00:16:37.6315836Z >>> 2024-12-18T00:16:37.6315927Z >>> # on worker 0 2024-12-18T00:16:37.6316075Z >>> options = TensorPipeRpcBackendOptions( 2024-12-18T00:16:37.6316178Z >>> num_worker_threads=8, 2024-12-18T00:16:37.6316305Z >>> device_maps={"worker1": {0: 1}} 2024-12-18T00:16:37.6316437Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-12-18T00:16:37.6316537Z >>> ) 2024-12-18T00:16:37.6316667Z >>> options.set_device_map("worker1", {1: 2}) 2024-12-18T00:16:37.6316793Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-12-18T00:16:37.6316890Z >>> 2024-12-18T00:16:37.6316984Z >>> rpc.init_rpc( 2024-12-18T00:16:37.6317087Z >>> "worker0", 2024-12-18T00:16:37.6317176Z >>> rank=0, 2024-12-18T00:16:37.6317331Z >>> world_size=2, 2024-12-18T00:16:37.6317507Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-12-18T00:16:37.6317617Z >>> rpc_backend_options=options 2024-12-18T00:16:37.6317722Z >>> ) 2024-12-18T00:16:37.6317809Z >>> 2024-12-18T00:16:37.6317905Z >>> x = torch.ones(2) 2024-12-18T00:16:37.6318080Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-12-18T00:16:37.6318349Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-12-18T00:16:37.6318646Z >>> # sending the return value back, it will follow the invert of 2024-12-18T00:16:37.6318820Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-12-18T00:16:37.6318932Z >>> # cuda:1 on worker0 2024-12-18T00:16:37.6319080Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-12-18T00:16:37.6319225Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-12-18T00:16:37.6319229Z 2024-12-18T00:16:37.6319497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.6319506Z 2024-12-18T00:16:37.7257737Z 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=32. 2024-12-18T00:16:37.7258053Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.7258059Z 2024-12-18T00:16:37.7258357Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2024-12-18T00:16:37.7258632Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2024-12-18T00:16:37.7258913Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2024-12-18T00:16:37.7259073Z :class:`DTensor` according to the ``out_placements``. 2024-12-18T00:16:37.7259078Z 2024-12-18T00:16:37.7259167Z Args: 2024-12-18T00:16:37.7259386Z func (Callable): the function to be applied on each local shard of 2024-12-18T00:16:37.7259493Z :class:`DTensor` s. 2024-12-18T00:16:37.7259739Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-12-18T00:16:37.7259995Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2024-12-18T00:16:37.7260248Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-12-18T00:16:37.7260493Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-12-18T00:16:37.7260739Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-12-18T00:16:37.7260871Z mapping to the flattened ``output``. 2024-12-18T00:16:37.7261074Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-12-18T00:16:37.7261357Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2024-12-18T00:16:37.7261453Z should be `None`. 2024-12-18T00:16:37.7261700Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-12-18T00:16:37.7261926Z in. In this case, even if `out_placements` is not `None`, the result function 2024-12-18T00:16:37.7262181Z should ignore the desired placements because the function is not running with 2024-12-18T00:16:37.7262291Z :class:`DTensor` s. 2024-12-18T00:16:37.7262456Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-12-18T00:16:37.7262752Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2024-12-18T00:16:37.7262984Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2024-12-18T00:16:37.7263224Z placements of each :class:`DTensor` argument is the same as the required 2024-12-18T00:16:37.7263405Z placements or not. If the placements are not the same and 2024-12-18T00:16:37.7263646Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-12-18T00:16:37.7263901Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2024-12-18T00:16:37.7264380Z the required sharding placements before passing its local tensor to ``func``. 2024-12-18T00:16:37.7264618Z The only exception is when required placements are not ``None`` and the 2024-12-18T00:16:37.7264856Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-12-18T00:16:37.7265179Z will be skipped and the argument will be directly passed to ``func``. 2024-12-18T00:16:37.7265402Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-12-18T00:16:37.7265498Z Default: None 2024-12-18T00:16:37.7265651Z device_mesh (:class:`DeviceMesh`, optional): 2024-12-18T00:16:37.7265863Z the device mesh that all the :class:`DTensor` s are placed on. If not 2024-12-18T00:16:37.7266114Z specified, this will be inferred from the input :class:`DTensor` s' device 2024-12-18T00:16:37.7266349Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2024-12-18T00:16:37.7266476Z device mesh. Default: None. 2024-12-18T00:16:37.7266596Z redistribute_inputs (bool, optional): 2024-12-18T00:16:37.7266850Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2024-12-18T00:16:37.7267111Z their placements are different from the required input placements. If this 2024-12-18T00:16:37.7267335Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-12-18T00:16:37.7267486Z an exception will be raised. Default: False. 2024-12-18T00:16:37.7267491Z 2024-12-18T00:16:37.7267580Z Returns: 2024-12-18T00:16:37.7267846Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-12-18T00:16:37.7268085Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-12-18T00:16:37.7268089Z 2024-12-18T00:16:37.7268189Z Raises: 2024-12-18T00:16:37.7268437Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2024-12-18T00:16:37.7268739Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-12-18T00:16:37.7268859Z argument passed in. 2024-12-18T00:16:37.7268863Z 2024-12-18T00:16:37.7269128Z AssertionError: For any non-DTensor output, we require its corresponding 2024-12-18T00:16:37.7269541Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-12-18T00:16:37.7269735Z if this is not the case. 2024-12-18T00:16:37.7269742Z 2024-12-18T00:16:37.7270254Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-12-18T00:16:37.7270409Z a redistribution according to ``in_placements``. 2024-12-18T00:16:37.7270413Z 2024-12-18T00:16:37.7270516Z Example: 2024-12-18T00:16:37.7270631Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.7270771Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-12-18T00:16:37.7270912Z >>> partial_sum_tensor = torch.mm(W, X) 2024-12-18T00:16:37.7271159Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-12-18T00:16:37.7271281Z >>> return reduced_tensor 2024-12-18T00:16:37.7271369Z >>> 2024-12-18T00:16:37.7271497Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-12-18T00:16:37.7271640Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-12-18T00:16:37.7271740Z >>> Y = torch.mm(W, X) 2024-12-18T00:16:37.7271946Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-12-18T00:16:37.7272134Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-12-18T00:16:37.7272237Z >>> 2024-12-18T00:16:37.7272507Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-12-18T00:16:37.7272634Z >>> local_mm_allreduce_forward = local_map( 2024-12-18T00:16:37.7272754Z >>> mm_allreduce_forward, 2024-12-18T00:16:37.7272917Z >>> out_placements=[Replicate()], 2024-12-18T00:16:37.7273087Z >>> in_placements=[col_wise, row_wise], 2024-12-18T00:16:37.7273194Z >>> device_mesh=device_mesh, 2024-12-18T00:16:37.7273282Z >>> ) 2024-12-18T00:16:37.7273387Z >>> 2024-12-18T00:16:37.7273859Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-12-18T00:16:37.7274284Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-12-18T00:16:37.7274619Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-12-18T00:16:37.7274625Z 2024-12-18T00:16:37.7274857Z .. note:: This API is currently experimental and subject to change 2024-12-18T00:16:37.7274862Z 2024-12-18T00:16:37.7275118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.7275123Z 2024-12-18T00:16:37.7275887Z 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=25. 2024-12-18T00:16:37.7276155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.7276160Z 2024-12-18T00:16:37.7276451Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2024-12-18T00:16:37.7276693Z strategies for an operator when the tensor inputs and outputs are DTensor. 2024-12-18T00:16:37.7276959Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-12-18T00:16:37.7277203Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-12-18T00:16:37.7277478Z when users would like to overwrite default sharding strategies of existing operators. 2024-12-18T00:16:37.7277495Z 2024-12-18T00:16:37.7277581Z Args: 2024-12-18T00:16:37.7277710Z op (Union[OpOverload, List[OpOverload]]): 2024-12-18T00:16:37.7277921Z An op or a list of ops to register the customized sharding function. 2024-12-18T00:16:37.7277927Z 2024-12-18T00:16:37.7278017Z Returns: 2024-12-18T00:16:37.7278297Z A function decorator which can be used to wrap a function that defines the sharding 2024-12-18T00:16:37.7278625Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-12-18T00:16:37.7278916Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-12-18T00:16:37.7279218Z already implemented the operator. The customized sharding function takes the same inputs 2024-12-18T00:16:37.7279459Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-12-18T00:16:37.7279739Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-12-18T00:16:37.7280009Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-12-18T00:16:37.7280138Z corresponding intput placements. 2024-12-18T00:16:37.7280147Z 2024-12-18T00:16:37.7280234Z Example: 2024-12-18T00:16:37.7280365Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:37.7280496Z >>> @register_sharding(aten._softmax.default) 2024-12-18T00:16:37.7280652Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-12-18T00:16:37.7280806Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-12-18T00:16:37.7280920Z >>> acceptable_shardings = [] 2024-12-18T00:16:37.7281020Z >>> 2024-12-18T00:16:37.7281198Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-12-18T00:16:37.7281350Z >>> acceptable_shardings.append(all_replicate) 2024-12-18T00:16:37.7281437Z >>> 2024-12-18T00:16:37.7281555Z >>> for sharding_dim in range(x.ndim): 2024-12-18T00:16:37.7281683Z >>> if sharding_dim != softmax_dim: 2024-12-18T00:16:37.7281783Z >>> all_sharded = ( 2024-12-18T00:16:37.7281906Z >>> [Shard(sharding_dim)], 2024-12-18T00:16:37.7282083Z >>> [Shard(sharding_dim), None, None], 2024-12-18T00:16:37.7282208Z >>> ) 2024-12-18T00:16:37.7282360Z >>> acceptable_shardings.append(all_sharded) 2024-12-18T00:16:37.7282447Z >>> 2024-12-18T00:16:37.7282571Z >>> return acceptable_shardings 2024-12-18T00:16:37.7282576Z 2024-12-18T00:16:37.7282804Z .. note:: This API is currently experimental and subject to change 2024-12-18T00:16:37.7282836Z 2024-12-18T00:16:37.7283103Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.7283108Z 2024-12-18T00:16:37.7471961Z 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=378. 2024-12-18T00:16:37.7472245Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.7472251Z 2024-12-18T00:16:37.7472636Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-12-18T00:16:37.7473002Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-12-18T00:16:37.7473007Z 2024-12-18T00:16:37.7473101Z Keyword Args: 2024-12-18T00:16:37.7473314Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T00:16:37.7473820Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-12-18T00:16:37.7474186Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-12-18T00:16:37.7474320Z as a placeholder. default: None. 2024-12-18T00:16:37.7474550Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T00:16:37.7474941Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T00:16:37.7475333Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-12-18T00:16:37.7475486Z input_kwarg_layouts (Dict[str, Placement]): 2024-12-18T00:16:37.7475860Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-12-18T00:16:37.7475965Z default: None 2024-12-18T00:16:37.7476129Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-12-18T00:16:37.7476523Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T00:16:37.7476757Z have the desired DTensor layouts. default: None. 2024-12-18T00:16:37.7476931Z use_local_output (bool, optional): 2024-12-18T00:16:37.7477642Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-12-18T00:16:37.7477734Z Returns: 2024-12-18T00:16:37.7478064Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-12-18T00:16:37.7478074Z 2024-12-18T00:16:37.7478176Z Example:: 2024-12-18T00:16:37.7478283Z >>> # xdoctest: +SKIP(failing) 2024-12-18T00:16:37.7478653Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-12-18T00:16:37.7478849Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T00:16:37.7478952Z >>> ... 2024-12-18T00:16:37.7479262Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T00:16:37.7479404Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T00:16:37.7479493Z >>> 2024-12-18T00:16:37.7479828Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-12-18T00:16:37.7479993Z >>> # and then redistributed to Replicated DTensor. 2024-12-18T00:16:37.7480097Z >>> parallelize_module( 2024-12-18T00:16:37.7480375Z >>> block, # this can be a submodule or module 2024-12-18T00:16:37.7480544Z >>> tp_mesh, 2024-12-18T00:16:37.7480649Z >>> parallelize_plan={ 2024-12-18T00:16:37.7480784Z >>> "attn": PrepareModuleInput( 2024-12-18T00:16:37.7480922Z >>> input_layouts=(Shard(0), None, None, ...), 2024-12-18T00:16:37.7481149Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-12-18T00:16:37.7481280Z >>> ), 2024-12-18T00:16:37.7481382Z >>> } 2024-12-18T00:16:37.7481471Z >>> ) 2024-12-18T00:16:37.7481476Z 2024-12-18T00:16:37.7481729Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.7481747Z 2024-12-18T00:16:37.7482423Z 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=533. 2024-12-18T00:16:37.7482698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.7482705Z 2024-12-18T00:16:37.7483096Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-12-18T00:16:37.7483426Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-12-18T00:16:37.7483443Z 2024-12-18T00:16:37.7483534Z Keyword Args: 2024-12-18T00:16:37.7483704Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T00:16:37.7484055Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-12-18T00:16:37.7484435Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-12-18T00:16:37.7484590Z ``None`` need to be specified as a placeholder. 2024-12-18T00:16:37.7484786Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T00:16:37.7485187Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-12-18T00:16:37.7485307Z have the desired DTensor layouts. 2024-12-18T00:16:37.7485423Z use_local_output (bool, optional): 2024-12-18T00:16:37.7485795Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-12-18T00:16:37.7485884Z Returns: 2024-12-18T00:16:37.7486191Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-12-18T00:16:37.7486196Z 2024-12-18T00:16:37.7486289Z Example:: 2024-12-18T00:16:37.7486408Z >>> # xdoctest: +SKIP(failing) 2024-12-18T00:16:37.7486720Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-12-18T00:16:37.7486911Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T00:16:37.7487009Z >>> ... 2024-12-18T00:16:37.7487316Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T00:16:37.7487460Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T00:16:37.7487547Z >>> 2024-12-18T00:16:37.7487967Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-12-18T00:16:37.7488105Z >>> # and then redistributed to Sharded DTensor. 2024-12-18T00:16:37.7488208Z >>> parallelize_module( 2024-12-18T00:16:37.7488357Z >>> block, # this can be a submodule or module 2024-12-18T00:16:37.7488448Z >>> tp_mesh, 2024-12-18T00:16:37.7488596Z >>> parallelize_plan = PrepareModuleOutput( 2024-12-18T00:16:37.7488714Z >>> output_layouts=Replicate(), 2024-12-18T00:16:37.7488848Z >>> desired_output_layouts=Shard(0) 2024-12-18T00:16:37.7488936Z >>> ) 2024-12-18T00:16:37.7489029Z >>> ) 2024-12-18T00:16:37.7489033Z 2024-12-18T00:16:37.7489295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.7489330Z 2024-12-18T00:16:37.8014962Z 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. 2024-12-18T00:16:37.8015330Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.8015336Z 2024-12-18T00:16:37.8015695Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-12-18T00:16:37.8016002Z distribution where all component are from different parameterizations of 2024-12-18T00:16:37.8016223Z the same distribution type. It is parameterized by a `Categorical` 2024-12-18T00:16:37.8016437Z "selecting distribution" (over `k` component) and a component 2024-12-18T00:16:37.8016643Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-12-18T00:16:37.8016804Z (equal to `[k]`) which indexes each (batch of) component. 2024-12-18T00:16:37.8016822Z 2024-12-18T00:16:37.8016924Z Examples:: 2024-12-18T00:16:37.8016929Z 2024-12-18T00:16:37.8017051Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:37.8017270Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-12-18T00:16:37.8017386Z >>> # weighted normal distributions 2024-12-18T00:16:37.8017516Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T00:16:37.8017663Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-12-18T00:16:37.8017787Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T00:16:37.8017806Z 2024-12-18T00:16:37.8018005Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-12-18T00:16:37.8018135Z >>> # weighted bivariate normal distributions 2024-12-18T00:16:37.8018281Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T00:16:37.8018392Z >>> comp = D.Independent(D.Normal( 2024-12-18T00:16:37.8018533Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-12-18T00:16:37.8018654Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T00:16:37.8018658Z 2024-12-18T00:16:37.8018853Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-12-18T00:16:37.8019056Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-12-18T00:16:37.8019174Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-12-18T00:16:37.8019299Z >>> comp = D.Independent(D.Normal( 2024-12-18T00:16:37.8019437Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-12-18T00:16:37.8019571Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T00:16:37.8019576Z 2024-12-18T00:16:37.8019662Z Args: 2024-12-18T00:16:37.8019877Z mixture_distribution: `torch.distributions.Categorical`-like 2024-12-18T00:16:37.8020064Z instance. Manages the probability of selecting component. 2024-12-18T00:16:37.8020245Z The number of categories must match the rightmost batch 2024-12-18T00:16:37.8020446Z dimension of the `component_distribution`. Must have either 2024-12-18T00:16:37.8020588Z scalar `batch_shape` or `batch_shape` matching 2024-12-18T00:16:37.8020740Z `component_distribution.batch_shape[:-1]` 2024-12-18T00:16:37.8020960Z component_distribution: `torch.distributions.Distribution`-like 2024-12-18T00:16:37.8021152Z instance. Right-most batch dimension indexes component. 2024-12-18T00:16:37.8021156Z 2024-12-18T00:16:37.8021407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.8021412Z 2024-12-18T00:16:37.8121606Z 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. 2024-12-18T00:16:37.8121886Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.8121891Z 2024-12-18T00:16:37.8122092Z Creates a RelaxedBernoulli distribution, parametrized by 2024-12-18T00:16:37.8122287Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-12-18T00:16:37.8122505Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-12-18T00:16:37.8122859Z so the values are in (0, 1), and has reparametrizable samples. 2024-12-18T00:16:37.8122863Z 2024-12-18T00:16:37.8122961Z Example:: 2024-12-18T00:16:37.8122966Z 2024-12-18T00:16:37.8123125Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:37.8123257Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-12-18T00:16:37.8123447Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-12-18T00:16:37.8123585Z >>> m.sample() 2024-12-18T00:16:37.8123703Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-12-18T00:16:37.8123721Z 2024-12-18T00:16:37.8123809Z Args: 2024-12-18T00:16:37.8123947Z temperature (Tensor): relaxation temperature 2024-12-18T00:16:37.8124135Z probs (Number, Tensor): the probability of sampling `1` 2024-12-18T00:16:37.8124296Z logits (Number, Tensor): the log-odds of sampling `1` 2024-12-18T00:16:37.8124300Z 2024-12-18T00:16:37.8124568Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.8124577Z 2024-12-18T00:16:37.8135071Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-12-18T00:16:37.8135350Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:37.8135354Z 2024-12-18T00:16:37.8135574Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-12-18T00:16:37.8135787Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-12-18T00:16:37.8136027Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-12-18T00:16:37.8136193Z its samples are on simplex, and are reparametrizable. 2024-12-18T00:16:37.8136197Z 2024-12-18T00:16:37.8136307Z Example:: 2024-12-18T00:16:37.8136312Z 2024-12-18T00:16:37.8136459Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:37.8136633Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-12-18T00:16:37.8136768Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-12-18T00:16:37.8136879Z >>> m.sample() 2024-12-18T00:16:37.8136999Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-12-18T00:16:37.8137003Z 2024-12-18T00:16:37.8137104Z Args: 2024-12-18T00:16:37.8137242Z temperature (Tensor): relaxation temperature 2024-12-18T00:16:37.8137362Z probs (Tensor): event probabilities 2024-12-18T00:16:37.8137561Z logits (Tensor): unnormalized log probability for each event 2024-12-18T00:16:37.8137565Z 2024-12-18T00:16:37.8137818Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:37.8137822Z 2024-12-18T00:16:38.2738157Z 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. 2024-12-18T00:16:38.2739340Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.2739948Z Return a new dict with new, potentially nested, key value pair 2024-12-18T00:16:38.2740473Z 2024-12-18T00:16:38.2740846Z >>> purchase = { 2024-12-18T00:16:38.2741153Z ... "name": "Alice", 2024-12-18T00:16:38.2741518Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T00:16:38.2741978Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T00:16:38.2742313Z ... } 2024-12-18T00:16:38.2742663Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2024-12-18T00:16:38.2743106Z {'credit card': '5555-1234-1234-1234', 2024-12-18T00:16:38.2743535Z 'name': 'Alice', 2024-12-18T00:16:38.2744144Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-12-18T00:16:38.2744623Z 2024-12-18T00:16:38.2745000Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.2745370Z 2024-12-18T00:16:38.2746045Z 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. 2024-12-18T00:16:38.2747377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.2747905Z Update value in a (potentially) nested dictionary 2024-12-18T00:16:38.2748187Z 2024-12-18T00:16:38.2748281Z inputs: 2024-12-18T00:16:38.2748594Z d - dictionary on which to operate 2024-12-18T00:16:38.2749107Z keys - list or tuple giving the location of the value to be changed in d 2024-12-18T00:16:38.2749586Z func - function to operate on that value 2024-12-18T00:16:38.2749821Z 2024-12-18T00:16:38.2750029Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-12-18T00:16:38.2750572Z original dictionary with v replaced by func(v), but does not mutate the 2024-12-18T00:16:38.2751033Z original dictionary. 2024-12-18T00:16:38.2751216Z 2024-12-18T00:16:38.2751426Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-12-18T00:16:38.2751977Z specified by the keys, with the innermost value set to func(default). 2024-12-18T00:16:38.2752310Z 2024-12-18T00:16:38.2752426Z >>> inc = lambda x: x + 1 2024-12-18T00:16:38.2752734Z >>> update_in({"a": 0}, ["a"], inc) 2024-12-18T00:16:38.2753033Z {'a': 1} 2024-12-18T00:16:38.2753176Z 2024-12-18T00:16:38.2753277Z >>> transaction = { 2024-12-18T00:16:38.2753547Z ... "name": "Alice", 2024-12-18T00:16:38.2753923Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T00:16:38.2754360Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T00:16:38.2754670Z ... } 2024-12-18T00:16:38.2755019Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2024-12-18T00:16:38.2755472Z {'credit card': '5555-1234-1234-1234', 2024-12-18T00:16:38.2755794Z 'name': 'Alice', 2024-12-18T00:16:38.2756122Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-12-18T00:16:38.2756405Z 2024-12-18T00:16:38.2756530Z >>> # updating a value when k0 is not in d 2024-12-18T00:16:38.2756898Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-12-18T00:16:38.2757237Z {1: {2: {3: 'bar'}}} 2024-12-18T00:16:38.2757524Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2024-12-18T00:16:38.2757853Z {1: 'foo', 2: {3: {4: 1}}} 2024-12-18T00:16:38.2758113Z 2024-12-18T00:16:38.2758590Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.2758977Z 2024-12-18T00:16:38.2759615Z 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. 2024-12-18T00:16:38.2760635Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.2761185Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-12-18T00:16:38.2761460Z 2024-12-18T00:16:38.2761657Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-12-18T00:16:38.2762176Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-12-18T00:16:38.2762491Z 2024-12-18T00:16:38.2762700Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-12-18T00:16:38.2763166Z structures such as dictionaries and lists. 2024-12-18T00:16:38.2772261Z 2024-12-18T00:16:38.2772393Z >>> transaction = { 2024-12-18T00:16:38.2772690Z ... "name": "Alice", 2024-12-18T00:16:38.2773078Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T00:16:38.2773532Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T00:16:38.2774110Z ... } 2024-12-18T00:16:38.2774375Z >>> get_in(["purchase", "items", 0], transaction) 2024-12-18T00:16:38.2774733Z 'Apple' 2024-12-18T00:16:38.2774984Z >>> get_in(["name"], transaction) 2024-12-18T00:16:38.2775296Z 'Alice' 2024-12-18T00:16:38.2775556Z >>> get_in(["purchase", "total"], transaction) 2024-12-18T00:16:38.2776076Z >>> get_in(["purchase", "items", "apple"], transaction) 2024-12-18T00:16:38.2776540Z >>> get_in(["purchase", "items", 10], transaction) 2024-12-18T00:16:38.2776938Z >>> get_in(["purchase", "total"], transaction, 0) 2024-12-18T00:16:38.2777287Z 0 2024-12-18T00:16:38.2777527Z >>> get_in(["y"], {}, no_default=True) 2024-12-18T00:16:38.2777906Z Traceback (most recent call last): 2024-12-18T00:16:38.2778287Z ... 2024-12-18T00:16:38.2778518Z KeyError: 'y' 2024-12-18T00:16:38.2778665Z 2024-12-18T00:16:38.2778771Z See Also: 2024-12-18T00:16:38.2779012Z itertoolz.get 2024-12-18T00:16:38.2779267Z operator.getitem 2024-12-18T00:16:38.2779535Z 2024-12-18T00:16:38.2779917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.2780292Z 2024-12-18T00:16:38.2780997Z 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. 2024-12-18T00:16:38.2782035Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.2782538Z Group a collection by a key function 2024-12-18T00:16:38.2782753Z 2024-12-18T00:16:38.2782924Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2024-12-18T00:16:38.2783342Z >>> groupby(len, names) # doctest: +SKIP 2024-12-18T00:16:38.2783749Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-12-18T00:16:38.2784040Z 2024-12-18T00:16:38.2784149Z >>> iseven = lambda x: x % 2 == 0 2024-12-18T00:16:38.2784534Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-12-18T00:16:38.2784938Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-12-18T00:16:38.2785158Z 2024-12-18T00:16:38.2785302Z Non-callable keys imply grouping on a member. 2024-12-18T00:16:38.2785565Z 2024-12-18T00:16:38.2785658Z >>> groupby( 2024-12-18T00:16:38.2785902Z ... "gender", 2024-12-18T00:16:38.2786155Z ... [ 2024-12-18T00:16:38.2786417Z ... {"name": "Alice", "gender": "F"}, 2024-12-18T00:16:38.2786761Z ... {"name": "Bob", "gender": "M"}, 2024-12-18T00:16:38.2787118Z ... {"name": "Charlie", "gender": "M"}, 2024-12-18T00:16:38.2787449Z ... ], 2024-12-18T00:16:38.2787697Z ... ) # doctest:+SKIP 2024-12-18T00:16:38.2787998Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-12-18T00:16:38.2788330Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-12-18T00:16:38.2788671Z {'gender': 'M', 'name': 'Charlie'}]} 2024-12-18T00:16:38.2788906Z 2024-12-18T00:16:38.2789047Z Not to be confused with ``itertools.groupby`` 2024-12-18T00:16:38.2789309Z 2024-12-18T00:16:38.2789400Z See Also: 2024-12-18T00:16:38.2789635Z countby 2024-12-18T00:16:38.2789871Z 2024-12-18T00:16:38.2790235Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.2790620Z 2024-12-18T00:16:38.5895712Z 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. 2024-12-18T00:16:38.5896695Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.5897263Z Applies Batch Normalization over a N-Dimensional input. 2024-12-18T00:16:38.5897568Z 2024-12-18T00:16:38.5897936Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-12-18T00:16:38.5898661Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-12-18T00:16:38.5899309Z Internal Covariate Shift `__ . 2024-12-18T00:16:38.5899919Z 2024-12-18T00:16:38.5900118Z .. math:: 2024-12-18T00:16:38.5900302Z 2024-12-18T00:16:38.5900544Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-12-18T00:16:38.5900888Z 2024-12-18T00:16:38.5901137Z The mean and standard-deviation are calculated per-dimension over all 2024-12-18T00:16:38.5902038Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-12-18T00:16:38.5902628Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-12-18T00:16:38.5903180Z By default, the elements of :math:`\gamma` are sampled from 2024-12-18T00:16:38.5903740Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-12-18T00:16:38.5904378Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-12-18T00:16:38.5904890Z `torch.var(input, unbiased=False)`. 2024-12-18T00:16:38.5905111Z 2024-12-18T00:16:38.5905350Z Also by default, during training this layer keeps running estimates of its 2024-12-18T00:16:38.5905951Z computed mean and variance, which are then used for normalization during 2024-12-18T00:16:38.5906561Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-12-18T00:16:38.5907027Z of 0.1. 2024-12-18T00:16:38.5907156Z 2024-12-18T00:16:38.5907402Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-12-18T00:16:38.5907983Z keep running estimates, and batch statistics are instead used during 2024-12-18T00:16:38.5908423Z evaluation time as well. 2024-12-18T00:16:38.5908624Z 2024-12-18T00:16:38.5908720Z .. note:: 2024-12-18T00:16:38.5909093Z This :attr:`momentum` argument is different from one used in optimizer 2024-12-18T00:16:38.5909675Z classes and the conventional notion of momentum. Mathematically, the 2024-12-18T00:16:38.5910164Z update rule for running statistics here is 2024-12-18T00:16:38.5910691Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-12-18T00:16:38.5911277Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-12-18T00:16:38.5911713Z new observed value. 2024-12-18T00:16:38.5911907Z 2024-12-18T00:16:38.5912212Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-12-18T00:16:38.5912893Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-12-18T00:16:38.5913449Z Normalization or Spatio-temporal Batch Normalization. 2024-12-18T00:16:38.5913734Z 2024-12-18T00:16:38.5913897Z Currently :class:`SyncBatchNorm` only supports 2024-12-18T00:16:38.5914435Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-12-18T00:16:38.5915064Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-12-18T00:16:38.5915615Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-12-18T00:16:38.5916045Z Network with DDP. 2024-12-18T00:16:38.5916209Z 2024-12-18T00:16:38.5916312Z Args: 2024-12-18T00:16:38.5916600Z num_features: :math:`C` from an expected input of size 2024-12-18T00:16:38.5916987Z :math:`(N, C, +)` 2024-12-18T00:16:38.5917373Z eps: a value added to the denominator for numerical stability. 2024-12-18T00:16:38.5917790Z Default: ``1e-5`` 2024-12-18T00:16:38.5918182Z momentum: the value used for the running_mean and running_var 2024-12-18T00:16:38.5918818Z computation. Can be set to ``None`` for cumulative moving average 2024-12-18T00:16:38.5919270Z (i.e. simple average). Default: 0.1 2024-12-18T00:16:38.5919723Z affine: a boolean value that when set to ``True``, this module has 2024-12-18T00:16:38.5920210Z learnable affine parameters. Default: ``True`` 2024-12-18T00:16:38.5920703Z track_running_stats: a boolean value that when set to ``True``, this 2024-12-18T00:16:38.5921280Z module tracks the running mean and variance, and when set to ``False``, 2024-12-18T00:16:38.5921851Z this module does not track such statistics, and initializes statistics 2024-12-18T00:16:38.5922412Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-12-18T00:16:38.5923066Z When these buffers are ``None``, this module always uses batch statistics. 2024-12-18T00:16:38.5923575Z in both training and eval modes. Default: ``True`` 2024-12-18T00:16:38.5924104Z process_group: synchronization of stats happen within each process group 2024-12-18T00:16:38.5924738Z individually. Default behavior is synchronization across the whole 2024-12-18T00:16:38.5925208Z world 2024-12-18T00:16:38.5925349Z 2024-12-18T00:16:38.5925455Z Shape: 2024-12-18T00:16:38.5925685Z - Input: :math:`(N, C, +)` 2024-12-18T00:16:38.5926043Z - Output: :math:`(N, C, +)` (same shape as input) 2024-12-18T00:16:38.5926311Z 2024-12-18T00:16:38.5926406Z .. note:: 2024-12-18T00:16:38.5926793Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-12-18T00:16:38.5927368Z synchronization is disabled when ``model.eval()`` is set or if 2024-12-18T00:16:38.5927827Z ``self.training`` is otherwise ``False``. 2024-12-18T00:16:38.5928068Z 2024-12-18T00:16:38.5928166Z Examples:: 2024-12-18T00:16:38.5928316Z 2024-12-18T00:16:38.5928420Z >>> # xdoctest: +SKIP 2024-12-18T00:16:38.5928726Z >>> # With Learnable Parameters 2024-12-18T00:16:38.5929065Z >>> m = nn.SyncBatchNorm(100) 2024-12-18T00:16:38.5929410Z >>> # creating process group (optional) 2024-12-18T00:16:38.5929789Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T00:16:38.5930154Z >>> ranks = list(range(8)) 2024-12-18T00:16:38.5930467Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T00:16:38.5930838Z >>> # Note: every rank calls into new_group for every 2024-12-18T00:16:38.5931258Z >>> # process group created, even if that rank is not 2024-12-18T00:16:38.5931625Z >>> # part of the group. 2024-12-18T00:16:38.5932071Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T00:16:38.5932652Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T00:16:38.5933103Z >>> # Without Learnable Parameters 2024-12-18T00:16:38.5933540Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-12-18T00:16:38.5933997Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-12-18T00:16:38.5934332Z >>> output = m(input) 2024-12-18T00:16:38.5934526Z 2024-12-18T00:16:38.5934647Z >>> # network is nn.BatchNorm layer 2024-12-18T00:16:38.5935156Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-12-18T00:16:38.5935728Z >>> # only single gpu per process is currently supported 2024-12-18T00:16:38.5936229Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-12-18T00:16:38.5936677Z >>> sync_bn_network, 2024-12-18T00:16:38.5937041Z >>> device_ids=[args.local_rank], 2024-12-18T00:16:38.5937426Z >>> output_device=args.local_rank) 2024-12-18T00:16:38.5937769Z 2024-12-18T00:16:38.5938141Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.5938507Z 2024-12-18T00:16:38.5939197Z 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=824. 2024-12-18T00:16:38.5940227Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.5941106Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-12-18T00:16:38.5941542Z 2024-12-18T00:16:38.5941632Z Args: 2024-12-18T00:16:38.5942018Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-12-18T00:16:38.5942619Z process_group (optional): process group to scope synchronization, 2024-12-18T00:16:38.5943135Z default is the whole world 2024-12-18T00:16:38.5943391Z 2024-12-18T00:16:38.5943497Z Returns: 2024-12-18T00:16:38.5943888Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-12-18T00:16:38.5944490Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-12-18T00:16:38.5945087Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-12-18T00:16:38.5945549Z instead. 2024-12-18T00:16:38.5945701Z 2024-12-18T00:16:38.5945813Z Example:: 2024-12-18T00:16:38.5945955Z 2024-12-18T00:16:38.5946089Z >>> # Network with nn.BatchNorm layer 2024-12-18T00:16:38.5946463Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:38.5946851Z >>> module = torch.nn.Sequential( 2024-12-18T00:16:38.5947211Z >>> torch.nn.Linear(20, 100), 2024-12-18T00:16:38.5947578Z >>> torch.nn.BatchNorm1d(100), 2024-12-18T00:16:38.5947925Z >>> ).cuda() 2024-12-18T00:16:38.5948235Z >>> # creating process group (optional) 2024-12-18T00:16:38.5948627Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T00:16:38.5949001Z >>> ranks = list(range(8)) 2024-12-18T00:16:38.5949329Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T00:16:38.5949709Z >>> # Note: every rank calls into new_group for every 2024-12-18T00:16:38.5950123Z >>> # process group created, even if that rank is not 2024-12-18T00:16:38.5950499Z >>> # part of the group. 2024-12-18T00:16:38.5950832Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:38.5951323Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T00:16:38.5951897Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T00:16:38.5952545Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-12-18T00:16:38.5952978Z 2024-12-18T00:16:38.5953068Z 2024-12-18T00:16:38.5953454Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.5953823Z 2024-12-18T00:16:38.6130502Z 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. 2024-12-18T00:16:38.6131454Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.6131840Z 2024-12-18T00:16:38.6132168Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-12-18T00:16:38.6132621Z 2024-12-18T00:16:38.6132910Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-12-18T00:16:38.6133531Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-12-18T00:16:38.6133890Z 2024-12-18T00:16:38.6134212Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-12-18T00:16:38.6134924Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-12-18T00:16:38.6135481Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-12-18T00:16:38.6135762Z 2024-12-18T00:16:38.6135866Z Shape: 2024-12-18T00:16:38.6136218Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-12-18T00:16:38.6136798Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-12-18T00:16:38.6137398Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-12-18T00:16:38.6137874Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-12-18T00:16:38.6138124Z 2024-12-18T00:16:38.6138210Z Args: 2024-12-18T00:16:38.6138494Z dim (Union[int, str]): Dimension to be unflattened 2024-12-18T00:16:38.6139109Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-12-18T00:16:38.6139807Z 2024-12-18T00:16:38.6139899Z Examples: 2024-12-18T00:16:38.6140156Z >>> input = torch.randn(2, 50) 2024-12-18T00:16:38.6140479Z >>> # With tuple of ints 2024-12-18T00:16:38.6140775Z >>> m = nn.Sequential( 2024-12-18T00:16:38.6141054Z >>> nn.Linear(50, 50), 2024-12-18T00:16:38.6141385Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-12-18T00:16:38.6141734Z >>> ) 2024-12-18T00:16:38.6141967Z >>> output = m(input) 2024-12-18T00:16:38.6142245Z >>> output.size() 2024-12-18T00:16:38.6142515Z torch.Size([2, 2, 5, 5]) 2024-12-18T00:16:38.6142787Z >>> # With torch.Size 2024-12-18T00:16:38.6143063Z >>> m = nn.Sequential( 2024-12-18T00:16:38.6143347Z >>> nn.Linear(50, 50), 2024-12-18T00:16:38.6143665Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-12-18T00:16:38.6143999Z >>> ) 2024-12-18T00:16:38.6144219Z >>> output = m(input) 2024-12-18T00:16:38.6144494Z >>> output.size() 2024-12-18T00:16:38.6144766Z torch.Size([2, 2, 5, 5]) 2024-12-18T00:16:38.6145075Z >>> # With namedshape (tuple of tuples) 2024-12-18T00:16:38.6145450Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-12-18T00:16:38.6145932Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-12-18T00:16:38.6146377Z >>> output = unflatten(input) 2024-12-18T00:16:38.6146689Z >>> output.size() 2024-12-18T00:16:38.6146960Z torch.Size([2, 2, 5, 5]) 2024-12-18T00:16:38.6147138Z 2024-12-18T00:16:38.6147406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.6147773Z 2024-12-18T00:16:38.6455481Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1698. 2024-12-18T00:16:38.6456543Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.6457119Z Creates a criterion that measures the triplet loss given input 2024-12-18T00:16:38.6457670Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-12-18T00:16:38.6458201Z positive, and negative examples, respectively), and a nonnegative, 2024-12-18T00:16:38.6458793Z real-valued function ("distance function") used to compute the relationship 2024-12-18T00:16:38.6459400Z between the anchor and positive example ("positive distance") and the 2024-12-18T00:16:38.6459902Z anchor and negative example ("negative distance"). 2024-12-18T00:16:38.6460165Z 2024-12-18T00:16:38.6460375Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-12-18T00:16:38.6460814Z can be described as: 2024-12-18T00:16:38.6460996Z 2024-12-18T00:16:38.6461102Z .. math:: 2024-12-18T00:16:38.6461383Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-12-18T00:16:38.6461805Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-12-18T00:16:38.6462071Z 2024-12-18T00:16:38.6462331Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-12-18T00:16:38.6462990Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-12-18T00:16:38.6463647Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-12-18T00:16:38.6464266Z between the positive and negative distances that is required for the loss to 2024-12-18T00:16:38.6464868Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-12-18T00:16:38.6465345Z that the distance function can handle. 2024-12-18T00:16:38.6465572Z 2024-12-18T00:16:38.6465703Z If :attr:`reduction` is not ``'none'`` 2024-12-18T00:16:38.6466033Z (default ``'mean'``), then: 2024-12-18T00:16:38.6466228Z 2024-12-18T00:16:38.6466321Z .. math:: 2024-12-18T00:16:38.6466554Z \ell(x, y) = 2024-12-18T00:16:38.6466811Z \begin{cases} 2024-12-18T00:16:38.6467175Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-12-18T00:16:38.6467858Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-12-18T00:16:38.6468272Z \end{cases} 2024-12-18T00:16:38.6468430Z 2024-12-18T00:16:38.6468667Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-12-18T00:16:38.6469324Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-12-18T00:16:38.6469739Z 2024-12-18T00:16:38.6469843Z Args: 2024-12-18T00:16:38.6470247Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-12-18T00:16:38.6470825Z quantifies the closeness of two tensors. If not specified, 2024-12-18T00:16:38.6471311Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-12-18T00:16:38.6471881Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-12-18T00:16:38.6472557Z between the positive and negative distances required for the loss to be 0. Larger 2024-12-18T00:16:38.6473242Z margins penalize cases where the negative examples are not distant enough from the 2024-12-18T00:16:38.6474019Z anchors, relative to the positives. Default: :math:`1`. 2024-12-18T00:16:38.6474557Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-12-18T00:16:38.6475204Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-12-18T00:16:38.6475828Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-12-18T00:16:38.6476459Z negative example than the anchor is, swaps the positive example and the anchor in 2024-12-18T00:16:38.6476991Z the loss computation. Default: ``False``. 2024-12-18T00:16:38.6477522Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-12-18T00:16:38.6478109Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-12-18T00:16:38.6478683Z ``'mean'``: the sum of the output will be divided by the number of 2024-12-18T00:16:38.6479246Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-12-18T00:16:38.6479621Z 2024-12-18T00:16:38.6479625Z 2024-12-18T00:16:38.6479718Z Shape: 2024-12-18T00:16:38.6480109Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-12-18T00:16:38.6480616Z as supported by the distance function. 2024-12-18T00:16:38.6481107Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-12-18T00:16:38.6481587Z otherwise. 2024-12-18T00:16:38.6481756Z 2024-12-18T00:16:38.6481858Z Examples:: 2024-12-18T00:16:38.6482012Z 2024-12-18T00:16:38.6482121Z >>> # Initialize embeddings 2024-12-18T00:16:38.6482443Z >>> embedding = nn.Embedding(1000, 128) 2024-12-18T00:16:38.6482812Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-12-18T00:16:38.6483180Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-12-18T00:16:38.6483562Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-12-18T00:16:38.6483929Z >>> anchor = embedding(anchor_ids) 2024-12-18T00:16:38.6484279Z >>> positive = embedding(positive_ids) 2024-12-18T00:16:38.6484640Z >>> negative = embedding(negative_ids) 2024-12-18T00:16:38.6484955Z >>> 2024-12-18T00:16:38.6485194Z >>> # Built-in Distance Function 2024-12-18T00:16:38.6485508Z >>> triplet_loss = \ 2024-12-18T00:16:38.6485969Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-12-18T00:16:38.6486531Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T00:16:38.6486902Z >>> output.backward() 2024-12-18T00:16:38.6487172Z >>> 2024-12-18T00:16:38.6487404Z >>> # Custom Distance Function 2024-12-18T00:16:38.6487716Z >>> def l_infinity(x1, x2): 2024-12-18T00:16:38.6488148Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-12-18T00:16:38.6488545Z >>> 2024-12-18T00:16:38.6488864Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-12-18T00:16:38.6489280Z >>> triplet_loss = ( 2024-12-18T00:16:38.6489737Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-12-18T00:16:38.6490339Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T00:16:38.6490746Z >>> output.backward() 2024-12-18T00:16:38.6491021Z >>> 2024-12-18T00:16:38.6491271Z >>> # Custom Distance Function (Lambda) 2024-12-18T00:16:38.6491606Z >>> triplet_loss = ( 2024-12-18T00:16:38.6491914Z >>> nn.TripletMarginWithDistanceLoss( 2024-12-18T00:16:38.6492373Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-12-18T00:16:38.6492868Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T00:16:38.6493243Z >>> output.backward() 2024-12-18T00:16:38.6493419Z 2024-12-18T00:16:38.6493528Z Reference: 2024-12-18T00:16:38.6493979Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-12-18T00:16:38.6494633Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2024-12-18T00:16:38.6495066Z 2024-12-18T00:16:38.6495450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-12-18T00:16:38.6495840Z 2024-12-18T00:16:38.7010847Z 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. 2024-12-18T00:16:38.7011770Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.7012341Z Computes a partial inverse of :class:`MaxPool2d`. 2024-12-18T00:16:38.7012611Z 2024-12-18T00:16:38.7012891Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-12-18T00:16:38.7013274Z 2024-12-18T00:16:38.7013519Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-12-18T00:16:38.7014122Z including the indices of the maximal values and computes a partial inverse 2024-12-18T00:16:38.7014624Z in which all non-maximal values are set to zero. 2024-12-18T00:16:38.7014894Z 2024-12-18T00:16:38.7014984Z Note: 2024-12-18T00:16:38.7015435Z This operation may behave nondeterministically when the input indices has repeat values. 2024-12-18T00:16:38.7016255Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-12-18T00:16:38.7016748Z 2024-12-18T00:16:38.7017005Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-12-18T00:16:38.7017531Z sizes. Hence, the inversion process can get ambiguous. 2024-12-18T00:16:38.7018006Z To accommodate this, you can provide the needed output size 2024-12-18T00:16:38.7018531Z as an additional argument :attr:`output_size` in the forward call. 2024-12-18T00:16:38.7018990Z See the Inputs and Example below. 2024-12-18T00:16:38.7019233Z 2024-12-18T00:16:38.7019323Z Args: 2024-12-18T00:16:38.7019635Z kernel_size (int or tuple): Size of the max pooling window. 2024-12-18T00:16:38.7020112Z stride (int or tuple): Stride of the max pooling window. 2024-12-18T00:16:38.7020535Z It is set to :attr:`kernel_size` by default. 2024-12-18T00:16:38.7020979Z padding (int or tuple): Padding that was added to the input 2024-12-18T00:16:38.7021286Z 2024-12-18T00:16:38.7021376Z Inputs: 2024-12-18T00:16:38.7021633Z - `input`: the input Tensor to invert 2024-12-18T00:16:38.7022078Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-12-18T00:16:38.7022549Z - `output_size` (optional): the targeted output size 2024-12-18T00:16:38.7022828Z 2024-12-18T00:16:38.7022919Z Shape: 2024-12-18T00:16:38.7023236Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-12-18T00:16:38.7024496Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-12-18T00:16:38.7024828Z 2024-12-18T00:16:38.7024936Z .. math:: 2024-12-18T00:16:38.7025362Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-12-18T00:16:38.7025747Z 2024-12-18T00:16:38.7025911Z .. math:: 2024-12-18T00:16:38.7026361Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-12-18T00:16:38.7026753Z 2024-12-18T00:16:38.7026920Z or as given by :attr:`output_size` in the call operator 2024-12-18T00:16:38.7027206Z 2024-12-18T00:16:38.7027301Z Example:: 2024-12-18T00:16:38.7027435Z 2024-12-18T00:16:38.7027611Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-12-18T00:16:38.7028019Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-12-18T00:16:38.7028390Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-12-18T00:16:38.7028763Z [ 5., 6., 7., 8.], 2024-12-18T00:16:38.7029104Z [ 9., 10., 11., 12.], 2024-12-18T00:16:38.7029451Z [13., 14., 15., 16.]]]]) 2024-12-18T00:16:38.7029803Z >>> output, indices = pool(input) 2024-12-18T00:16:38.7030145Z >>> unpool(output, indices) 2024-12-18T00:16:38.7030451Z tensor([[[[ 0., 0., 0., 0.], 2024-12-18T00:16:38.7030769Z [ 0., 6., 0., 8.], 2024-12-18T00:16:38.7031080Z [ 0., 0., 0., 0.], 2024-12-18T00:16:38.7031398Z [ 0., 14., 0., 16.]]]]) 2024-12-18T00:16:38.7031826Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-12-18T00:16:38.7032308Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-12-18T00:16:38.7032686Z [ 6., 7., 8., 9., 10.], 2024-12-18T00:16:38.7033044Z [11., 12., 13., 14., 15.], 2024-12-18T00:16:38.7033399Z [16., 17., 18., 19., 20.]]]]) 2024-12-18T00:16:38.7033757Z >>> output, indices = pool(input) 2024-12-18T00:16:38.7034148Z >>> # This call will not work without specifying output_size 2024-12-18T00:16:38.7034614Z >>> unpool(output, indices, output_size=input.size()) 2024-12-18T00:16:38.7035008Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-12-18T00:16:38.7035339Z [ 0., 7., 0., 9., 0.], 2024-12-18T00:16:38.7035657Z [ 0., 0., 0., 0., 0.], 2024-12-18T00:16:38.7035982Z [ 0., 17., 0., 19., 0.]]]]) 2024-12-18T00:16:38.7036198Z 2024-12-18T00:16:38.7036202Z 2024-12-18T00:16:38.7036290Z 2024-12-18T00:16:38.7036678Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.7037063Z 2024-12-18T00:16:38.7286318Z 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. 2024-12-18T00:16:38.7287249Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.7287953Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-12-18T00:16:38.7288387Z 2024-12-18T00:16:38.7288730Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-12-18T00:16:38.7289289Z and with 2D inputs, this class 2024-12-18T00:16:38.7289491Z 2024-12-18T00:16:38.7289801Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-12-18T00:16:38.7290546Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-12-18T00:16:38.7291287Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-12-18T00:16:38.7291907Z 2024-12-18T00:16:38.7292261Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-12-18T00:16:38.7292836Z operations. 2024-12-18T00:16:38.7292981Z 2024-12-18T00:16:38.7293252Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-12-18T00:16:38.7293953Z pass. This scales the output of the Embedding before performing a weighted 2024-12-18T00:16:38.7294555Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-12-18T00:16:38.7295171Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-12-18T00:16:38.7295646Z :attr:`per_sample_weights`. 2024-12-18T00:16:38.7295838Z 2024-12-18T00:16:38.7295944Z Args: 2024-12-18T00:16:38.7296257Z num_embeddings (int): size of the dictionary of embeddings 2024-12-18T00:16:38.7296717Z embedding_dim (int): the size of each embedding vector 2024-12-18T00:16:38.7297327Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-12-18T00:16:38.7297929Z is renormalized to have norm :attr:`max_norm`. 2024-12-18T00:16:38.7298558Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-12-18T00:16:38.7299383Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-12-18T00:16:38.7300007Z the words in the mini-batch. Default ``False``. 2024-12-18T00:16:38.7300488Z Note: this option is not supported when ``mode="max"``. 2024-12-18T00:16:38.7301050Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-12-18T00:16:38.7301638Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-12-18T00:16:38.7302217Z into consideration. ``"mean"`` computes the average of the values 2024-12-18T00:16:38.7302742Z in the bag, ``"max"`` computes the max value over each bag. 2024-12-18T00:16:38.7303165Z Default: ``"mean"`` 2024-12-18T00:16:38.7303728Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-12-18T00:16:38.7304432Z Notes for more details regarding sparse gradients. Note: this option is not 2024-12-18T00:16:38.7304936Z supported when ``mode="max"``. 2024-12-18T00:16:38.7305562Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-12-18T00:16:38.7306291Z is equivalent to the size of `indices`. This matches the CSR format. 2024-12-18T00:16:38.7306978Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-12-18T00:16:38.7307718Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-12-18T00:16:38.7308374Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-12-18T00:16:38.7309024Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-12-18T00:16:38.7309656Z zeros, but can be updated to another value to be used as the padding vector. 2024-12-18T00:16:38.7310279Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-12-18T00:16:38.7310765Z reduction. 2024-12-18T00:16:38.7310996Z 2024-12-18T00:16:38.7311125Z Attributes: 2024-12-18T00:16:38.7311619Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-12-18T00:16:38.7312206Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-12-18T00:16:38.7312463Z 2024-12-18T00:16:38.7312570Z Examples:: 2024-12-18T00:16:38.7312725Z 2024-12-18T00:16:38.7312925Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-12-18T00:16:38.7313405Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-12-18T00:16:38.7313810Z >>> # a batch of 2 samples of 4 indices each 2024-12-18T00:16:38.7314245Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T00:16:38.7314712Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T00:16:38.7315113Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:38.7315490Z >>> embedding_sum(input, offsets) 2024-12-18T00:16:38.7315839Z tensor([[-0.8861, -5.4350, -0.0523], 2024-12-18T00:16:38.7316168Z [ 1.1306, -2.5798, -1.0044]]) 2024-12-18T00:16:38.7316380Z 2024-12-18T00:16:38.7316503Z >>> # Example with padding_idx 2024-12-18T00:16:38.7316920Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-12-18T00:16:38.7317443Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T00:16:38.7317909Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T00:16:38.7318292Z >>> embedding_sum(input, offsets) 2024-12-18T00:16:38.7318724Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-12-18T00:16:38.7319051Z [-0.7082, 3.2145, -2.6251]]) 2024-12-18T00:16:38.7319264Z 2024-12-18T00:16:38.7319440Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-12-18T00:16:38.7319884Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-12-18T00:16:38.7320315Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-12-18T00:16:38.7320704Z embedding.weight, 2024-12-18T00:16:38.7321040Z padding_idx=embedding.padding_idx, 2024-12-18T00:16:38.7321379Z mode='sum') 2024-12-18T00:16:38.7321647Z 2024-12-18T00:16:38.7322029Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.7322401Z 2024-12-18T00:16:38.7615505Z 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=1748. 2024-12-18T00:16:38.7616540Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.7616917Z 2024-12-18T00:16:38.7617165Z Context manager for training with uneven inputs across processes in DDP. 2024-12-18T00:16:38.7617517Z 2024-12-18T00:16:38.7617754Z This context manager will keep track of already-joined DDP processes, 2024-12-18T00:16:38.7618295Z and "shadow" the forward and backward passes by inserting collective 2024-12-18T00:16:38.7618865Z communication operations to match with the ones created by non-joined 2024-12-18T00:16:38.7619454Z DDP processes. This will ensure each collective call has a corresponding 2024-12-18T00:16:38.7620032Z call by already-joined DDP processes, preventing hangs or errors that 2024-12-18T00:16:38.7620579Z would otherwise happen when training with uneven inputs across 2024-12-18T00:16:38.7621145Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-12-18T00:16:38.7621706Z specified to be ``True``, all trainers will throw an error once one rank 2024-12-18T00:16:38.7622242Z runs out of inputs, allowing these errors to be caught and handled 2024-12-18T00:16:38.7622682Z according to application logic. 2024-12-18T00:16:38.7622880Z 2024-12-18T00:16:38.7623116Z Once all DDP processes have joined, the context manager will broadcast 2024-12-18T00:16:38.7623686Z the model corresponding to the last joined process to all processes to 2024-12-18T00:16:38.7624318Z ensure the model is the same across all processes 2024-12-18T00:16:38.7624724Z (which is guaranteed by DDP). 2024-12-18T00:16:38.7624927Z 2024-12-18T00:16:38.7625134Z To use this to enable training with uneven inputs across processes, 2024-12-18T00:16:38.7625685Z simply wrap this context manager around your training loop. No further 2024-12-18T00:16:38.7626248Z modifications to the model or data loading is required. 2024-12-18T00:16:38.7626538Z 2024-12-18T00:16:38.7626700Z .. warning:: 2024-12-18T00:16:38.7627041Z If the model or training loop this context manager is wrapped around 2024-12-18T00:16:38.7627559Z has additional distributed collective operations, such as 2024-12-18T00:16:38.7628049Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-12-18T00:16:38.7628565Z ``throw_on_early_termination`` must be enabled. This is because this 2024-12-18T00:16:38.7629105Z context manager is not aware of non-DDP collective communication. 2024-12-18T00:16:38.7629609Z This flag will cause all ranks to throw when any one rank 2024-12-18T00:16:38.7630105Z exhausts inputs, allowing these errors to be caught and recovered 2024-12-18T00:16:38.7630539Z from across all ranks. 2024-12-18T00:16:38.7630725Z 2024-12-18T00:16:38.7630813Z Args: 2024-12-18T00:16:38.7631122Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-12-18T00:16:38.7631630Z gradients by the initial ``world_size`` DDP training was launched 2024-12-18T00:16:38.7632117Z with. If ``False``, will compute the effective world size 2024-12-18T00:16:38.7632589Z (number of ranks that have not depleted their inputs yet) and 2024-12-18T00:16:38.7633047Z divide gradients by that during allreduce. Set 2024-12-18T00:16:38.7633491Z ``divide_by_initial_world_size=True`` to ensure every input 2024-12-18T00:16:38.7634001Z sample including the uneven inputs have equal weight in terms of 2024-12-18T00:16:38.7634504Z how much they contribute to the global gradient. This is 2024-12-18T00:16:38.7634961Z achieved by always dividing the gradient by the initial 2024-12-18T00:16:38.7635449Z ``world_size`` even when we encounter uneven inputs. If you set 2024-12-18T00:16:38.7635933Z this to ``False``, we divide the gradient by the remaining 2024-12-18T00:16:38.7636440Z number of nodes. This ensures parity with training on a smaller 2024-12-18T00:16:38.7636951Z ``world_size`` although it also means the uneven inputs would 2024-12-18T00:16:38.7637452Z contribute more towards the global gradient. Typically, you 2024-12-18T00:16:38.7637957Z would want to set this to ``True`` for cases where the last few 2024-12-18T00:16:38.7638541Z inputs of your training job are uneven. In extreme cases, where 2024-12-18T00:16:38.7639054Z there is a large discrepancy in the number of inputs, setting 2024-12-18T00:16:38.7639505Z this to ``False`` might provide better results. 2024-12-18T00:16:38.7639980Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-12-18T00:16:38.7640496Z in ``enable=False`` to disable in cases where you know that 2024-12-18T00:16:38.7640970Z inputs are even across participating processes. Default is 2024-12-18T00:16:38.7641368Z ``True``. 2024-12-18T00:16:38.7641703Z throw_on_early_termination (bool): Whether to throw an error 2024-12-18T00:16:38.7642195Z or continue training when at least one rank has exhausted 2024-12-18T00:16:38.7642681Z inputs. If ``True``, will throw upon the first rank reaching end 2024-12-18T00:16:38.7643272Z of data. If ``False``, will continue training with a smaller 2024-12-18T00:16:38.7643858Z effective world size until all ranks are joined. Note that if 2024-12-18T00:16:38.7644306Z this flag is specified, then the flag 2024-12-18T00:16:38.7644722Z ``divide_by_initial_world_size`` would be ignored. Default 2024-12-18T00:16:38.7645116Z is ``False``. 2024-12-18T00:16:38.7645345Z 2024-12-18T00:16:38.7645402Z 2024-12-18T00:16:38.7645515Z Example:: 2024-12-18T00:16:38.7645640Z 2024-12-18T00:16:38.7645757Z >>> # xdoctest: +SKIP("Distributed") 2024-12-18T00:16:38.7646083Z >>> import torch 2024-12-18T00:16:38.7646369Z >>> import torch.distributed as dist 2024-12-18T00:16:38.7646697Z >>> import os 2024-12-18T00:16:38.7647007Z >>> import torch.multiprocessing as mp 2024-12-18T00:16:38.7647369Z >>> import torch.nn as nn 2024-12-18T00:16:38.7647668Z >>> # On each spawned worker 2024-12-18T00:16:38.7647965Z >>> def worker(rank): 2024-12-18T00:16:38.7648320Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-12-18T00:16:38.7648731Z >>> torch.cuda.set_device(rank) 2024-12-18T00:16:38.7649079Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T00:16:38.7649510Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-12-18T00:16:38.7649943Z >>> model, device_ids=[rank], output_device=rank 2024-12-18T00:16:38.7650297Z >>> ) 2024-12-18T00:16:38.7650560Z >>> # Rank 1 gets one more input than rank 0. 2024-12-18T00:16:38.7650983Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-12-18T00:16:38.7651398Z >>> with model.join(): 2024-12-18T00:16:38.7651688Z >>> for _ in range(5): 2024-12-18T00:16:38.7651998Z >>> for inp in inputs: 2024-12-18T00:16:38.7652330Z >>> loss = model(inp).sum() 2024-12-18T00:16:38.7652670Z >>> loss.backward() 2024-12-18T00:16:38.7653064Z >>> # Without the join() API, the below synchronization will hang 2024-12-18T00:16:38.7653525Z >>> # blocking for rank 1's allreduce to complete. 2024-12-18T00:16:38.7653914Z >>> torch.cuda.synchronize(device=rank) 2024-12-18T00:16:38.7654150Z 2024-12-18T00:16:38.7654418Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.7654793Z 2024-12-18T00:16:38.7655555Z 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=2039. 2024-12-18T00:16:38.7656650Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.7657028Z 2024-12-18T00:16:38.7657349Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-12-18T00:16:38.7657779Z 2024-12-18T00:16:38.7657999Z Registers an optimizer with DDP such that the optimization for a 2024-12-18T00:16:38.7658525Z parameter will run immediately when that parameter's gradient is 2024-12-18T00:16:38.7659054Z finished with reduction, instead of waiting for all parameters' 2024-12-18T00:16:38.7659596Z gradients to finish reduction. This can result in a training speedup 2024-12-18T00:16:38.7660156Z depending on your workload since the optimizer can run while gradient 2024-12-18T00:16:38.7660727Z reduction for other parameters are still ongoing. In addition, this has 2024-12-18T00:16:38.7661297Z the potential to reduce peak memory consumption during training, as it 2024-12-18T00:16:38.7661858Z only needs to load the per-parameter optimizer states of a single 2024-12-18T00:16:38.7662393Z parameter at a time, instead of loading all per-parameter optimizer 2024-12-18T00:16:38.7662823Z states at once. 2024-12-18T00:16:38.7662967Z 2024-12-18T00:16:38.7663071Z Args: 2024-12-18T00:16:38.7663404Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-12-18T00:16:38.7663817Z as a fused optimizer. 2024-12-18T00:16:38.7664169Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-12-18T00:16:38.7664676Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-12-18T00:16:38.7665241Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-12-18T00:16:38.7665801Z Optimizers. If this is omitted, all DDP model parameters will be 2024-12-18T00:16:38.7666249Z optimized. 2024-12-18T00:16:38.7666629Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-12-18T00:16:38.7666957Z 2024-12-18T00:16:38.7667056Z .. warning :: 2024-12-18T00:16:38.7667416Z _register_fused_optim should only be called once on a DDP instance, 2024-12-18T00:16:38.7667968Z and registering multiple fused optimizers for the same DDP model 2024-12-18T00:16:38.7668449Z is not currently supported. Please ping 2024-12-18T00:16:38.7668960Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T00:16:38.7669411Z for your use case. 2024-12-18T00:16:38.7669589Z 2024-12-18T00:16:38.7669686Z .. warning :: 2024-12-18T00:16:38.7670027Z _register_fused_optim and register_comm_hook currently do not 2024-12-18T00:16:38.7670563Z compose together, meaning that custom DDP communication hooks are 2024-12-18T00:16:38.7671061Z not supported with overlapped optimizers. Please ping 2024-12-18T00:16:38.7671576Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T00:16:38.7672026Z for your use case. 2024-12-18T00:16:38.7672185Z 2024-12-18T00:16:38.7672288Z .. warning :: 2024-12-18T00:16:38.7672654Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-12-18T00:16:38.7673113Z with overlapped optimizer. Please ping 2024-12-18T00:16:38.7673775Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T00:16:38.7674228Z for your use case. 2024-12-18T00:16:38.7674387Z 2024-12-18T00:16:38.7674497Z Example:: 2024-12-18T00:16:38.7674620Z 2024-12-18T00:16:38.7674765Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-12-18T00:16:38.7675316Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-12-18T00:16:38.7675933Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-12-18T00:16:38.7676344Z >>> lr = 1e-2 2024-12-18T00:16:38.7676598Z >>> betas = (0.9, 0.99) 2024-12-18T00:16:38.7676876Z >>> eps = 1e-6 2024-12-18T00:16:38.7677254Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-12-18T00:16:38.7677713Z >>> # Example with subset of parameters 2024-12-18T00:16:38.7678090Z >>> params_to_opt = [list(net.parameters())[0]] 2024-12-18T00:16:38.7678520Z >>> net._register_fused_optim( 2024-12-18T00:16:38.7678976Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-12-18T00:16:38.7679425Z ... ) 2024-12-18T00:16:38.7679548Z 2024-12-18T00:16:38.7679802Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.7680183Z 2024-12-18T00:16:38.7888684Z 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. 2024-12-18T00:16:38.7889722Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.7890334Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-12-18T00:16:38.7890689Z 2024-12-18T00:16:38.7890964Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T00:16:38.7891638Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T00:16:38.7892302Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T00:16:38.7892996Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T00:16:38.7893417Z 2024-12-18T00:16:38.7893528Z .. note:: 2024-12-18T00:16:38.7893897Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-12-18T00:16:38.7894488Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-12-18T00:16:38.7895349Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T00:16:38.7895933Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T00:16:38.7896681Z One place we are confident in is that NHWC(channels_last) conversion for 2024-12-18T00:16:38.7897242Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-12-18T00:16:38.7897791Z even in cases where we have to apply permutation to input tensors. 2024-12-18T00:16:38.7898125Z 2024-12-18T00:16:38.7898428Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T00:16:38.7898905Z channels_last. This ensures that; 2024-12-18T00:16:38.7899357Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T00:16:38.7899932Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T00:16:38.7900515Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T00:16:38.7900987Z from memory_format conversion. 2024-12-18T00:16:38.7901216Z 2024-12-18T00:16:38.7901446Z The optimal case is that, layers between convolution layers are channels 2024-12-18T00:16:38.7902036Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T00:16:38.7902625Z encounters the first convolution layer and stay in that memory format. 2024-12-18T00:16:38.7903220Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T00:16:38.7903576Z 2024-12-18T00:16:38.7903802Z In case where a channels last incompatible layer is between convolution 2024-12-18T00:16:38.7904361Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T00:16:38.7904924Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T00:16:38.7905500Z contiguous format and be permuted to channels last when it encounters 2024-12-18T00:16:38.7906062Z another convolution layer. There's no point in propagating that 2024-12-18T00:16:38.7906617Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T00:16:38.7907055Z ``memory_format``. 2024-12-18T00:16:38.7907238Z 2024-12-18T00:16:38.7907471Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T00:16:38.7908055Z there might have been a better spot to fuse the permutation other than 2024-12-18T00:16:38.7908527Z immediately before a convolution. 2024-12-18T00:16:38.7908753Z 2024-12-18T00:16:38.7908859Z Args: 2024-12-18T00:16:38.7909208Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-12-18T00:16:38.7909641Z ``nn.Module`` 2024-12-18T00:16:38.7910015Z memory_format: user specified ``memory_format``, 2024-12-18T00:16:38.7910466Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T00:16:38.7910763Z 2024-12-18T00:16:38.7910866Z Returns: 2024-12-18T00:16:38.7911150Z The original module with updated ``nn.Conv2d`` 2024-12-18T00:16:38.7911405Z 2024-12-18T00:16:38.7911512Z Example: 2024-12-18T00:16:38.7911779Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:38.7912193Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T00:16:38.7912696Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T00:16:38.7913161Z >>> model = nn.Sequential( 2024-12-18T00:16:38.7913492Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-12-18T00:16:38.7913822Z >>> # This is identical to: 2024-12-18T00:16:38.7914280Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T00:16:38.7914921Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T00:16:38.7915419Z >>> out = model(input) 2024-12-18T00:16:38.7915704Z 2024-12-18T00:16:38.7916075Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.7916499Z 2024-12-18T00:16:38.7917191Z 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. 2024-12-18T00:16:38.7918206Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.7918928Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-12-18T00:16:38.7919570Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T00:16:38.7920250Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T00:16:38.7920907Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T00:16:38.7921601Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T00:16:38.7922028Z 2024-12-18T00:16:38.7922129Z .. note:: 2024-12-18T00:16:38.7922515Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-12-18T00:16:38.7923120Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-12-18T00:16:38.7923687Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T00:16:38.7924246Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T00:16:38.7924838Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-12-18T00:16:38.7925416Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-12-18T00:16:38.7925962Z even in cases where we have to apply permutation to input tensors. 2024-12-18T00:16:38.7926292Z 2024-12-18T00:16:38.7926521Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T00:16:38.7926990Z channels_last_3d. This ensures that; 2024-12-18T00:16:38.7927440Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T00:16:38.7928004Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T00:16:38.7928602Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T00:16:38.7929073Z from memory_format conversion. 2024-12-18T00:16:38.7929301Z 2024-12-18T00:16:38.7929527Z The optimal case is that, layers between convolution layers are channels 2024-12-18T00:16:38.7930115Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T00:16:38.7930707Z encounters the first convolution layer and stay in that memory format. 2024-12-18T00:16:38.7931292Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T00:16:38.7931658Z 2024-12-18T00:16:38.7931882Z In case where a channels last incompatible layer is between convolution 2024-12-18T00:16:38.7932445Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T00:16:38.7933021Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T00:16:38.7933605Z contiguous format and be permuted to channels last when it encounters 2024-12-18T00:16:38.7934167Z another convolution layer. There's no point in propagating that 2024-12-18T00:16:38.7934720Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T00:16:38.7935154Z ``memory_format``. 2024-12-18T00:16:38.7935339Z 2024-12-18T00:16:38.7935574Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T00:16:38.7936152Z there might have been a better spot to fuse the permutation other than 2024-12-18T00:16:38.7936618Z immediately before a convolution. 2024-12-18T00:16:38.7936842Z 2024-12-18T00:16:38.7936944Z Args: 2024-12-18T00:16:38.7937277Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-12-18T00:16:38.7937724Z ``nn.Module`` 2024-12-18T00:16:38.7938155Z memory_format: user specified ``memory_format``, 2024-12-18T00:16:38.7938641Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T00:16:38.7938943Z 2024-12-18T00:16:38.7939053Z Returns: 2024-12-18T00:16:38.7939346Z The original module with updated ``nn.Conv3d`` 2024-12-18T00:16:38.7939600Z 2024-12-18T00:16:38.7939690Z Example: 2024-12-18T00:16:38.7940022Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:38.7940444Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T00:16:38.7940951Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T00:16:38.7941418Z >>> model = nn.Sequential( 2024-12-18T00:16:38.7941734Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-12-18T00:16:38.7942077Z >>> # This is identical to: 2024-12-18T00:16:38.7942541Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T00:16:38.7943195Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T00:16:38.7943699Z >>> out = model(input) 2024-12-18T00:16:38.7943976Z 2024-12-18T00:16:38.7944339Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.7944719Z 2024-12-18T00:16:38.8103749Z 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=936. 2024-12-18T00:16:38.8104662Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.8105276Z Prune tensor by removing random channels along the specified dimension. 2024-12-18T00:16:38.8105621Z 2024-12-18T00:16:38.8105870Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T00:16:38.8106443Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T00:16:38.8106940Z along the specified ``dim`` selected at random. 2024-12-18T00:16:38.8107391Z Modifies module in place (and also return the modified module) 2024-12-18T00:16:38.8107797Z by: 2024-12-18T00:16:38.8107925Z 2024-12-18T00:16:38.8108136Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:38.8108831Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:38.8109391Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:38.8109940Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:38.8110357Z ``name+'_orig'``. 2024-12-18T00:16:38.8110535Z 2024-12-18T00:16:38.8110624Z Args: 2024-12-18T00:16:38.8110935Z module (nn.Module): module containing the tensor to prune 2024-12-18T00:16:38.8111420Z name (str): parameter name within ``module`` on which pruning 2024-12-18T00:16:38.8111826Z will act. 2024-12-18T00:16:38.8112167Z amount (int or float): quantity of parameters to prune. 2024-12-18T00:16:38.8112641Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T00:16:38.8113149Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T00:16:38.8113611Z absolute number of parameters to prune. 2024-12-18T00:16:38.8114064Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T00:16:38.8114382Z 2024-12-18T00:16:38.8114488Z Returns: 2024-12-18T00:16:38.8114901Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T00:16:38.8115372Z 2024-12-18T00:16:38.8115472Z Examples: 2024-12-18T00:16:38.8115718Z >>> # xdoctest: +SKIP 2024-12-18T00:16:38.8116025Z >>> m = prune.random_structured( 2024-12-18T00:16:38.8116407Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-12-18T00:16:38.8116747Z ... ) 2024-12-18T00:16:38.8117074Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-12-18T00:16:38.8117662Z >>> print(columns_pruned) 2024-12-18T00:16:38.8117965Z 3 2024-12-18T00:16:38.8118186Z 2024-12-18T00:16:38.8118641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.8119025Z 2024-12-18T00:16:38.8119749Z 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=977. 2024-12-18T00:16:38.8120724Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.8121419Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-12-18T00:16:38.8121859Z 2024-12-18T00:16:38.8122095Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T00:16:38.8122674Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T00:16:38.8123200Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-12-18T00:16:38.8123706Z Modifies module in place (and also return the modified module) 2024-12-18T00:16:38.8124102Z by: 2024-12-18T00:16:38.8124240Z 2024-12-18T00:16:38.8124453Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:38.8125011Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:38.8125572Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:38.8126121Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:38.8126557Z ``name+'_orig'``. 2024-12-18T00:16:38.8126721Z 2024-12-18T00:16:38.8126811Z Args: 2024-12-18T00:16:38.8127125Z module (nn.Module): module containing the tensor to prune 2024-12-18T00:16:38.8127608Z name (str): parameter name within ``module`` on which pruning 2024-12-18T00:16:38.8128006Z will act. 2024-12-18T00:16:38.8128350Z amount (int or float): quantity of parameters to prune. 2024-12-18T00:16:38.8128808Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T00:16:38.8129311Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T00:16:38.8129767Z absolute number of parameters to prune. 2024-12-18T00:16:38.8130219Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-12-18T00:16:38.8130700Z entries for argument ``p`` in :func:`torch.norm`. 2024-12-18T00:16:38.8131179Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T00:16:38.8131725Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-12-18T00:16:38.8132277Z shape as module parameter) used to compute mask for pruning. 2024-12-18T00:16:38.8132814Z The values in this tensor indicate the importance of the corresponding 2024-12-18T00:16:38.8133298Z elements in the parameter being pruned. 2024-12-18T00:16:38.8133784Z If unspecified or None, the module parameter will be used in its place. 2024-12-18T00:16:38.8134131Z 2024-12-18T00:16:38.8134238Z Returns: 2024-12-18T00:16:38.8134587Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T00:16:38.8134938Z 2024-12-18T00:16:38.8135031Z Examples: 2024-12-18T00:16:38.8135296Z >>> from torch.nn.utils import prune 2024-12-18T00:16:38.8135643Z >>> m = prune.ln_structured( 2024-12-18T00:16:38.8136039Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-12-18T00:16:38.8136425Z ... ) 2024-12-18T00:16:38.8136649Z 2024-12-18T00:16:38.8137023Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.8137390Z 2024-12-18T00:16:38.8137948Z 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=1024. 2024-12-18T00:16:38.8138928Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.8139342Z 2024-12-18T00:16:38.8139787Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-12-18T00:16:38.8140332Z 2024-12-18T00:16:38.8140449Z Modifies modules in place by: 2024-12-18T00:16:38.8140657Z 2024-12-18T00:16:38.8140920Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:38.8141471Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:38.8142022Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:38.8142560Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:38.8142985Z ``name+'_orig'``. 2024-12-18T00:16:38.8143139Z 2024-12-18T00:16:38.8143229Z Args: 2024-12-18T00:16:38.8143559Z parameters (Iterable of (module, name) tuples): parameters of 2024-12-18T00:16:38.8144079Z the model to prune in a global fashion, i.e. by aggregating all 2024-12-18T00:16:38.8144609Z weights prior to deciding which ones to prune. module must be of 2024-12-18T00:16:38.8145093Z type :class:`nn.Module`, and name must be a string. 2024-12-18T00:16:38.8145574Z pruning_method (function): a valid pruning function from this module, 2024-12-18T00:16:38.8146100Z or a custom one implemented by the user that satisfies the 2024-12-18T00:16:38.8146627Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-12-18T00:16:38.8147207Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-12-18T00:16:38.8147790Z the corresponding parameter's importance scores tensor. The tensor 2024-12-18T00:16:38.8148348Z should be the same shape as the parameter, and is used for computing 2024-12-18T00:16:38.8148774Z mask for pruning. 2024-12-18T00:16:38.8149160Z If unspecified or None, the parameter will be used in place of its 2024-12-18T00:16:38.8149594Z importance scores. 2024-12-18T00:16:38.8149901Z kwargs: other keyword arguments such as: 2024-12-18T00:16:38.8150337Z amount (int or float): quantity of parameters to prune across the 2024-12-18T00:16:38.8150761Z specified parameters. 2024-12-18T00:16:38.8151135Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T00:16:38.8151639Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T00:16:38.8152091Z absolute number of parameters to prune. 2024-12-18T00:16:38.8152331Z 2024-12-18T00:16:38.8152432Z Raises: 2024-12-18T00:16:38.8152711Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-12-18T00:16:38.8152972Z 2024-12-18T00:16:38.8153060Z Note: 2024-12-18T00:16:38.8153403Z Since global structured pruning doesn't make much sense unless the 2024-12-18T00:16:38.8153940Z norm is normalized by the size of the parameter, we now limit the 2024-12-18T00:16:38.8154419Z scope of global pruning to unstructured methods. 2024-12-18T00:16:38.8154685Z 2024-12-18T00:16:38.8154792Z Examples: 2024-12-18T00:16:38.8155034Z >>> from torch.nn.utils import prune 2024-12-18T00:16:38.8155389Z >>> from collections import OrderedDict 2024-12-18T00:16:38.8155739Z >>> net = nn.Sequential(OrderedDict([ 2024-12-18T00:16:38.8156080Z ... ('first', nn.Linear(10, 4)), 2024-12-18T00:16:38.8167045Z ... ('second', nn.Linear(4, 1)), 2024-12-18T00:16:38.8167398Z ... ])) 2024-12-18T00:16:38.8167653Z >>> parameters_to_prune = ( 2024-12-18T00:16:38.8167967Z ... (net.first, 'weight'), 2024-12-18T00:16:38.8168288Z ... (net.second, 'weight'), 2024-12-18T00:16:38.8168592Z ... ) 2024-12-18T00:16:38.8168828Z >>> prune.global_unstructured( 2024-12-18T00:16:38.8169157Z ... parameters_to_prune, 2024-12-18T00:16:38.8169497Z ... pruning_method=prune.L1Unstructured, 2024-12-18T00:16:38.8169853Z ... amount=10, 2024-12-18T00:16:38.8170189Z ... ) 2024-12-18T00:16:38.8170580Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-12-18T00:16:38.8171025Z tensor(10) 2024-12-18T00:16:38.8171175Z 2024-12-18T00:16:38.8171179Z 2024-12-18T00:16:38.8171433Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.8171809Z 2024-12-18T00:16:38.8172452Z 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=1143. 2024-12-18T00:16:38.8173356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.8174313Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-12-18T00:16:38.8174818Z 2024-12-18T00:16:38.8175049Z Modifies module in place (and also return the modified module) by: 2024-12-18T00:16:38.8175383Z 2024-12-18T00:16:38.8175596Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:38.8176162Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:38.8176719Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:38.8177267Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:38.8177699Z ``name+'_orig'``. 2024-12-18T00:16:38.8177866Z 2024-12-18T00:16:38.8177974Z Args: 2024-12-18T00:16:38.8178274Z module (nn.Module): module containing the tensor to prune 2024-12-18T00:16:38.8178760Z name (str): parameter name within ``module`` on which pruning 2024-12-18T00:16:38.8179159Z will act. 2024-12-18T00:16:38.8179500Z mask (Tensor): binary mask to be applied to the parameter. 2024-12-18T00:16:38.8179793Z 2024-12-18T00:16:38.8179897Z Returns: 2024-12-18T00:16:38.8180244Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T00:16:38.8180596Z 2024-12-18T00:16:38.8180690Z Examples: 2024-12-18T00:16:38.8180958Z >>> from torch.nn.utils import prune 2024-12-18T00:16:38.8181309Z >>> m = prune.custom_from_mask( 2024-12-18T00:16:38.8181710Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-12-18T00:16:38.8182086Z ... ) 2024-12-18T00:16:38.8182333Z >>> print(m.bias_mask) 2024-12-18T00:16:38.8182630Z tensor([0., 1., 0.]) 2024-12-18T00:16:38.8182809Z 2024-12-18T00:16:38.8182911Z 2024-12-18T00:16:38.8183288Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.8183657Z 2024-12-18T00:16:38.9187843Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=116. 2024-12-18T00:16:38.9188759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.9189501Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-12-18T00:16:38.9190013Z 2024-12-18T00:16:38.9190258Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-12-18T00:16:38.9190848Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-12-18T00:16:38.9191412Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-12-18T00:16:38.9191856Z (UAI 2018). 2024-12-18T00:16:38.9191996Z 2024-12-18T00:16:38.9192231Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-12-18T00:16:38.9192801Z but using exponential weights instead of equal weights across iterations. 2024-12-18T00:16:38.9193173Z 2024-12-18T00:16:38.9193413Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-12-18T00:16:38.9194005Z on the device :attr:`device` and allows to compute running averages of the 2024-12-18T00:16:38.9194471Z parameters of the :attr:`model`. 2024-12-18T00:16:38.9194682Z 2024-12-18T00:16:38.9194962Z Args: 2024-12-18T00:16:38.9195309Z model (torch.nn.Module): model to use with SWA/EMA 2024-12-18T00:16:38.9195813Z device (torch.device, optional): if provided, the averaged model will be 2024-12-18T00:16:38.9196290Z stored on the :attr:`device` 2024-12-18T00:16:38.9196724Z avg_fn (function, optional): the averaging function used to update 2024-12-18T00:16:38.9197359Z parameters; the function must take in the current value of the 2024-12-18T00:16:38.9198013Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-12-18T00:16:38.9198621Z parameter, and the number of models already averaged; if None, 2024-12-18T00:16:38.9199098Z an equally weighted average is used (default: None) 2024-12-18T00:16:38.9199612Z multi_avg_fn (function, optional): the averaging function used to update 2024-12-18T00:16:38.9200209Z parameters inplace; the function must take in the current values of the 2024-12-18T00:16:38.9200852Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-12-18T00:16:38.9201483Z parameters as a list, and the number of models already averaged; if None, 2024-12-18T00:16:38.9202005Z an equally weighted average is used (default: None) 2024-12-18T00:16:38.9202485Z use_buffers (bool): if ``True``, it will compute running averages for 2024-12-18T00:16:38.9203042Z both the parameters and the buffers of the model. (default: ``False``) 2024-12-18T00:16:38.9203400Z 2024-12-18T00:16:38.9203495Z Example: 2024-12-18T00:16:38.9203769Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:38.9204149Z >>> loader, optimizer, model, loss_fn = ... 2024-12-18T00:16:38.9204563Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-12-18T00:16:38.9205086Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-12-18T00:16:38.9205556Z >>> T_max=300) 2024-12-18T00:16:38.9205900Z >>> swa_start = 160 2024-12-18T00:16:38.9206229Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-12-18T00:16:38.9206597Z >>> for i in range(300): 2024-12-18T00:16:38.9206901Z >>> for input, target in loader: 2024-12-18T00:16:38.9207252Z >>> optimizer.zero_grad() 2024-12-18T00:16:38.9207629Z >>> loss_fn(model(input), target).backward() 2024-12-18T00:16:38.9207997Z >>> optimizer.step() 2024-12-18T00:16:38.9208318Z >>> if i > swa_start: 2024-12-18T00:16:38.9208642Z >>> swa_model.update_parameters(model) 2024-12-18T00:16:38.9209008Z >>> swa_scheduler.step() 2024-12-18T00:16:38.9209331Z >>> else: 2024-12-18T00:16:38.9209605Z >>> scheduler.step() 2024-12-18T00:16:38.9209914Z >>> 2024-12-18T00:16:38.9210196Z >>> # Update bn statistics for the swa_model at the end 2024-12-18T00:16:38.9210641Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-12-18T00:16:38.9210935Z 2024-12-18T00:16:38.9211240Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-12-18T00:16:38.9211866Z If no averaging function is provided, the default is to compute 2024-12-18T00:16:38.9212375Z equally-weighted average of the weights (SWA). 2024-12-18T00:16:38.9212636Z 2024-12-18T00:16:38.9212747Z Example: 2024-12-18T00:16:38.9213029Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:38.9213481Z >>> # Compute exponential moving averages of the weights and buffers 2024-12-18T00:16:38.9213992Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-12-18T00:16:38.9214512Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-12-18T00:16:38.9214865Z 2024-12-18T00:16:38.9214977Z .. note:: 2024-12-18T00:16:38.9215349Z When using SWA/EMA with models containing Batch Normalization you may 2024-12-18T00:16:38.9216017Z need to update the activation statistics for Batch Normalization. 2024-12-18T00:16:38.9216571Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-12-18T00:16:38.9217164Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-12-18T00:16:38.9217826Z statistics in a post-training step by passing data through the model. The 2024-12-18T00:16:38.9218430Z second does it during the parameter update phase by averaging all buffers. 2024-12-18T00:16:38.9219045Z Empirical evidence has shown that updating the statistics in normalization 2024-12-18T00:16:38.9219655Z layers increases accuracy, but you may wish to empirically test which 2024-12-18T00:16:38.9220158Z approach yields the best results in your problem. 2024-12-18T00:16:38.9220442Z 2024-12-18T00:16:38.9220540Z .. note:: 2024-12-18T00:16:38.9220939Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-12-18T00:16:38.9221313Z 2024-12-18T00:16:38.9221463Z .. note:: 2024-12-18T00:16:38.9221810Z When :meth:`update_parameters` is called for the first time (i.e. 2024-12-18T00:16:38.9222328Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-12-18T00:16:38.9222834Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-12-18T00:16:38.9223360Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-12-18T00:16:38.9223777Z to update the parameters. 2024-12-18T00:16:38.9223991Z 2024-12-18T00:16:38.9224217Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T00:16:38.9224699Z https://arxiv.org/abs/1803.05407 2024-12-18T00:16:38.9225168Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-12-18T00:16:38.9225623Z Average: 2024-12-18T00:16:38.9225878Z https://arxiv.org/abs/1806.05594 2024-12-18T00:16:38.9226314Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-12-18T00:16:38.9226757Z https://arxiv.org/abs/1904.11943 2024-12-18T00:16:38.9227210Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-12-18T00:16:38.9227661Z Generalizes Well: 2024-12-18T00:16:38.9227958Z https://arxiv.org/abs/2001.02312 2024-12-18T00:16:38.9228278Z .. _Polyak averaging: 2024-12-18T00:16:38.9228628Z https://paperswithcode.com/method/polyak-averaging 2024-12-18T00:16:38.9229012Z 2024-12-18T00:16:38.9229387Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.9229753Z 2024-12-18T00:16:38.9230241Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=368. 2024-12-18T00:16:38.9231085Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:38.9231683Z Anneals the learning rate in each parameter group to a fixed value. 2024-12-18T00:16:38.9232023Z 2024-12-18T00:16:38.9232260Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-12-18T00:16:38.9232827Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-12-18T00:16:38.9233155Z 2024-12-18T00:16:38.9233255Z Args: 2024-12-18T00:16:38.9233564Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-12-18T00:16:38.9234054Z swa_lrs (float or list): the learning rate value for all param groups 2024-12-18T00:16:38.9234513Z together or separately for each group. 2024-12-18T00:16:38.9234967Z annealing_epochs (int): number of epochs in the annealing phase 2024-12-18T00:16:38.9235388Z (default: 10) 2024-12-18T00:16:38.9235778Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-12-18T00:16:38.9236330Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-12-18T00:16:38.9236826Z (default: "cos") 2024-12-18T00:16:38.9237195Z last_epoch (int): the index of the last epoch (default: -1) 2024-12-18T00:16:38.9237504Z 2024-12-18T00:16:38.9237686Z The :class:`SWALR` scheduler can be used together with other 2024-12-18T00:16:38.9238208Z schedulers to switch to a constant learning rate late in the training 2024-12-18T00:16:38.9238783Z as in the example below. 2024-12-18T00:16:38.9239010Z 2024-12-18T00:16:38.9239106Z Example: 2024-12-18T00:16:38.9239377Z >>> # xdoctest: +SKIP("Undefined variables") 2024-12-18T00:16:38.9239746Z >>> loader, optimizer, model = ... 2024-12-18T00:16:38.9240090Z >>> lr_lambda = lambda epoch: 0.9 2024-12-18T00:16:38.9240540Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-12-18T00:16:38.9240978Z >>> lr_lambda=lr_lambda) 2024-12-18T00:16:38.9241375Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-12-18T00:16:38.9241853Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-12-18T00:16:38.9242257Z >>> swa_start = 160 2024-12-18T00:16:38.9242540Z >>> for i in range(300): 2024-12-18T00:16:38.9242855Z >>> for input, target in loader: 2024-12-18T00:16:38.9243191Z >>> optimizer.zero_grad() 2024-12-18T00:16:38.9243560Z >>> loss_fn(model(input), target).backward() 2024-12-18T00:16:38.9243928Z >>> optimizer.step() 2024-12-18T00:16:38.9244245Z >>> if i > swa_start: 2024-12-18T00:16:38.9244557Z >>> swa_scheduler.step() 2024-12-18T00:16:38.9244862Z >>> else: 2024-12-18T00:16:38.9245129Z >>> scheduler.step() 2024-12-18T00:16:38.9245346Z 2024-12-18T00:16:38.9245572Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T00:16:38.9245693Z https://arxiv.org/abs/1803.05407 2024-12-18T00:16:38.9245794Z 2024-12-18T00:16:38.9246048Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:38.9246052Z 2024-12-18T00:16:39.3896003Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1274. 2024-12-18T00:16:39.3896966Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:39.3897517Z Asserts that ``actual`` and ``expected`` are close. 2024-12-18T00:16:39.3897782Z 2024-12-18T00:16:39.3898215Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-12-18T00:16:39.3898714Z 2024-12-18T00:16:39.3898826Z .. math:: 2024-12-18T00:16:39.3898960Z 2024-12-18T00:16:39.3899346Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-12-18T00:16:39.3899838Z 2024-12-18T00:16:39.3900198Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-12-18T00:16:39.3900891Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-12-18T00:16:39.3901208Z 2024-12-18T00:16:39.3901413Z In addition, they are only considered close if they have the same 2024-12-18T00:16:39.3901746Z 2024-12-18T00:16:39.3901949Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-12-18T00:16:39.3902408Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-12-18T00:16:39.3902806Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-12-18T00:16:39.3903200Z - stride (if ``check_stride`` is ``True``). 2024-12-18T00:16:39.3903436Z 2024-12-18T00:16:39.3903748Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-12-18T00:16:39.3904167Z 2024-12-18T00:16:39.3904538Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-12-18T00:16:39.3905619Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-12-18T00:16:39.3906349Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-12-18T00:16:39.3907093Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-12-18T00:16:39.3907693Z 2024-12-18T00:16:39.3908029Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-12-18T00:16:39.3908800Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-12-18T00:16:39.3909378Z definition above. 2024-12-18T00:16:39.3909539Z 2024-12-18T00:16:39.3909859Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-12-18T00:16:39.3910654Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-12-18T00:16:39.3911519Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-12-18T00:16:39.3912376Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-12-18T00:16:39.3913104Z their elements are considered close according to the above definition. 2024-12-18T00:16:39.3913451Z 2024-12-18T00:16:39.3913560Z .. note:: 2024-12-18T00:16:39.3913691Z 2024-12-18T00:16:39.3914037Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-12-18T00:16:39.3914818Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-12-18T00:16:39.3915534Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-12-18T00:16:39.3915937Z 2024-12-18T00:16:39.3916028Z Args: 2024-12-18T00:16:39.3916275Z actual (Any): Actual input. 2024-12-18T00:16:39.3916608Z expected (Any): Expected input. 2024-12-18T00:16:39.3917201Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-12-18T00:16:39.3917834Z are allowed. Otherwise type equality is required. 2024-12-18T00:16:39.3918572Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-12-18T00:16:39.3919332Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T00:16:39.3920085Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-12-18T00:16:39.3920841Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T00:16:39.3921483Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-12-18T00:16:39.3922151Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-12-18T00:16:39.3922803Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-12-18T00:16:39.3923417Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-12-18T00:16:39.3924134Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-12-18T00:16:39.3924956Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-12-18T00:16:39.3925592Z :func:`torch.promote_types`) before being compared. 2024-12-18T00:16:39.3926234Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-12-18T00:16:39.3927038Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-12-18T00:16:39.3927670Z compared. 2024-12-18T00:16:39.3928203Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-12-18T00:16:39.3929051Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-12-18T00:16:39.3929943Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-12-18T00:16:39.3930542Z should return the new message. 2024-12-18T00:16:39.3930763Z 2024-12-18T00:16:39.3930853Z Raises: 2024-12-18T00:16:39.3931215Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-12-18T00:16:39.3931736Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-12-18T00:16:39.3932349Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-12-18T00:16:39.3933163Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-12-18T00:16:39.3933761Z different types. 2024-12-18T00:16:39.3934305Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-12-18T00:16:39.3935165Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-12-18T00:16:39.3935973Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-12-18T00:16:39.3936715Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-12-18T00:16:39.3937256Z :attr:`~torch.Tensor.layout`. 2024-12-18T00:16:39.3937707Z AssertionError: If only one of corresponding tensors is quantized. 2024-12-18T00:16:39.3938421Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-12-18T00:16:39.3939226Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-12-18T00:16:39.3939764Z :attr:`~torch.Tensor.device`. 2024-12-18T00:16:39.3940324Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-12-18T00:16:39.3941145Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-12-18T00:16:39.3941992Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-12-18T00:16:39.3942468Z 2024-12-18T00:16:39.3942846Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-12-18T00:16:39.3943473Z ``dtype``'s, the maximum of both tolerances is used. 2024-12-18T00:16:39.3943750Z 2024-12-18T00:16:39.3943883Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3944271Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-12-18T00:16:39.3944632Z +===========================+============+==========+ 2024-12-18T00:16:39.3945011Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-12-18T00:16:39.3945399Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3945782Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-12-18T00:16:39.3946168Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3946551Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3946938Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3947317Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-12-18T00:16:39.3947686Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3948070Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-12-18T00:16:39.3948452Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3948904Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3949289Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3949681Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-12-18T00:16:39.3950057Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3950497Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3950883Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3951271Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3951660Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3952032Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3952424Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3952812Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3953202Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3953594Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:39.3953965Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3954343Z | other | ``0.0`` | ``0.0`` | 2024-12-18T00:16:39.3954715Z +---------------------------+------------+----------+ 2024-12-18T00:16:39.3954968Z 2024-12-18T00:16:39.3955070Z .. note:: 2024-12-18T00:16:39.3955204Z 2024-12-18T00:16:39.3955604Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-12-18T00:16:39.3956472Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-12-18T00:16:39.3957217Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-12-18T00:16:39.3957596Z 2024-12-18T00:16:39.3957698Z >>> import functools 2024-12-18T00:16:39.3958151Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-12-18T00:16:39.3958740Z >>> assert_equal(1e-9, 1e-10) 2024-12-18T00:16:39.3959078Z Traceback (most recent call last): 2024-12-18T00:16:39.3959420Z ... 2024-12-18T00:16:39.3959665Z AssertionError: Scalars are not equal! 2024-12-18T00:16:39.3960002Z 2024-12-18T00:16:39.3960271Z Expected 1e-10 but got 1e-09. 2024-12-18T00:16:39.3960615Z Absolute difference: 9.000000000000001e-10 2024-12-18T00:16:39.3960970Z Relative difference: 9.0 2024-12-18T00:16:39.3961168Z 2024-12-18T00:16:39.3961260Z Examples: 2024-12-18T00:16:39.3961513Z >>> # tensor to tensor comparison 2024-12-18T00:16:39.3961875Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-12-18T00:16:39.3962261Z >>> actual = torch.acos(torch.cos(expected)) 2024-12-18T00:16:39.3962663Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:39.3962924Z 2024-12-18T00:16:39.3963054Z >>> # scalar to scalar comparison 2024-12-18T00:16:39.3963366Z >>> import math 2024-12-18T00:16:39.3963646Z >>> expected = math.sqrt(2.0) 2024-12-18T00:16:39.3963978Z >>> actual = 2.0 / math.sqrt(2.0) 2024-12-18T00:16:39.3964345Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:39.3964600Z 2024-12-18T00:16:39.3964745Z >>> # numpy array to numpy array comparison 2024-12-18T00:16:39.3965083Z >>> import numpy as np 2024-12-18T00:16:39.3965400Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-12-18T00:16:39.3965767Z >>> actual = np.arccos(np.cos(expected)) 2024-12-18T00:16:39.3966154Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:39.3966406Z 2024-12-18T00:16:39.3966541Z >>> # sequence to sequence comparison 2024-12-18T00:16:39.3966869Z >>> import numpy as np 2024-12-18T00:16:39.3967316Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-12-18T00:16:39.3967919Z >>> # length and their elements have to match. 2024-12-18T00:16:39.3968335Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-12-18T00:16:39.3968726Z >>> actual = tuple(expected) 2024-12-18T00:16:39.3969090Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:39.3969349Z 2024-12-18T00:16:39.3969532Z >>> # mapping to mapping comparison 2024-12-18T00:16:39.3969895Z >>> from collections import OrderedDict 2024-12-18T00:16:39.3970240Z >>> import numpy as np 2024-12-18T00:16:39.3970541Z >>> foo = torch.tensor(1.0) 2024-12-18T00:16:39.3970852Z >>> bar = 2.0 2024-12-18T00:16:39.3971103Z >>> baz = np.array(3.0) 2024-12-18T00:16:39.3971553Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-12-18T00:16:39.3972136Z >>> # have to have the same set of keys and their elements have to match. 2024-12-18T00:16:39.3972675Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-12-18T00:16:39.3973143Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-12-18T00:16:39.3973534Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:39.3973989Z 2024-12-18T00:16:39.3974117Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T00:16:39.3974479Z >>> actual = expected.clone() 2024-12-18T00:16:39.3974876Z >>> # By default, directly related instances can be compared 2024-12-18T00:16:39.3975389Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-12-18T00:16:39.3975930Z >>> # This check can be made more strict with allow_subclasses=False 2024-12-18T00:16:39.3976352Z >>> torch.testing.assert_close( 2024-12-18T00:16:39.3976785Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-12-18T00:16:39.3977202Z ... ) 2024-12-18T00:16:39.3977462Z Traceback (most recent call last): 2024-12-18T00:16:39.3977790Z ... 2024-12-18T00:16:39.3978121Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T00:16:39.3978678Z and . 2024-12-18T00:16:39.3979246Z >>> # If the inputs are not directly related, they are never considered close 2024-12-18T00:16:39.3979772Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-12-18T00:16:39.3980183Z Traceback (most recent call last): 2024-12-18T00:16:39.3980490Z ... 2024-12-18T00:16:39.3980910Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T00:16:39.3981434Z and . 2024-12-18T00:16:39.3981915Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-12-18T00:16:39.3982433Z >>> # their type if check_dtype=False. 2024-12-18T00:16:39.3982855Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-12-18T00:16:39.3983142Z 2024-12-18T00:16:39.3983247Z >>> # NaN != NaN by default. 2024-12-18T00:16:39.3983585Z >>> expected = torch.tensor(float("Nan")) 2024-12-18T00:16:39.3983942Z >>> actual = expected.clone() 2024-12-18T00:16:39.3984309Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:39.3984695Z Traceback (most recent call last): 2024-12-18T00:16:39.3985003Z ... 2024-12-18T00:16:39.3985260Z AssertionError: Scalars are not close! 2024-12-18T00:16:39.3985594Z 2024-12-18T00:16:39.3985854Z Expected nan but got nan. 2024-12-18T00:16:39.3986217Z Absolute difference: nan (up to 1e-05 allowed) 2024-12-18T00:16:39.3986616Z Relative difference: nan (up to 1.3e-06 allowed) 2024-12-18T00:16:39.3987086Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-12-18T00:16:39.3987420Z 2024-12-18T00:16:39.3987634Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T00:16:39.3988064Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-12-18T00:16:39.3988455Z >>> # The default error message can be overwritten. 2024-12-18T00:16:39.3989018Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-12-18T00:16:39.3989589Z Traceback (most recent call last): 2024-12-18T00:16:39.3989957Z ... 2024-12-18T00:16:39.3990250Z AssertionError: Argh, the tensors are not close! 2024-12-18T00:16:39.3990747Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-12-18T00:16:39.3991205Z >>> # extra information 2024-12-18T00:16:39.3991504Z >>> torch.testing.assert_close( 2024-12-18T00:16:39.3991943Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-12-18T00:16:39.3992361Z ... ) 2024-12-18T00:16:39.3992624Z Traceback (most recent call last): 2024-12-18T00:16:39.3992951Z ... 2024-12-18T00:16:39.3993180Z AssertionError: Header 2024-12-18T00:16:39.3993470Z 2024-12-18T00:16:39.3993730Z Tensor-likes are not close! 2024-12-18T00:16:39.3994033Z 2024-12-18T00:16:39.3994302Z Mismatched elements: 2 / 3 (66.7%) 2024-12-18T00:16:39.3994746Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-12-18T00:16:39.3995324Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-12-18T00:16:39.3995768Z 2024-12-18T00:16:39.3996007Z Footer 2024-12-18T00:16:39.3996231Z 2024-12-18T00:16:39.3996591Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:39.3996969Z 2024-12-18T00:16:40.6476778Z 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=110. 2024-12-18T00:16:40.6477759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.6478300Z Register a container-like type as pytree node. 2024-12-18T00:16:40.6478610Z 2024-12-18T00:16:40.6478704Z Args: 2024-12-18T00:16:40.6479029Z cls (type): A Python type to treat as an internal pytree node. 2024-12-18T00:16:40.6479625Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-12-18T00:16:40.6480290Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-12-18T00:16:40.6480959Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-12-18T00:16:40.6481495Z passed to the ``unflatten_fn``. 2024-12-18T00:16:40.6481998Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-12-18T00:16:40.6482665Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-12-18T00:16:40.6483218Z The function should return an instance of ``cls``. 2024-12-18T00:16:40.6483763Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-12-18T00:16:40.6484322Z qualified name used when serializing the tree spec. 2024-12-18T00:16:40.6484906Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-12-18T00:16:40.6485626Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-12-18T00:16:40.6486307Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-12-18T00:16:40.6487017Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-12-18T00:16:40.6487714Z how to convert the custom json dumpable representation of the context back to the 2024-12-18T00:16:40.6488364Z original context. This is used for json deserialization, which is being used in 2024-12-18T00:16:40.6489115Z :mod:`torch.export` right now. 2024-12-18T00:16:40.6489341Z 2024-12-18T00:16:40.6489459Z Example:: 2024-12-18T00:16:40.6489608Z 2024-12-18T00:16:40.6489712Z >>> # xdoctest: +SKIP 2024-12-18T00:16:40.6490056Z >>> # Registry a Python type with lambda functions 2024-12-18T00:16:40.6490479Z >>> register_pytree_node( 2024-12-18T00:16:40.6490835Z ... set, 2024-12-18T00:16:40.6491111Z ... lambda s: (sorted(s), None, None), 2024-12-18T00:16:40.6491483Z ... lambda children, _: set(children), 2024-12-18T00:16:40.6491819Z ... ) 2024-12-18T00:16:40.6492042Z 2024-12-18T00:16:40.6492416Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.6492784Z 2024-12-18T00:16:40.6975189Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1201. 2024-12-18T00:16:40.6976192Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.6976582Z 2024-12-18T00:16:40.6976797Z Context passed to policy function during selective checkpointing. 2024-12-18T00:16:40.6977141Z 2024-12-18T00:16:40.6977370Z This class is used to pass relevant metadata to the policy function during 2024-12-18T00:16:40.6977992Z selective checkpointing. The metadata includes whether the current invocation 2024-12-18T00:16:40.6978542Z of the policy function is during recomputation or not. 2024-12-18T00:16:40.6978814Z 2024-12-18T00:16:40.6978915Z Example: 2024-12-18T00:16:40.6979138Z >>> # xdoctest: +SKIP(stub) 2024-12-18T00:16:40.6979420Z >>> 2024-12-18T00:16:40.6979671Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T00:16:40.6980024Z >>> print(ctx.is_recompute) 2024-12-18T00:16:40.6980320Z >>> 2024-12-18T00:16:40.6980706Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T00:16:40.6981199Z >>> 2024-12-18T00:16:40.6981480Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T00:16:40.6981835Z >>> fn, x, y, 2024-12-18T00:16:40.6982102Z >>> use_reentrant=False, 2024-12-18T00:16:40.6982396Z >>> context_fn=context_fn, 2024-12-18T00:16:40.6982692Z >>> ) 2024-12-18T00:16:40.6982830Z 2024-12-18T00:16:40.6983092Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.6983474Z 2024-12-18T00:16:40.6984116Z 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=1335. 2024-12-18T00:16:40.6985122Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.6985499Z 2024-12-18T00:16:40.6985751Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-12-18T00:16:40.6986103Z 2024-12-18T00:16:40.6986331Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-12-18T00:16:40.6986833Z operations are recomputed during the backward pass. 2024-12-18T00:16:40.6987105Z 2024-12-18T00:16:40.6987194Z Args: 2024-12-18T00:16:40.6987443Z policy_fn_or_list (Callable or List): 2024-12-18T00:16:40.6987850Z - If a policy function is provided, it should accept a 2024-12-18T00:16:40.6988384Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-12-18T00:16:40.6988967Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-12-18T00:16:40.6989528Z indicating whether the execution of the op should be recomputed or not. 2024-12-18T00:16:40.6990105Z - If a list of operations is provided, it is equivalent to a policy 2024-12-18T00:16:40.6990621Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-12-18T00:16:40.6991153Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-12-18T00:16:40.6991593Z operations. 2024-12-18T00:16:40.6992093Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-12-18T00:16:40.6992695Z raised if any tensors cached by selective activation checkpoint are 2024-12-18T00:16:40.6993245Z mutated in order to ensure correctness. If set to `True`, this check 2024-12-18T00:16:40.6993678Z is disabled. 2024-12-18T00:16:40.6993934Z Returns: 2024-12-18T00:16:40.6994225Z A tuple of two context managers. 2024-12-18T00:16:40.6994486Z 2024-12-18T00:16:40.6994579Z Example: 2024-12-18T00:16:40.6994825Z >>> # xdoctest: +REQUIRES(LINUX) 2024-12-18T00:16:40.6995140Z >>> import functools 2024-12-18T00:16:40.6995407Z >>> 2024-12-18T00:16:40.6995661Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-12-18T00:16:40.6996018Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-12-18T00:16:40.6996353Z >>> 2024-12-18T00:16:40.6996580Z >>> ops_to_save = [ 2024-12-18T00:16:40.6996863Z >>> torch.ops.aten.mm.default, 2024-12-18T00:16:40.6997178Z >>> ] 2024-12-18T00:16:40.6997384Z >>> 2024-12-18T00:16:40.6997632Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T00:16:40.6997983Z >>> if op in ops_to_save: 2024-12-18T00:16:40.6998306Z >>> return CheckpointPolicy.MUST_SAVE 2024-12-18T00:16:40.6998740Z >>> else: 2024-12-18T00:16:40.6999017Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-12-18T00:16:40.6999367Z >>> 2024-12-18T00:16:40.6999766Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T00:16:40.7000254Z >>> 2024-12-18T00:16:40.7000480Z >>> # or equivalently 2024-12-18T00:16:40.7000922Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-12-18T00:16:40.7001416Z >>> 2024-12-18T00:16:40.7001638Z >>> def fn(x, y): 2024-12-18T00:16:40.7002001Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-12-18T00:16:40.7002415Z >>> 2024-12-18T00:16:40.7002670Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T00:16:40.7003020Z >>> fn, x, y, 2024-12-18T00:16:40.7003287Z >>> use_reentrant=False, 2024-12-18T00:16:40.7003593Z >>> context_fn=context_fn, 2024-12-18T00:16:40.7003877Z >>> ) 2024-12-18T00:16:40.7004013Z 2024-12-18T00:16:40.7004267Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.7004648Z 2024-12-18T00:16:40.7187458Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=957. 2024-12-18T00:16:40.7188389Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.7188783Z 2024-12-18T00:16:40.7188932Z Create a :class:`setuptools.Extension` for C++. 2024-12-18T00:16:40.7189190Z 2024-12-18T00:16:40.7189447Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T00:16:40.7190039Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-12-18T00:16:40.7190382Z 2024-12-18T00:16:40.7190602Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T00:16:40.7191064Z constructor. Full list arguments can be found at 2024-12-18T00:16:40.7191664Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T00:16:40.7192116Z 2024-12-18T00:16:40.7192233Z .. note:: 2024-12-18T00:16:40.7192600Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T00:16:40.7193162Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T00:16:40.7193691Z the user's responsibility in their library to not use APIs from 2024-12-18T00:16:40.7194233Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T00:16:40.7195023Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T00:16:40.7195583Z example, to give access to custom ops from python, the library should 2024-12-18T00:16:40.7196282Z register the ops through the dispatcher. 2024-12-18T00:16:40.7196517Z 2024-12-18T00:16:40.7196624Z Example: 2024-12-18T00:16:40.7196845Z >>> # xdoctest: +SKIP 2024-12-18T00:16:40.7197175Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:40.7197563Z >>> from setuptools import setup 2024-12-18T00:16:40.7198110Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-12-18T00:16:40.7198670Z >>> setup( 2024-12-18T00:16:40.7198919Z ... name='extension', 2024-12-18T00:16:40.7199210Z ... ext_modules=[ 2024-12-18T00:16:40.7199486Z ... CppExtension( 2024-12-18T00:16:40.7199769Z ... name='extension', 2024-12-18T00:16:40.7200104Z ... sources=['extension.cpp'], 2024-12-18T00:16:40.7200460Z ... extra_compile_args=['-g'], 2024-12-18T00:16:40.7200852Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2024-12-18T00:16:40.7201216Z ... ], 2024-12-18T00:16:40.7201459Z ... cmdclass={ 2024-12-18T00:16:40.7201741Z ... 'build_ext': BuildExtension 2024-12-18T00:16:40.7202063Z ... }) 2024-12-18T00:16:40.7202194Z 2024-12-18T00:16:40.7202464Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.7202842Z 2024-12-18T00:16:40.7203414Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1019. 2024-12-18T00:16:40.7204644Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.7205043Z 2024-12-18T00:16:40.7205207Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-12-18T00:16:40.7205492Z 2024-12-18T00:16:40.7205736Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T00:16:40.7206310Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-12-18T00:16:40.7206873Z extension. This includes the CUDA include path, library path and runtime 2024-12-18T00:16:40.7207334Z library. 2024-12-18T00:16:40.7207458Z 2024-12-18T00:16:40.7207665Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T00:16:40.7208145Z constructor. Full list arguments can be found at 2024-12-18T00:16:40.7208735Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T00:16:40.7209189Z 2024-12-18T00:16:40.7209296Z .. note:: 2024-12-18T00:16:40.7209659Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T00:16:40.7210226Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T00:16:40.7210750Z the user's responsibility in their library to not use APIs from 2024-12-18T00:16:40.7211301Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T00:16:40.7211885Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T00:16:40.7212442Z example, to give access to custom ops from python, the library should 2024-12-18T00:16:40.7213087Z register the ops through the dispatcher. 2024-12-18T00:16:40.7213325Z 2024-12-18T00:16:40.7213452Z Example: 2024-12-18T00:16:40.7213709Z >>> # xdoctest: +SKIP 2024-12-18T00:16:40.7214039Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:40.7214424Z >>> from setuptools import setup 2024-12-18T00:16:40.7214869Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-12-18T00:16:40.7215308Z >>> setup( 2024-12-18T00:16:40.7215544Z ... name='cuda_extension', 2024-12-18T00:16:40.7215852Z ... ext_modules=[ 2024-12-18T00:16:40.7216136Z ... CUDAExtension( 2024-12-18T00:16:40.7216447Z ... name='cuda_extension', 2024-12-18T00:16:40.7216849Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T00:16:40.7217261Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T00:16:40.7217747Z ... 'nvcc': ['-O2']}, 2024-12-18T00:16:40.7218195Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2024-12-18T00:16:40.7218566Z ... ], 2024-12-18T00:16:40.7218798Z ... cmdclass={ 2024-12-18T00:16:40.7219066Z ... 'build_ext': BuildExtension 2024-12-18T00:16:40.7219386Z ... }) 2024-12-18T00:16:40.7219563Z 2024-12-18T00:16:40.7219703Z Compute capabilities: 2024-12-18T00:16:40.7219865Z 2024-12-18T00:16:40.7220178Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-12-18T00:16:40.7220891Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-12-18T00:16:40.7221603Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-12-18T00:16:40.7222313Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2024-12-18T00:16:40.7223036Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-12-18T00:16:40.7223571Z support (see below for details on PTX). 2024-12-18T00:16:40.7223795Z 2024-12-18T00:16:40.7224118Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-12-18T00:16:40.7224676Z CCs you want the extension to support: 2024-12-18T00:16:40.7224895Z 2024-12-18T00:16:40.7225100Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-12-18T00:16:40.7225632Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-12-18T00:16:40.7226006Z 2024-12-18T00:16:40.7226322Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-12-18T00:16:40.7227081Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-12-18T00:16:40.7227822Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-12-18T00:16:40.7228543Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-12-18T00:16:40.7229287Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-12-18T00:16:40.7229996Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-12-18T00:16:40.7230719Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-12-18T00:16:40.7231479Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-12-18T00:16:40.7232019Z "8.0 8.6" would be better. 2024-12-18T00:16:40.7232191Z 2024-12-18T00:16:40.7232500Z Note that while it's possible to include all supported archs, the more archs get included the 2024-12-18T00:16:40.7233212Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-12-18T00:16:40.7233622Z 2024-12-18T00:16:40.7233962Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-12-18T00:16:40.7234623Z To workaround the issue, move python binding logic to pure C++ file. 2024-12-18T00:16:40.7234959Z 2024-12-18T00:16:40.7235054Z Example use: 2024-12-18T00:16:40.7235299Z #include 2024-12-18T00:16:40.7235637Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-12-18T00:16:40.7235894Z 2024-12-18T00:16:40.7235999Z Instead of: 2024-12-18T00:16:40.7236233Z #include 2024-12-18T00:16:40.7236594Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-12-18T00:16:40.7236871Z 2024-12-18T00:16:40.7237145Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-12-18T00:16:40.7238045Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-12-18T00:16:40.7238765Z 2024-12-18T00:16:40.7238890Z Relocatable device code linking: 2024-12-18T00:16:40.7239139Z 2024-12-18T00:16:40.7239465Z If you want to reference device symbols across compilation units (across object files), 2024-12-18T00:16:40.7240132Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-12-18T00:16:40.7240863Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-12-18T00:16:40.7241743Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-12-18T00:16:40.7242521Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-12-18T00:16:40.7243162Z helps reduce the protentional perf degradation of `-rdc`. 2024-12-18T00:16:40.7243641Z Note that it needs to be used at both steps to be useful. 2024-12-18T00:16:40.7243923Z 2024-12-18T00:16:40.7244308Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-12-18T00:16:40.7244967Z There is also a case where `-dlink` is used without `-rdc`: 2024-12-18T00:16:40.7245521Z when an extension is linked against a static lib containing rdc-compiled objects 2024-12-18T00:16:40.7246111Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-12-18T00:16:40.7246445Z 2024-12-18T00:16:40.7246651Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-12-18T00:16:40.7246968Z 2024-12-18T00:16:40.7247074Z Example: 2024-12-18T00:16:40.7247308Z >>> # xdoctest: +SKIP 2024-12-18T00:16:40.7247625Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:40.7247994Z >>> CUDAExtension( 2024-12-18T00:16:40.7248272Z ... name='cuda_extension', 2024-12-18T00:16:40.7248651Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T00:16:40.7249032Z ... dlink=True, 2024-12-18T00:16:40.7249318Z ... dlink_libraries=["dlink_lib"], 2024-12-18T00:16:40.7249685Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T00:16:40.7250063Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-12-18T00:16:40.7250308Z 2024-12-18T00:16:40.7250578Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.7250950Z 2024-12-18T00:16:40.7251517Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1300. 2024-12-18T00:16:40.7252401Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.7252793Z 2024-12-18T00:16:40.7252940Z Load a PyTorch C++ extension just-in-time (JIT). 2024-12-18T00:16:40.7253195Z 2024-12-18T00:16:40.7253422Z To load an extension, a Ninja build file is emitted, which is used to 2024-12-18T00:16:40.7253962Z compile the given sources into a dynamic library. This library is 2024-12-18T00:16:40.7254519Z subsequently loaded into the current Python process as a module and 2024-12-18T00:16:40.7254980Z returned from this function, ready for use. 2024-12-18T00:16:40.7255231Z 2024-12-18T00:16:40.7255442Z By default, the directory to which the build file is emitted and the 2024-12-18T00:16:40.7256013Z resulting library compiled to is ``/torch_extensions/``, where 2024-12-18T00:16:40.7256587Z ```` is the temporary folder on the current platform and ```` 2024-12-18T00:16:40.7257136Z the name of the extension. This location can be overridden in two ways. 2024-12-18T00:16:40.7257681Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-12-18T00:16:40.7258232Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-12-18T00:16:40.7258794Z into subfolders of this directory. Second, if the ``build_directory`` 2024-12-18T00:16:40.7259364Z argument to this function is supplied, it overrides the entire path, i.e. 2024-12-18T00:16:40.7259879Z the library will be compiled into that folder directly. 2024-12-18T00:16:40.7260150Z 2024-12-18T00:16:40.7260373Z To compile the sources, the default system compiler (``c++``) is used, 2024-12-18T00:16:40.7261061Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-12-18T00:16:40.7261652Z additional arguments to the compilation process, ``extra_cflags`` or 2024-12-18T00:16:40.7262225Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-12-18T00:16:40.7262812Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-12-18T00:16:40.7263329Z ``extra_cflags`` to pass further include directories. 2024-12-18T00:16:40.7263596Z 2024-12-18T00:16:40.7263846Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-12-18T00:16:40.7264383Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-12-18T00:16:40.7264936Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-12-18T00:16:40.7265516Z passing the CUDA lib64 directory as a library directory, and linking 2024-12-18T00:16:40.7266005Z ``cudart``. You can pass additional flags to nvcc via 2024-12-18T00:16:40.7266490Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-12-18T00:16:40.7267055Z heuristics for finding the CUDA install directory are used, which usually 2024-12-18T00:16:40.7267616Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-12-18T00:16:40.7268050Z safest option. 2024-12-18T00:16:40.7268201Z 2024-12-18T00:16:40.7268290Z Args: 2024-12-18T00:16:40.7268630Z name: The name of the extension to build. This MUST be the same as the 2024-12-18T00:16:40.7269077Z name of the pybind11 module! 2024-12-18T00:16:40.7269486Z sources: A list of relative or absolute paths to C++ source files. 2024-12-18T00:16:40.7270036Z extra_cflags: optional list of compiler flags to forward to the build. 2024-12-18T00:16:40.7270605Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-12-18T00:16:40.7271058Z when building CUDA sources. 2024-12-18T00:16:40.7271489Z extra_ldflags: optional list of linker flags to forward to the build. 2024-12-18T00:16:40.7272063Z extra_include_paths: optional list of include directories to forward 2024-12-18T00:16:40.7272492Z to the build. 2024-12-18T00:16:40.7272836Z build_directory: optional path to use as build workspace. 2024-12-18T00:16:40.7273322Z verbose: If ``True``, turns on verbose logging of load steps. 2024-12-18T00:16:40.7274028Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T00:16:40.7274545Z the build. If set to ``None`` (default), this value is 2024-12-18T00:16:40.7275023Z automatically determined based on the existence of ``.cu`` or 2024-12-18T00:16:40.7275524Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-12-18T00:16:40.7275943Z and libraries to be included. 2024-12-18T00:16:40.7276369Z is_python_module: If ``True`` (default), imports the produced shared 2024-12-18T00:16:40.7276891Z library as a Python module. If ``False``, behavior depends on 2024-12-18T00:16:40.7277306Z ``is_standalone``. 2024-12-18T00:16:40.7277679Z is_standalone: If ``False`` (default) loads the constructed extension 2024-12-18T00:16:40.7278211Z into the process as a plain dynamic library. If ``True``, build a 2024-12-18T00:16:40.7278710Z standalone executable. 2024-12-18T00:16:40.7278899Z 2024-12-18T00:16:40.7279006Z Returns: 2024-12-18T00:16:40.7279253Z If ``is_python_module`` is ``True``: 2024-12-18T00:16:40.7279649Z Returns the loaded PyTorch extension as a Python module. 2024-12-18T00:16:40.7279954Z 2024-12-18T00:16:40.7280160Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-12-18T00:16:40.7280699Z Returns nothing. (The shared library is loaded into the process as 2024-12-18T00:16:40.7281133Z a side effect.) 2024-12-18T00:16:40.7281290Z 2024-12-18T00:16:40.7281412Z If ``is_standalone`` is ``True``. 2024-12-18T00:16:40.7281830Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-12-18T00:16:40.7282443Z added to the PATH environment variable as a side effect.) 2024-12-18T00:16:40.7282743Z 2024-12-18T00:16:40.7282833Z Example: 2024-12-18T00:16:40.7283066Z >>> # xdoctest: +SKIP 2024-12-18T00:16:40.7283384Z >>> from torch.utils.cpp_extension import load 2024-12-18T00:16:40.7283739Z >>> module = load( 2024-12-18T00:16:40.7284032Z ... name='extension', 2024-12-18T00:16:40.7284437Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T00:16:40.7284822Z ... extra_cflags=['-O2'], 2024-12-18T00:16:40.7285124Z ... verbose=True) 2024-12-18T00:16:40.7285287Z 2024-12-18T00:16:40.7285555Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.7285923Z 2024-12-18T00:16:40.7286466Z 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=1593. 2024-12-18T00:16:40.7287365Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.7287756Z 2024-12-18T00:16:40.7287967Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-12-18T00:16:40.7288305Z 2024-12-18T00:16:40.7288538Z This function behaves exactly like :func:`load`, but takes its sources as 2024-12-18T00:16:40.7289121Z strings rather than filenames. These strings are stored to files in the 2024-12-18T00:16:40.7289686Z build directory, after which the behavior of :func:`load_inline` is 2024-12-18T00:16:40.7290121Z identical to :func:`load`. 2024-12-18T00:16:40.7290298Z 2024-12-18T00:16:40.7290387Z See `the 2024-12-18T00:16:40.7290843Z tests `_ 2024-12-18T00:16:40.7291420Z for good examples of using this function. 2024-12-18T00:16:40.7291652Z 2024-12-18T00:16:40.7291894Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-12-18T00:16:40.7292510Z the necessary header includes, as well as the (pybind11) binding code. More 2024-12-18T00:16:40.7293127Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-12-18T00:16:40.7293674Z single ``.cpp`` file. This file is then prepended with ``#include 2024-12-18T00:16:40.7294090Z ``. 2024-12-18T00:16:40.7294267Z 2024-12-18T00:16:40.7294494Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-12-18T00:16:40.7295085Z automatically generated for each function specified. ``functions`` can 2024-12-18T00:16:40.7295667Z either be a list of function names, or a dictionary mapping from function 2024-12-18T00:16:40.7296231Z names to docstrings. If a list is given, the name of each function is used 2024-12-18T00:16:40.7296683Z as its docstring. 2024-12-18T00:16:40.7296846Z 2024-12-18T00:16:40.7297068Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-12-18T00:16:40.7297586Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-12-18T00:16:40.7298100Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-12-18T00:16:40.7298664Z separately, but ultimately linked into a single library. Note that no 2024-12-18T00:16:40.7299228Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-12-18T00:16:40.7299809Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-12-18T00:16:40.7300372Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-12-18T00:16:40.7300830Z include its name in ``functions``). 2024-12-18T00:16:40.7301037Z 2024-12-18T00:16:40.7301235Z See :func:`load` for a description of arguments omitted below. 2024-12-18T00:16:40.7301531Z 2024-12-18T00:16:40.7301635Z Args: 2024-12-18T00:16:40.7301969Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-12-18T00:16:40.7302533Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-12-18T00:16:40.7303129Z functions: A list of function names for which to generate function 2024-12-18T00:16:40.7303701Z bindings. If a dictionary is given, it should map function names to 2024-12-18T00:16:40.7304223Z docstrings (which are otherwise just the function names). 2024-12-18T00:16:40.7304754Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T00:16:40.7305285Z the build. If set to ``None`` (default), this value is 2024-12-18T00:16:40.7305800Z automatically determined based on whether ``cuda_sources`` is 2024-12-18T00:16:40.7306287Z provided. Set it to ``True`` to force CUDA headers 2024-12-18T00:16:40.7306673Z and libraries to be included. 2024-12-18T00:16:40.7307105Z with_pytorch_error_handling: Determines whether pytorch error and 2024-12-18T00:16:40.7307625Z warning macros are handled by pytorch instead of pybind. To do 2024-12-18T00:16:40.7308162Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-12-18T00:16:40.7308711Z function. This redirection might cause issues in obscure cases 2024-12-18T00:16:40.7309230Z of cpp. This flag should be set to ``False`` when this redirect 2024-12-18T00:16:40.7309636Z causes issues. 2024-12-18T00:16:40.7309797Z 2024-12-18T00:16:40.7309903Z Example: 2024-12-18T00:16:40.7310174Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:40.7310603Z >>> from torch.utils.cpp_extension import load_inline 2024-12-18T00:16:40.7310979Z >>> source = """ 2024-12-18T00:16:40.7311288Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-12-18T00:16:40.7311652Z return x.sin() + y.sin(); 2024-12-18T00:16:40.7311923Z } 2024-12-18T00:16:40.7312136Z """ 2024-12-18T00:16:40.7312411Z >>> module = load_inline(name='inline_extension', 2024-12-18T00:16:40.7312785Z ... cpp_sources=[source], 2024-12-18T00:16:40.7313143Z ... functions=['sin_add']) 2024-12-18T00:16:40.7313379Z 2024-12-18T00:16:40.7313473Z .. note:: 2024-12-18T00:16:40.7313845Z Since load_inline will just-in-time compile the source code, please ensure 2024-12-18T00:16:40.7314439Z that you have the right toolchains installed in the runtime. For example, 2024-12-18T00:16:40.7315014Z when loading C++, make sure a C++ compiler is available. If you're loading 2024-12-18T00:16:40.7315606Z a CUDA extension, you will need to additionally install the corresponding CUDA 2024-12-18T00:16:40.7316226Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2024-12-18T00:16:40.7316826Z are not included when you install torch and must be additionally installed. 2024-12-18T00:16:40.7317192Z 2024-12-18T00:16:40.7317444Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2024-12-18T00:16:40.7318047Z the extension. This may use up too many resources on some systems. One 2024-12-18T00:16:40.7318701Z can control the number of workers by setting the `MAX_JOBS` environment 2024-12-18T00:16:40.7319172Z variable to a non-negative number. 2024-12-18T00:16:40.7319389Z 2024-12-18T00:16:40.7319654Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.7320020Z 2024-12-18T00:16:40.7386080Z 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. 2024-12-18T00:16:40.7387063Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.7387454Z 2024-12-18T00:16:40.7387752Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-12-18T00:16:40.7388171Z 2024-12-18T00:16:40.7388469Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-12-18T00:16:40.7389148Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-12-18T00:16:40.7389756Z server like load. It can emulate multiple calling threads to a single module 2024-12-18T00:16:40.7390495Z provided. In the future we plan to enhance this component to support inter and 2024-12-18T00:16:40.7391101Z intra-op parallelism as well as multiple models running in a single process. 2024-12-18T00:16:40.7391472Z 2024-12-18T00:16:40.7391726Z Please note that even though nn.Module is supported, it might incur an overhead 2024-12-18T00:16:40.7392399Z from the need to hold GIL every time we execute Python code or pass around 2024-12-18T00:16:40.7392990Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-12-18T00:16:40.7393589Z model for inference deployment it is better to switch to using it in this 2024-12-18T00:16:40.7394041Z benchmark. 2024-12-18T00:16:40.7394168Z 2024-12-18T00:16:40.7394264Z Example:: 2024-12-18T00:16:40.7394414Z 2024-12-18T00:16:40.7394533Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:40.7394910Z >>> from torch.utils import ThroughputBenchmark 2024-12-18T00:16:40.7395305Z >>> bench = ThroughputBenchmark(my_module) 2024-12-18T00:16:40.7395715Z >>> # Pre-populate benchmark's data set with the inputs 2024-12-18T00:16:40.7396102Z >>> for input in inputs: 2024-12-18T00:16:40.7396503Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-12-18T00:16:40.7396981Z ... bench.add_input(input[0], x2=input[1]) 2024-12-18T00:16:40.7397431Z >>> # Inputs supplied above are randomly used during the execution 2024-12-18T00:16:40.7397861Z >>> stats = bench.benchmark( 2024-12-18T00:16:40.7398172Z ... num_calling_threads=4, 2024-12-18T00:16:40.7398531Z ... num_warmup_iters = 100, 2024-12-18T00:16:40.7398843Z ... num_iters = 1000, 2024-12-18T00:16:40.7407153Z ... ) 2024-12-18T00:16:40.7407645Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-12-18T00:16:40.7408126Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-12-18T00:16:40.7408437Z 2024-12-18T00:16:40.7408710Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.7409100Z 2024-12-18T00:16:40.8295518Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=17. 2024-12-18T00:16:40.8296527Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:40.8297145Z Sampler that restricts data loading to a subset of the dataset. 2024-12-18T00:16:40.8297472Z 2024-12-18T00:16:40.8297609Z It is especially useful in conjunction with 2024-12-18T00:16:40.8298116Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-12-18T00:16:40.8298751Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-12-18T00:16:40.8299356Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-12-18T00:16:40.8299852Z original dataset that is exclusive to it. 2024-12-18T00:16:40.8300107Z 2024-12-18T00:16:40.8300228Z .. note:: 2024-12-18T00:16:40.8300610Z Dataset is assumed to be of constant size and that any instance of it always 2024-12-18T00:16:40.8301120Z returns the same elements in the same order. 2024-12-18T00:16:40.8301370Z 2024-12-18T00:16:40.8301474Z Args: 2024-12-18T00:16:40.8301715Z dataset: Dataset used for sampling. 2024-12-18T00:16:40.8302180Z num_replicas (int, optional): Number of processes participating in 2024-12-18T00:16:40.8302777Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-12-18T00:16:40.8303271Z current distributed group. 2024-12-18T00:16:40.8303738Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-12-18T00:16:40.8304311Z By default, :attr:`rank` is retrieved from the current distributed 2024-12-18T00:16:40.8304725Z group. 2024-12-18T00:16:40.8305106Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-12-18T00:16:40.8305825Z indices. 2024-12-18T00:16:40.8306186Z seed (int, optional): random seed used to shuffle the sampler if 2024-12-18T00:16:40.8306704Z :attr:`shuffle=True`. This number should be identical across all 2024-12-18T00:16:40.8307180Z processes in the distributed group. Default: ``0``. 2024-12-18T00:16:40.8307792Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-12-18T00:16:40.8308337Z tail of the data to make it evenly divisible across the number of 2024-12-18T00:16:40.8308860Z replicas. If ``False``, the sampler will add extra indices to make 2024-12-18T00:16:40.8309399Z the data evenly divisible across the replicas. Default: ``False``. 2024-12-18T00:16:40.8309727Z 2024-12-18T00:16:40.8309839Z .. warning:: 2024-12-18T00:16:40.8310172Z In distributed mode, calling the :meth:`set_epoch` method at 2024-12-18T00:16:40.8310741Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-12-18T00:16:40.8311380Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-12-18T00:16:40.8311893Z the same ordering will be always used. 2024-12-18T00:16:40.8312126Z 2024-12-18T00:16:40.8312237Z Example:: 2024-12-18T00:16:40.8312370Z 2024-12-18T00:16:40.8312490Z >>> # xdoctest: +SKIP 2024-12-18T00:16:40.8312899Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-12-18T00:16:40.8313425Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-12-18T00:16:40.8313842Z ... sampler=sampler) 2024-12-18T00:16:40.8314219Z >>> for epoch in range(start_epoch, n_epochs): 2024-12-18T00:16:40.8314588Z ... if is_distributed: 2024-12-18T00:16:40.8314902Z ... sampler.set_epoch(epoch) 2024-12-18T00:16:40.8315238Z ... train(loader) 2024-12-18T00:16:40.8315520Z 2024-12-18T00:16:40.8315898Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:40.8316264Z 2024-12-18T00:16:41.0140506Z gathering tests 2024-12-18T00:16:41.0151331Z running 705 test(s) 2024-12-18T00:16:41.0179651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1045 <- wrt source file 2024-12-18T00:16:41.0186747Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2024-12-18T00:16:41.0187857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1081 <- wrt source file 2024-12-18T00:16:41.0191339Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2024-12-18T00:16:41.0192530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1150 <- wrt source file 2024-12-18T00:16:41.0193862Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2024-12-18T00:16:41.0195115Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1199 <- wrt source file 2024-12-18T00:16:41.0196511Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-12-18T00:16:41.0197798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1236 <- wrt source file 2024-12-18T00:16:41.0199111Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2024-12-18T00:16:41.0200365Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1391 <- wrt source file 2024-12-18T00:16:41.0201998Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-12-18T00:16:41.0203389Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2488 <- wrt source file 2024-12-18T00:16:41.0204653Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2024-12-18T00:16:41.0205988Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2744 <- wrt source file 2024-12-18T00:16:41.0207349Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-12-18T00:16:41.0208677Z * 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 2024-12-18T00:16:41.0210036Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2024-12-18T00:16:41.0211348Z * 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 2024-12-18T00:16:41.0212713Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2024-12-18T00:16:41.0213931Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-12-18T00:16:41.0215051Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2024-12-18T00:16:41.0216144Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-12-18T00:16:41.0217240Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2024-12-18T00:16:41.0218360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-12-18T00:16:41.0697468Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-12-18T00:16:41.0698777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-12-18T00:16:41.0700087Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-12-18T00:16:41.0701320Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 650 <- wrt source file 2024-12-18T00:16:41.0706136Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-12-18T00:16:41.0707465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 707 <- wrt source file 2024-12-18T00:16:41.0723796Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-12-18T00:16:41.0725101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1336 <- wrt source file 2024-12-18T00:16:41.0843063Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-12-18T00:16:41.0846635Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1381 <- wrt source file 2024-12-18T00:16:41.0850964Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-12-18T00:16:41.0852394Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1454 <- wrt source file 2024-12-18T00:16:41.0858132Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2024-12-18T00:16:41.0859431Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1484 <- wrt source file 2024-12-18T00:16:41.0863083Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-12-18T00:16:41.0864289Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-12-18T00:16:41.0882693Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-12-18T00:16:41.0883916Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-12-18T00:16:41.0888727Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2024-12-18T00:16:41.0889938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-12-18T00:16:41.0891663Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2024-12-18T00:16:41.0892860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-12-18T00:16:41.0903302Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2024-12-18T00:16:41.0904423Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 292 <- wrt source file 2024-12-18T00:16:41.0952136Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2024-12-18T00:16:41.0953342Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1019 <- wrt source file 2024-12-18T00:16:41.0963546Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-12-18T00:16:41.0973528Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1294 <- wrt source file 2024-12-18T00:16:41.0974940Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2024-12-18T00:16:41.0976105Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1378 <- wrt source file 2024-12-18T00:16:41.0980709Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2024-12-18T00:16:41.0981883Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1412 <- wrt source file 2024-12-18T00:16:41.0988920Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2024-12-18T00:16:41.0990052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1463 <- wrt source file 2024-12-18T00:16:41.1007258Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2024-12-18T00:16:41.1008401Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1504 <- wrt source file 2024-12-18T00:16:41.1022642Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2024-12-18T00:16:41.1023792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1540 <- wrt source file 2024-12-18T00:16:41.1038892Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2024-12-18T00:16:41.1040053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1578 <- wrt source file 2024-12-18T00:16:41.1059052Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2024-12-18T00:16:41.1060175Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1751 <- wrt source file 2024-12-18T00:16:41.1090204Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2024-12-18T00:16:41.1091351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1918 <- wrt source file 2024-12-18T00:16:41.1115247Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2024-12-18T00:16:41.1116540Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2018 <- wrt source file 2024-12-18T00:16:41.1117842Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2024-12-18T00:16:41.1119167Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2118 <- wrt source file 2024-12-18T00:16:41.1120316Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2024-12-18T00:16:41.1121386Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-12-18T00:16:41.1122512Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2024-12-18T00:16:41.1123519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-12-18T00:16:41.1124554Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2024-12-18T00:16:41.1125591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-12-18T00:16:41.1126674Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::_load_local:0 2024-12-18T00:16:41.1127789Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 135 <- wrt source file 2024-12-18T00:16:41.1128953Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2024-12-18T00:16:41.1130175Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 235 <- wrt source file 2024-12-18T00:16:41.1133750Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-12-18T00:16:41.1134985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 290 <- wrt source file 2024-12-18T00:16:41.1137909Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2024-12-18T00:16:41.1139019Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 483 <- wrt source file 2024-12-18T00:16:41.1148396Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2024-12-18T00:16:41.1149577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 550 <- wrt source file 2024-12-18T00:16:41.1157305Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2024-12-18T00:16:41.1158635Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 674 <- wrt source file 2024-12-18T00:16:41.1159832Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2024-12-18T00:16:41.1161032Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 995 <- wrt source file 2024-12-18T00:16:41.1252947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2024-12-18T00:16:41.1254133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1084 <- wrt source file 2024-12-18T00:16:41.1400433Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2024-12-18T00:16:41.1401646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-12-18T00:16:41.1406096Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2024-12-18T00:16:41.1407328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 417 <- wrt source file 2024-12-18T00:16:41.1438440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2024-12-18T00:16:41.1439703Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1570 <- wrt source file 2024-12-18T00:16:41.1441370Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2024-12-18T00:16:41.1442617Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1705 <- wrt source file 2024-12-18T00:16:41.1443989Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2024-12-18T00:16:41.1445256Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1953 <- wrt source file 2024-12-18T00:16:41.1469502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-12-18T00:16:41.1470750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1972 <- wrt source file 2024-12-18T00:16:41.1476368Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2024-12-18T00:16:41.1477652Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-12-18T00:16:41.1479081Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2024-12-18T00:16:41.1480346Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 289 <- wrt source file 2024-12-18T00:16:41.1481589Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2024-12-18T00:16:41.1482994Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 314 <- wrt source file 2024-12-18T00:16:41.1484504Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2024-12-18T00:16:41.1485876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 388 <- wrt source file 2024-12-18T00:16:41.1487125Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2024-12-18T00:16:41.1488405Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 460 <- wrt source file 2024-12-18T00:16:41.1489650Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2024-12-18T00:16:41.1490819Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 923 <- wrt source file 2024-12-18T00:16:41.1491970Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2024-12-18T00:16:41.1493126Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-12-18T00:16:41.1494334Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2024-12-18T00:16:41.1495591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 245 <- wrt source file 2024-12-18T00:16:41.1496935Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-12-18T00:16:41.1498170Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 277 <- wrt source file 2024-12-18T00:16:41.1499391Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2024-12-18T00:16:41.1500861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1778 <- wrt source file 2024-12-18T00:16:41.1502323Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-12-18T00:16:41.1503706Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 45 <- wrt source file 2024-12-18T00:16:41.1505344Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2024-12-18T00:16:41.1506621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 105 <- wrt source file 2024-12-18T00:16:41.1507874Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-12-18T00:16:41.1509139Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 159 <- wrt source file 2024-12-18T00:16:41.1965237Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2024-12-18T00:16:41.1966497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 345 <- wrt source file 2024-12-18T00:16:41.1967788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-12-18T00:16:41.1968993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 376 <- wrt source file 2024-12-18T00:16:41.1970513Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-12-18T00:16:41.1972044Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 397 <- wrt source file 2024-12-18T00:16:41.1973883Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-12-18T00:16:41.1975192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 409 <- wrt source file 2024-12-18T00:16:41.1976536Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0 2024-12-18T00:16:41.1977766Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 493 <- wrt source file 2024-12-18T00:16:41.1979107Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0 2024-12-18T00:16:41.1980312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 591 <- wrt source file 2024-12-18T00:16:41.1981591Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0 2024-12-18T00:16:41.1982876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-12-18T00:16:41.1984220Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-12-18T00:16:41.1985518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-12-18T00:16:41.1986822Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-12-18T00:16:41.1988053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-12-18T00:16:41.1989257Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-12-18T00:16:41.2089357Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 428 <- wrt source file 2024-12-18T00:16:41.2103024Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0 2024-12-18T00:16:41.2105371Z * 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 2024-12-18T00:16:41.2120354Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-12-18T00:16:41.2122755Z * 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 2024-12-18T00:16:41.2175109Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-12-18T00:16:41.2177467Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 254 <- wrt source file 2024-12-18T00:16:41.2179882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2024-12-18T00:16:41.2182394Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 287 <- wrt source file 2024-12-18T00:16:41.2185085Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-12-18T00:16:41.2187535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 896 <- wrt source file 2024-12-18T00:16:41.2471923Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-12-18T00:16:41.2513016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 474 <- wrt source file 2024-12-18T00:16:41.2566235Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-12-18T00:16:41.2568541Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2024-12-18T00:16:41.3022672Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-12-18T00:16:41.3026029Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2024-12-18T00:16:41.3079448Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-12-18T00:16:41.3081792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2024-12-18T00:16:41.3096234Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-12-18T00:16:41.3098615Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2024-12-18T00:16:41.3101143Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-12-18T00:16:41.3103564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1705 <- wrt source file 2024-12-18T00:16:41.3260709Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-12-18T00:16:41.3263122Z * 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 2024-12-18T00:16:41.3265622Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-12-18T00:16:41.3267962Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-12-18T00:16:41.3270256Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-12-18T00:16:41.3272900Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 112 <- wrt source file 2024-12-18T00:16:41.3276083Z * SKIPPED: 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file 2024-12-18T00:16:41.3291734Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-12-18T00:16:41.3293878Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 96 <- wrt source file 2024-12-18T00:16:41.3296066Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2024-12-18T00:16:41.3298512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 86 <- wrt source file 2024-12-18T00:16:41.3300994Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-12-18T00:16:41.3303626Z * 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 1348 <- wrt source file 2024-12-18T00:16:41.3306403Z * 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 2024-12-18T00:16:41.3308831Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 71 <- wrt source file 2024-12-18T00:16:41.3574686Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-12-18T00:16:41.3577102Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 200 <- wrt source file 2024-12-18T00:16:41.3650439Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-12-18T00:16:41.3653018Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 269 <- wrt source file 2024-12-18T00:16:41.3655614Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-12-18T00:16:41.3658130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 375 <- wrt source file 2024-12-18T00:16:41.3722006Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-12-18T00:16:41.3724555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 502 <- wrt source file 2024-12-18T00:16:41.3868489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-12-18T00:16:41.3871035Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 674 <- wrt source file 2024-12-18T00:16:41.4011547Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-12-18T00:16:41.4012929Z * 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 2024-12-18T00:16:41.4014351Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-12-18T00:16:41.4015735Z * 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 2024-12-18T00:16:41.4077169Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-12-18T00:16:41.4078564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 45 <- wrt source file 2024-12-18T00:16:41.4082528Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-12-18T00:16:41.4083937Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 425 <- wrt source file 2024-12-18T00:16:41.4085216Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2024-12-18T00:16:41.4086572Z * 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 2024-12-18T00:16:41.4124777Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-12-18T00:16:41.4126073Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 305 <- wrt source file 2024-12-18T00:16:41.4127418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-12-18T00:16:41.4128734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 996 <- wrt source file 2024-12-18T00:16:41.4173041Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-12-18T00:16:41.4174562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1061 <- wrt source file 2024-12-18T00:16:41.4175939Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-12-18T00:16:41.4177249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1282 <- wrt source file 2024-12-18T00:16:41.4191640Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-12-18T00:16:41.4193043Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1348 <- wrt source file 2024-12-18T00:16:41.4195234Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-12-18T00:16:41.4196628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1411 <- wrt source file 2024-12-18T00:16:41.4199667Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-12-18T00:16:41.4200954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1456 <- wrt source file 2024-12-18T00:16:41.4202214Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-12-18T00:16:41.4203449Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1566 <- wrt source file 2024-12-18T00:16:41.4205270Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-12-18T00:16:41.4206507Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-12-18T00:16:41.4207732Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-12-18T00:16:41.4208924Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-12-18T00:16:41.4210125Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-12-18T00:16:41.4211639Z * 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 2024-12-18T00:16:41.4213146Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-12-18T00:16:41.4214801Z * 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 2024-12-18T00:16:41.4216473Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-12-18T00:16:41.4218068Z * 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 2024-12-18T00:16:41.4219666Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-12-18T00:16:41.4221246Z * 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 2024-12-18T00:16:41.4222881Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-12-18T00:16:41.4224457Z * 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 2024-12-18T00:16:41.4226033Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-12-18T00:16:41.4227463Z * 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 2024-12-18T00:16:41.4231316Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-12-18T00:16:41.4232765Z * 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 2024-12-18T00:16:41.4262390Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-12-18T00:16:41.4264509Z * 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 2024-12-18T00:16:41.4265824Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-12-18T00:16:41.4267099Z * 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 2024-12-18T00:16:41.4268418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-12-18T00:16:41.4269698Z * 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 2024-12-18T00:16:41.4271000Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-12-18T00:16:41.4272305Z * 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 2024-12-18T00:16:41.4273856Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-12-18T00:16:41.4275234Z * 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 2024-12-18T00:16:41.4276779Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-12-18T00:16:41.4278242Z * 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 2024-12-18T00:16:41.4279770Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-12-18T00:16:41.4281173Z * 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 2024-12-18T00:16:41.4282599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-12-18T00:16:41.4284000Z * 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 2024-12-18T00:16:41.4285511Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-12-18T00:16:41.4286975Z * 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 2024-12-18T00:16:41.4288533Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-12-18T00:16:41.4290033Z * 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 2024-12-18T00:16:41.4291585Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-12-18T00:16:41.4293103Z * 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 2024-12-18T00:16:41.4294641Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-12-18T00:16:41.4296264Z * 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 2024-12-18T00:16:41.4298942Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-12-18T00:16:41.4301501Z * 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 2024-12-18T00:16:41.4304079Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-12-18T00:16:41.4306580Z * 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 2024-12-18T00:16:41.4309128Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-12-18T00:16:41.4311657Z * 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 2024-12-18T00:16:41.4314321Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-12-18T00:16:41.4316901Z * 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 2024-12-18T00:16:41.4319709Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-12-18T00:16:41.4322297Z * 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 2024-12-18T00:16:41.4324998Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-12-18T00:16:41.4327519Z * 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 2024-12-18T00:16:41.4330068Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-12-18T00:16:41.4332477Z * 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 2024-12-18T00:16:41.4334911Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-12-18T00:16:41.4337281Z * 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 2024-12-18T00:16:41.4339714Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-12-18T00:16:41.4342157Z * 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 2024-12-18T00:16:41.4344795Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-12-18T00:16:41.4347390Z * 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 2024-12-18T00:16:41.4350033Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-12-18T00:16:41.4352605Z * 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 2024-12-18T00:16:41.4355243Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-12-18T00:16:41.4357874Z * 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 2024-12-18T00:16:41.4360640Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-12-18T00:16:41.4363318Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 276 <- wrt source file 2024-12-18T00:16:41.4366092Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-12-18T00:16:41.4368894Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 24 <- wrt source file 2024-12-18T00:16:41.4371833Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-12-18T00:16:41.4374800Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 177 <- wrt source file 2024-12-18T00:16:41.4377680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-12-18T00:16:41.4380442Z * 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 2024-12-18T00:16:41.4382980Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-12-18T00:16:41.4385996Z * 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 2024-12-18T00:16:41.4389522Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-12-18T00:16:41.4392992Z * 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 2024-12-18T00:16:41.4396591Z * 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 2024-12-18T00:16:41.4400003Z * 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 2024-12-18T00:16:41.4403286Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-12-18T00:16:41.4406190Z * 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 2024-12-18T00:16:41.4408863Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-12-18T00:16:41.4411554Z * 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 2024-12-18T00:16:41.4414340Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-12-18T00:16:41.4416945Z * 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 2024-12-18T00:16:41.4419470Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-12-18T00:16:41.4422029Z * 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 2024-12-18T00:16:41.4424739Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-12-18T00:16:41.4427418Z * 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 2024-12-18T00:16:41.4430218Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-12-18T00:16:41.4432933Z * 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 2024-12-18T00:16:41.4435666Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-12-18T00:16:41.4438481Z * 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 2024-12-18T00:16:41.4441429Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-12-18T00:16:41.4444079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 93 <- wrt source file 2024-12-18T00:16:41.4446473Z * SKIPPED: 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2024-12-18T00:16:41.4492802Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-12-18T00:16:41.4495361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 225 <- wrt source file 2024-12-18T00:16:41.4498043Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-12-18T00:16:41.4500516Z * 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 2024-12-18T00:16:41.4503075Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-12-18T00:16:41.4505518Z * 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 2024-12-18T00:16:41.4508012Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-12-18T00:16:41.4510483Z * 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 2024-12-18T00:16:41.4513029Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-12-18T00:16:41.4515503Z * 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 2024-12-18T00:16:41.4518019Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-12-18T00:16:41.4520524Z * 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 2024-12-18T00:16:41.4523004Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-12-18T00:16:41.4525404Z * 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 2024-12-18T00:16:41.4527871Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-12-18T00:16:41.4530521Z * 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 2024-12-18T00:16:41.4533456Z * 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 2024-12-18T00:16:41.4536296Z * 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 2024-12-18T00:16:41.4539130Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-12-18T00:16:41.4541902Z * 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 2024-12-18T00:16:41.4544749Z * 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 2024-12-18T00:16:41.4547648Z * 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 459 <- wrt source file 2024-12-18T00:16:41.4550664Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-12-18T00:16:41.4553295Z * 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 2024-12-18T00:16:41.4555759Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2024-12-18T00:16:41.4558057Z * 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 2024-12-18T00:16:41.4560453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2024-12-18T00:16:41.4562691Z * 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 2024-12-18T00:16:41.4564985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2024-12-18T00:16:41.4567212Z * 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 2024-12-18T00:16:41.4569470Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2024-12-18T00:16:41.4571878Z * 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 2024-12-18T00:16:41.4574660Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2024-12-18T00:16:41.4577246Z * 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 2024-12-18T00:16:41.4579850Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2024-12-18T00:16:41.4582355Z * 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 2024-12-18T00:16:41.4584849Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2024-12-18T00:16:41.4587447Z * 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 2024-12-18T00:16:41.4590249Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2024-12-18T00:16:41.4592935Z * 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 2024-12-18T00:16:41.4595659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2024-12-18T00:16:41.4598082Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file 2024-12-18T00:16:41.4600339Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2024-12-18T00:16:41.4602498Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 294 <- wrt source file 2024-12-18T00:16:41.4604659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2024-12-18T00:16:41.4606784Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 396 <- wrt source file 2024-12-18T00:16:41.4608947Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2024-12-18T00:16:41.4611110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 631 <- wrt source file 2024-12-18T00:16:41.4613546Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2024-12-18T00:16:41.4615731Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 885 <- wrt source file 2024-12-18T00:16:41.4618055Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2024-12-18T00:16:41.4620202Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-12-18T00:16:41.4622363Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2024-12-18T00:16:41.4624488Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-12-18T00:16:41.4626663Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2024-12-18T00:16:41.4628763Z * 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 2024-12-18T00:16:41.4630954Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-12-18T00:16:41.4633131Z * 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 2024-12-18T00:16:41.4635394Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-12-18T00:16:41.4637640Z * 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 2024-12-18T00:16:41.4640059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2024-12-18T00:16:41.4642356Z * 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 2024-12-18T00:16:41.4644692Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2024-12-18T00:16:41.4646881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 62 <- wrt source file 2024-12-18T00:16:41.4649038Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2024-12-18T00:16:41.4651245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 119 <- wrt source file 2024-12-18T00:16:41.4653580Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2024-12-18T00:16:41.4655892Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 156 <- wrt source file 2024-12-18T00:16:41.4658296Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-12-18T00:16:41.4660622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 280 <- wrt source file 2024-12-18T00:16:41.4662967Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2024-12-18T00:16:41.4665186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 345 <- wrt source file 2024-12-18T00:16:41.4667365Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-12-18T00:16:41.4684921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 177 <- wrt source file 2024-12-18T00:16:41.4687135Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2024-12-18T00:16:41.4689367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0, line 714 <- wrt source file 2024-12-18T00:16:41.4691727Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0 2024-12-18T00:16:41.4693968Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 848 <- wrt source file 2024-12-18T00:16:41.4696151Z * SKIPPED: 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2024-12-18T00:16:41.4711610Z * 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 2024-12-18T00:16:41.4713829Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2024-12-18T00:16:41.4716158Z * 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 2024-12-18T00:16:41.4718708Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2024-12-18T00:16:41.4720961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2024-12-18T00:16:41.4723071Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2024-12-18T00:16:41.4725396Z * DOCTEST : 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: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3740 <- wrt source file 2024-12-18T00:16:41.4790313Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 2024-12-18T00:16:41.4792973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3865 <- wrt source file 2024-12-18T00:16:41.4795671Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0 2024-12-18T00:16:41.4798190Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 3967 <- wrt source file 2024-12-18T00:16:41.4800682Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0 2024-12-18T00:16:41.4803073Z * DOCTEST : 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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 30 <- wrt source file 2024-12-18T00:16:41.4867678Z * 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 2024-12-18T00:16:41.4870616Z * 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 2024-12-18T00:16:41.4873528Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0 2024-12-18T00:16:41.4876608Z * 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 2024-12-18T00:16:41.4879571Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0 2024-12-18T00:16:41.4961238Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1323 <- wrt source file 2024-12-18T00:16:41.4964199Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0 2024-12-18T00:16:41.4967141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1382 <- wrt source file 2024-12-18T00:16:41.4970089Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0 2024-12-18T00:16:41.4973017Z * DOCTEST : 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DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 237 <- wrt source file 2024-12-18T00:16:41.5114392Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0 2024-12-18T00:16:41.5117192Z * 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 2024-12-18T00:16:41.5119986Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0 2024-12-18T00:16:41.5122681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 49 <- wrt source file 2024-12-18T00:16:41.5125413Z * SKIPPED: 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file 2024-12-18T00:16:41.5157813Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-12-18T00:16:41.5160112Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 20 <- wrt source file 2024-12-18T00:16:41.5162273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2024-12-18T00:16:41.5164479Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file 2024-12-18T00:16:41.5166804Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0 2024-12-18T00:16:41.5169148Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-12-18T00:16:41.5171620Z * SUCCESS: 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2024-12-18T00:16:41.6053547Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file 2024-12-18T00:16:41.6065442Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2024-12-18T00:16:41.6067711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file 2024-12-18T00:16:41.6071697Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2024-12-18T00:16:41.6073973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3466 <- wrt source file 2024-12-18T00:16:41.6080819Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2024-12-18T00:16:41.6083090Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-12-18T00:16:41.6110784Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2024-12-18T00:16:41.6112795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-12-18T00:16:41.6115492Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-12-18T00:16:41.6117512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-12-18T00:16:41.6123095Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2024-12-18T00:16:41.6125122Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-12-18T00:16:41.6127742Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-12-18T00:16:41.6129763Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-12-18T00:16:41.6159844Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2024-12-18T00:16:41.6162546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-12-18T00:16:41.6180632Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-12-18T00:16:41.6183015Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-12-18T00:16:41.6185378Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0 2024-12-18T00:16:41.6187606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-12-18T00:16:41.6189604Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2024-12-18T00:16:41.6191647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-12-18T00:16:41.6193616Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2024-12-18T00:16:41.6195605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-12-18T00:16:41.6197681Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2024-12-18T00:16:41.6199727Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-12-18T00:16:41.6201742Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2024-12-18T00:16:41.6203660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-12-18T00:16:41.6205589Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2024-12-18T00:16:41.6207479Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-12-18T00:16:41.6209426Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2024-12-18T00:16:41.6211304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-12-18T00:16:41.6213209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2024-12-18T00:16:41.6215102Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-12-18T00:16:41.6217060Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2024-12-18T00:16:41.6219059Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-12-18T00:16:41.6221155Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-12-18T00:16:41.6223220Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-12-18T00:16:41.6225293Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2024-12-18T00:16:41.6227360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-12-18T00:16:41.6229472Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-12-18T00:16:41.6231558Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-12-18T00:16:41.6233700Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-12-18T00:16:41.6235745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-12-18T00:16:41.6237796Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2024-12-18T00:16:41.6239904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-12-18T00:16:41.6241851Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2024-12-18T00:16:41.6244062Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 103 <- wrt source file 2024-12-18T00:16:41.6246388Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-12-18T00:16:41.6248626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-12-18T00:16:41.6250882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-12-18T00:16:41.6253124Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-12-18T00:16:41.6255440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-12-18T00:16:41.6257648Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-12-18T00:16:41.6259853Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-12-18T00:16:41.6262033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-12-18T00:16:41.6264268Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-12-18T00:16:41.6266484Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-12-18T00:16:41.6268794Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-12-18T00:16:41.6271017Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-12-18T00:16:41.6273261Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-12-18T00:16:41.6275632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-12-18T00:16:41.6277893Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-12-18T00:16:41.6280219Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-12-18T00:16:41.6282599Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-12-18T00:16:41.6284843Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-12-18T00:16:41.6287052Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-12-18T00:16:41.6289210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-12-18T00:16:41.6291413Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-12-18T00:16:41.6293574Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-12-18T00:16:41.6295915Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2024-12-18T00:16:41.6298140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-12-18T00:16:41.6300552Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-12-18T00:16:41.6302755Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-12-18T00:16:41.6304930Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2024-12-18T00:16:41.6307103Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-12-18T00:16:41.6309319Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2024-12-18T00:16:41.6311487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-12-18T00:16:41.6313701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2024-12-18T00:16:41.6315847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-12-18T00:16:41.6318038Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2024-12-18T00:16:41.6320248Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-12-18T00:16:41.6322462Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2024-12-18T00:16:41.6324708Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-12-18T00:16:41.6327058Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-12-18T00:16:41.6329352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-12-18T00:16:41.6331656Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-12-18T00:16:41.6333945Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-12-18T00:16:41.6336301Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-12-18T00:16:41.6338580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-12-18T00:16:41.6340875Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-12-18T00:16:41.6343129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-12-18T00:16:41.6345472Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-12-18T00:16:41.6347896Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-12-18T00:16:41.6350658Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-12-18T00:16:41.6353477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-12-18T00:16:41.6356113Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-12-18T00:16:41.6358623Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-12-18T00:16:41.6360940Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-12-18T00:16:41.6363196Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-12-18T00:16:41.6365564Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-12-18T00:16:41.6367832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-12-18T00:16:41.6370115Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-12-18T00:16:41.6372342Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-12-18T00:16:41.6374741Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-12-18T00:16:41.6377008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-12-18T00:16:41.6379343Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-12-18T00:16:41.6381655Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-12-18T00:16:41.6384005Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-12-18T00:16:41.6386285Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-12-18T00:16:41.6388612Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-12-18T00:16:41.6390884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-12-18T00:16:41.6590468Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-12-18T00:16:41.6593243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-12-18T00:16:41.8972794Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-12-18T00:16:41.9128411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-12-18T00:16:41.9147994Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-12-18T00:16:41.9150377Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-12-18T00:16:41.9152704Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2024-12-18T00:16:41.9155330Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-12-18T00:16:41.9157653Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-12-18T00:16:41.9160108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file 2024-12-18T00:16:41.9162439Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-12-18T00:16:41.9164744Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file 2024-12-18T00:16:41.9167139Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-12-18T00:16:41.9169496Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file 2024-12-18T00:16:41.9171897Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-12-18T00:16:41.9174403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-12-18T00:16:41.9176865Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-12-18T00:16:41.9179259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-12-18T00:16:41.9181673Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-12-18T00:16:41.9183926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-12-18T00:16:41.9186125Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-12-18T00:16:41.9188329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-12-18T00:16:41.9190597Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-12-18T00:16:41.9192810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-12-18T00:16:41.9204277Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-12-18T00:16:41.9206514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-12-18T00:16:41.9281018Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-12-18T00:16:41.9283667Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-12-18T00:16:41.9286188Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-12-18T00:16:41.9288565Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-12-18T00:16:41.9360708Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-12-18T00:16:41.9363835Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-12-18T00:16:41.9366139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-12-18T00:16:41.9368490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-12-18T00:16:41.9371072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2024-12-18T00:16:41.9373234Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-12-18T00:16:41.9385763Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-12-18T00:16:41.9388046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-12-18T00:16:41.9399038Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-12-18T00:16:41.9401881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-12-18T00:16:41.9582653Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-12-18T00:16:41.9585512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-12-18T00:16:42.1976967Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-12-18T00:16:42.2130403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-12-18T00:16:42.2132754Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-12-18T00:16:42.2134976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-12-18T00:16:42.2139122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2024-12-18T00:16:42.2141243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-12-18T00:16:42.2147403Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2024-12-18T00:16:42.2149552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-12-18T00:16:42.2167578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-12-18T00:16:42.2169689Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-12-18T00:16:42.2176150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-12-18T00:16:42.2178235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-12-18T00:16:42.2202583Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-12-18T00:16:42.2205286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-12-18T00:16:42.2209753Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-12-18T00:16:42.2212008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-12-18T00:16:42.2224605Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-12-18T00:16:42.2226810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 519 <- wrt source file 2024-12-18T00:16:42.2233356Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-12-18T00:16:42.2235481Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 597 <- wrt source file 2024-12-18T00:16:42.2239949Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-12-18T00:16:42.2242049Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 679 <- wrt source file 2024-12-18T00:16:42.2246418Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-12-18T00:16:42.2248637Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 750 <- wrt source file 2024-12-18T00:16:42.2258067Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-12-18T00:16:42.2260402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 943 <- wrt source file 2024-12-18T00:16:42.2266210Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-12-18T00:16:42.2268554Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1263 <- wrt source file 2024-12-18T00:16:42.2275108Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-12-18T00:16:42.2277482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1403 <- wrt source file 2024-12-18T00:16:42.2284557Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-12-18T00:16:42.2286902Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1468 <- wrt source file 2024-12-18T00:16:42.2291755Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-12-18T00:16:42.2294072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1547 <- wrt source file 2024-12-18T00:16:42.2300122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-12-18T00:16:42.2302482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1647 <- wrt source file 2024-12-18T00:16:42.2311503Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-12-18T00:16:42.2313722Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1888 <- wrt source file 2024-12-18T00:16:42.2337135Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-12-18T00:16:42.2339591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 548 <- wrt source file 2024-12-18T00:16:42.2342164Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-12-18T00:16:42.2344586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1008 <- wrt source file 2024-12-18T00:16:42.2352051Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-12-18T00:16:42.2354265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1262 <- wrt source file 2024-12-18T00:16:42.2359089Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2024-12-18T00:16:42.2361341Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2180 <- wrt source file 2024-12-18T00:16:42.2363734Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-12-18T00:16:42.2366092Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2622 <- wrt source file 2024-12-18T00:16:42.2368496Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-12-18T00:16:42.2370920Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2650 <- wrt source file 2024-12-18T00:16:42.2373444Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-12-18T00:16:42.2375993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2677 <- wrt source file 2024-12-18T00:16:42.2378322Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-12-18T00:16:42.2380657Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2704 <- wrt source file 2024-12-18T00:16:42.2383110Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-12-18T00:16:42.2385519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2735 <- wrt source file 2024-12-18T00:16:42.2388055Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-12-18T00:16:42.2390406Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2759 <- wrt source file 2024-12-18T00:16:42.2392711Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-12-18T00:16:42.2394994Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2797 <- wrt source file 2024-12-18T00:16:42.2397411Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-12-18T00:16:42.2399799Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-12-18T00:16:42.2408322Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-12-18T00:16:42.2411365Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-12-18T00:16:42.2417705Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-12-18T00:16:42.2420527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-12-18T00:16:42.2427710Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-12-18T00:16:42.2430352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-12-18T00:16:42.2434685Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-12-18T00:16:42.2436860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-12-18T00:16:42.2442870Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-12-18T00:16:42.2445079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-12-18T00:16:42.2463553Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-12-18T00:16:42.2465812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-12-18T00:16:42.8344527Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-12-18T00:16:42.8624742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-12-18T00:16:42.8634956Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-12-18T00:16:42.8637211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-12-18T00:16:42.8642927Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-12-18T00:16:42.8645244Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-12-18T00:16:42.8667881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-12-18T00:16:42.8670292Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-12-18T00:16:42.8676484Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-12-18T00:16:42.8678837Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-12-18T00:16:42.8684206Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-12-18T00:16:42.8686441Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-12-18T00:16:42.8689504Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-12-18T00:16:42.8691926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-12-18T00:16:42.8697621Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-12-18T00:16:42.8699962Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-12-18T00:16:42.8705110Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-12-18T00:16:42.8707438Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-12-18T00:16:43.3646233Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-12-18T00:16:43.3928499Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-12-18T00:16:43.3937600Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-12-18T00:16:43.3939844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-12-18T00:16:43.3944788Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-12-18T00:16:43.3947062Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-12-18T00:16:43.3967026Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-12-18T00:16:43.3969304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-12-18T00:16:43.3972976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-12-18T00:16:43.3975575Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-12-18T00:16:43.3978399Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-12-18T00:16:43.3980742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-12-18T00:16:43.3984708Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-12-18T00:16:43.3986946Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-12-18T00:16:43.4029053Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-12-18T00:16:43.4031302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-12-18T00:16:43.5964293Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-12-18T00:16:43.6020756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-12-18T00:16:43.6031501Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-12-18T00:16:43.6034091Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-12-18T00:16:43.6750835Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-12-18T00:16:43.6753158Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-12-18T00:16:43.6758622Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-12-18T00:16:43.6760820Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-12-18T00:16:43.6796316Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-12-18T00:16:43.6798595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-12-18T00:16:43.8442824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-12-18T00:16:43.8500602Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-12-18T00:16:43.8545003Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-12-18T00:16:43.8547468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-12-18T00:16:43.9193113Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-12-18T00:16:43.9195468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-12-18T00:16:43.9200725Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-12-18T00:16:43.9202933Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-12-18T00:16:43.9247082Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-12-18T00:16:43.9250202Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-12-18T00:16:44.1166870Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-12-18T00:16:44.1224149Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-12-18T00:16:44.1230759Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-12-18T00:16:44.1233270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-12-18T00:16:44.1240045Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-12-18T00:16:44.1242475Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-12-18T00:16:44.1265151Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-12-18T00:16:44.1330957Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-12-18T00:16:44.1334868Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-12-18T00:16:44.1337468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-12-18T00:16:44.1342949Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-12-18T00:16:44.1345369Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-12-18T00:16:44.1362826Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-12-18T00:16:44.1365046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 591 <- wrt source file 2024-12-18T00:16:44.1374978Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-12-18T00:16:44.1377024Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 948 <- wrt source file 2024-12-18T00:16:44.1731970Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-12-18T00:16:44.1734683Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file 2024-12-18T00:16:44.1747344Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-12-18T00:16:44.1749458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1536 <- wrt source file 2024-12-18T00:16:44.1758456Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-12-18T00:16:44.1760572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1658 <- wrt source file 2024-12-18T00:16:44.1768890Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-12-18T00:16:44.1771001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1772 <- wrt source file 2024-12-18T00:16:44.1782377Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-12-18T00:16:44.1784523Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-12-18T00:16:44.1795798Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-12-18T00:16:44.1798158Z * 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 2024-12-18T00:16:44.1801160Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-12-18T00:16:44.1803712Z * 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 2024-12-18T00:16:44.1807500Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-12-18T00:16:44.1809979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 86 <- wrt source file 2024-12-18T00:16:44.6771379Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-12-18T00:16:44.6786983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 254 <- wrt source file 2024-12-18T00:16:44.6789602Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-12-18T00:16:44.6792139Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 319 <- wrt source file 2024-12-18T00:16:44.7253389Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-12-18T00:16:44.7293912Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 532 <- wrt source file 2024-12-18T00:16:44.8163126Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-12-18T00:16:44.8170000Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 653 <- wrt source file 2024-12-18T00:16:44.8363292Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-12-18T00:16:44.8365987Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 957 <- wrt source file 2024-12-18T00:16:44.8665607Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-12-18T00:16:44.8668112Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-12-18T00:16:44.8689444Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-12-18T00:16:44.8691885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-12-18T00:16:44.8700824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-12-18T00:16:44.8703375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-12-18T00:16:44.8708516Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-12-18T00:16:44.8711012Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 126 <- wrt source file 2024-12-18T00:16:44.8713483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-12-18T00:16:44.8716054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-12-18T00:16:44.8718824Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-12-18T00:16:44.8721560Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1418 <- wrt source file 2024-12-18T00:16:44.8724424Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-12-18T00:16:44.8727723Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1981 <- wrt source file 2024-12-18T00:16:44.8730956Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-12-18T00:16:44.8733988Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1991 <- wrt source file 2024-12-18T00:16:44.8737072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-12-18T00:16:44.8740194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2026 <- wrt source file 2024-12-18T00:16:44.8743436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-12-18T00:16:44.8746261Z * 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 2024-12-18T00:16:44.8748879Z * 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 2024-12-18T00:16:44.8751230Z * 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 2024-12-18T00:16:44.8753329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2024-12-18T00:16:44.8755630Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-12-18T00:16:44.8758093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-12-18T00:16:44.8760564Z * 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 2024-12-18T00:16:44.8763054Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-12-18T00:16:44.8765506Z * 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 2024-12-18T00:16:44.8768022Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-12-18T00:16:44.8770578Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 506 <- wrt source file 2024-12-18T00:16:44.8773207Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-12-18T00:16:44.8775680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file 2024-12-18T00:16:44.8777834Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2024-12-18T00:16:44.8780060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 881 <- wrt source file 2024-12-18T00:16:44.8782437Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-12-18T00:16:44.8784834Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 924 <- wrt source file 2024-12-18T00:16:44.8787163Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-12-18T00:16:44.8789472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file 2024-12-18T00:16:44.8791587Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2024-12-18T00:16:44.8793664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1219 <- wrt source file 2024-12-18T00:16:44.8795813Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-12-18T00:16:44.8797996Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 360 <- wrt source file 2024-12-18T00:16:44.8800353Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-12-18T00:16:44.8802543Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 438 <- wrt source file 2024-12-18T00:16:44.8804691Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-12-18T00:16:44.8806830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 496 <- wrt source file 2024-12-18T00:16:44.8809025Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-12-18T00:16:44.8811154Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 552 <- wrt source file 2024-12-18T00:16:44.8813329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-12-18T00:16:44.8815484Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 580 <- wrt source file 2024-12-18T00:16:44.8817708Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-12-18T00:16:44.8819982Z * 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 2024-12-18T00:16:44.8822389Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-12-18T00:16:44.8824813Z * 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 2024-12-18T00:16:44.8827907Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-12-18T00:16:44.8830327Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 214 <- wrt source file 2024-12-18T00:16:44.8832691Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-12-18T00:16:44.8834966Z * 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 2024-12-18T00:16:44.8838715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-12-18T00:16:44.8841052Z * 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 2024-12-18T00:16:44.8843815Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-12-18T00:16:44.8846363Z * 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 2024-12-18T00:16:44.8848951Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-12-18T00:16:44.8851805Z * 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 2024-12-18T00:16:44.8868767Z * 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 2024-12-18T00:16:44.8871439Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 309 <- wrt source file 2024-12-18T00:16:44.8873814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-12-18T00:16:44.8876091Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 411 <- wrt source file 2024-12-18T00:16:44.8878543Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-12-18T00:16:44.8880777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 511 <- wrt source file 2024-12-18T00:16:44.8882958Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-12-18T00:16:44.8885154Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 571 <- wrt source file 2024-12-18T00:16:44.8887429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-12-18T00:16:44.8889655Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 636 <- wrt source file 2024-12-18T00:16:44.8891894Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-12-18T00:16:44.8894075Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 714 <- wrt source file 2024-12-18T00:16:44.8896300Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2024-12-18T00:16:44.8898525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 847 <- wrt source file 2024-12-18T00:16:44.8900825Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-12-18T00:16:44.8903082Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 996 <- wrt source file 2024-12-18T00:16:44.8905384Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-12-18T00:16:44.8907709Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1152 <- wrt source file 2024-12-18T00:16:44.8910113Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-12-18T00:16:44.8912627Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1295 <- wrt source file 2024-12-18T00:16:44.8915084Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-12-18T00:16:44.8917460Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1543 <- wrt source file 2024-12-18T00:16:44.8919734Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-12-18T00:16:44.8922197Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1813 <- wrt source file 2024-12-18T00:16:44.8924944Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-12-18T00:16:44.8927641Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1829 <- wrt source file 2024-12-18T00:16:44.8930391Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-12-18T00:16:44.8932846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1973 <- wrt source file 2024-12-18T00:16:44.8935102Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-12-18T00:16:44.8937241Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 334 <- wrt source file 2024-12-18T00:16:44.9026614Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-12-18T00:16:44.9028844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 364 <- wrt source file 2024-12-18T00:16:44.9031088Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-12-18T00:16:44.9033283Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 399 <- wrt source file 2024-12-18T00:16:44.9035497Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-12-18T00:16:44.9037698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 434 <- wrt source file 2024-12-18T00:16:44.9040031Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-12-18T00:16:44.9042207Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 471 <- wrt source file 2024-12-18T00:16:44.9044360Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-12-18T00:16:44.9046585Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 847 <- wrt source file 2024-12-18T00:16:44.9048916Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-12-18T00:16:44.9051069Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 960 <- wrt source file 2024-12-18T00:16:44.9053156Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2024-12-18T00:16:44.9055557Z * 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 2024-12-18T00:16:44.9058291Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-12-18T00:16:44.9061122Z * 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 2024-12-18T00:16:44.9064098Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-12-18T00:16:44.9066828Z * 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 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-12-18T00:16:44.9435583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 304 <- wrt source file 2024-12-18T00:16:44.9437913Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-12-18T00:16:44.9440342Z * 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 2024-12-18T00:16:44.9442845Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-12-18T00:16:44.9445209Z * 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 2024-12-18T00:16:44.9447547Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-12-18T00:16:44.9449895Z * 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 2024-12-18T00:16:44.9452401Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-12-18T00:16:44.9454905Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 96 <- wrt source file 2024-12-18T00:16:44.9457501Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-12-18T00:16:44.9460025Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 263 <- wrt source file 2024-12-18T00:16:44.9462761Z * SKIPPED: 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2024-12-18T00:16:44.9480385Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-12-18T00:16:44.9483305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 48 <- wrt source file 2024-12-18T00:16:44.9486255Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-12-18T00:16:44.9489114Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 98 <- wrt source file 2024-12-18T00:16:44.9491999Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-12-18T00:16:44.9494785Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 317 <- wrt source file 2024-12-18T00:16:44.9497580Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-12-18T00:16:44.9500464Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 403 <- wrt source file 2024-12-18T00:16:44.9503513Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-12-18T00:16:44.9506487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 613 <- wrt source file 2024-12-18T00:16:44.9509501Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-12-18T00:16:44.9512531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 681 <- wrt source file 2024-12-18T00:16:44.9515443Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2024-12-18T00:16:44.9518466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2024-12-18T00:16:44.9521471Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2024-12-18T00:16:44.9524556Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 34 <- wrt source file 2024-12-18T00:16:44.9527604Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-12-18T00:16:44.9530538Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 62 <- wrt source file 2024-12-18T00:16:44.9533433Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-12-18T00:16:44.9536302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 122 <- wrt source file 2024-12-18T00:16:44.9539223Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-12-18T00:16:44.9542083Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 189 <- wrt source file 2024-12-18T00:16:44.9544973Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-12-18T00:16:44.9547788Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 36 <- wrt source file 2024-12-18T00:16:44.9550658Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-12-18T00:16:44.9553620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 24 <- wrt source file 2024-12-18T00:16:44.9556733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-12-18T00:16:44.9560211Z * 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 2024-12-18T00:16:44.9563221Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2024-12-18T00:16:44.9566074Z * 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 2024-12-18T00:16:44.9568906Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2024-12-18T00:16:44.9571793Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 33 <- wrt source file 2024-12-18T00:16:44.9574942Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-12-18T00:16:44.9577817Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 28 <- wrt source file 2024-12-18T00:16:44.9580705Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-12-18T00:16:44.9583522Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 72 <- wrt source file 2024-12-18T00:16:44.9586473Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-12-18T00:16:44.9589316Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 28 <- wrt source file 2024-12-18T00:16:44.9593090Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-12-18T00:16:44.9595979Z * 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 2024-12-18T00:16:44.9598977Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-12-18T00:16:44.9601783Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 36 <- wrt source file 2024-12-18T00:16:44.9604587Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-12-18T00:16:44.9607314Z * 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 2024-12-18T00:16:44.9635514Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-12-18T00:16:44.9638182Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2024-12-18T00:16:44.9640875Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-12-18T00:16:44.9643493Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2024-12-18T00:16:44.9646150Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-12-18T00:16:44.9648783Z * 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 2024-12-18T00:16:44.9651445Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-12-18T00:16:44.9654107Z * 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 2024-12-18T00:16:44.9656837Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-12-18T00:16:44.9659512Z * 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 2024-12-18T00:16:44.9662211Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-12-18T00:16:44.9664888Z * 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 2024-12-18T00:16:44.9667619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-12-18T00:16:44.9670271Z * 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 2024-12-18T00:16:44.9673138Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-12-18T00:16:44.9675986Z * 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 2024-12-18T00:16:44.9678960Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-12-18T00:16:44.9681753Z * 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 2024-12-18T00:16:44.9684602Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-12-18T00:16:44.9687334Z * 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 2024-12-18T00:16:44.9690035Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-12-18T00:16:44.9692704Z * 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 2024-12-18T00:16:44.9695398Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-12-18T00:16:44.9698036Z * 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 2024-12-18T00:16:44.9700710Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-12-18T00:16:44.9703396Z * 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 2024-12-18T00:16:44.9706172Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-12-18T00:16:44.9708884Z * 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 2024-12-18T00:16:44.9711628Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-12-18T00:16:44.9714558Z * 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 2024-12-18T00:16:44.9717724Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-12-18T00:16:44.9720835Z * 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 2024-12-18T00:16:44.9723938Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-12-18T00:16:44.9726878Z * 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 2024-12-18T00:16:44.9729750Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-12-18T00:16:44.9732488Z * 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 2024-12-18T00:16:44.9735293Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-12-18T00:16:44.9736666Z ============ 2024-12-18T00:16:44.9737128Z Finished doctests 2024-12-18T00:16:44.9737549Z 338 / 705 passed 2024-12-18T00:16:44.9737994Z  2024-12-18T00:16:44.9738508Z === Found 105 parse-time warnings === 2024-12-18T00:16:44.9739242Z --- Parse Warning: 1 / 105 --- 2024-12-18T00:16:44.9741233Z /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=1496. 2024-12-18T00:16:44.9743508Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:44.9744327Z 2024-12-18T00:16:44.9744735Z dim_order(ambiguity_check=False) -> tuple 2024-12-18T00:16:44.9745289Z 2024-12-18T00:16:44.9745896Z Returns the uniquely determined tuple of int describing the dim order or 2024-12-18T00:16:44.9746724Z physical layout of :attr:`self`. 2024-12-18T00:16:44.9747213Z 2024-12-18T00:16:44.9747744Z The dim order represents how dimensions are laid out in memory, 2024-12-18T00:16:44.9748598Z starting from the outermost to the innermost dimension. 2024-12-18T00:16:44.9749255Z 2024-12-18T00:16:44.9749775Z Note that the dim order may not always be uniquely determined. 2024-12-18T00:16:44.9751014Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2024-12-18T00:16:44.9752667Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2024-12-18T00:16:44.9754067Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2024-12-18T00:16:44.9755344Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2024-12-18T00:16:44.9756344Z Otherwise, it will raise TypeError. 2024-12-18T00:16:44.9756873Z 2024-12-18T00:16:44.9757206Z Args: 2024-12-18T00:16:44.9757992Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2024-12-18T00:16:44.9758989Z 2024-12-18T00:16:44.9759360Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2024-12-18T00:16:44.9759900Z (0, 1, 2, 3) 2024-12-18T00:16:44.9760417Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2024-12-18T00:16:44.9761045Z (0, 2, 1, 3) 2024-12-18T00:16:44.9761653Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2024-12-18T00:16:44.9762374Z (0, 2, 3, 1) 2024-12-18T00:16:44.9762812Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2024-12-18T00:16:44.9763352Z (0, 1, 2, 3) 2024-12-18T00:16:44.9763742Z >>> try: 2024-12-18T00:16:44.9764275Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2024-12-18T00:16:44.9764969Z ... except RuntimeError as e: 2024-12-18T00:16:44.9765491Z ... print(e) 2024-12-18T00:16:44.9766334Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2024-12-18T00:16:44.9767327Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2024-12-18T00:16:44.9768105Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2024-12-18T00:16:44.9768903Z ... ) # It can be mapped to contiguous format 2024-12-18T00:16:44.9769472Z (0, 1, 2, 3) 2024-12-18T00:16:44.9769866Z >>> try: 2024-12-18T00:16:44.9770414Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2024-12-18T00:16:44.9771141Z ... except TypeError as e: 2024-12-18T00:16:44.9771631Z ... print(e) 2024-12-18T00:16:44.9772417Z The ambiguity_check argument must be a bool or a list of memory formats. 2024-12-18T00:16:44.9773210Z .. warning:: 2024-12-18T00:16:44.9773911Z The dim_order tensor API is experimental and subject to change. 2024-12-18T00:16:44.9774617Z 2024-12-18T00:16:44.9775242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:44.9776139Z 2024-12-18T00:16:44.9776539Z warnings.warn(msg) 2024-12-18T00:16:44.9776976Z 2024-12-18T00:16:44.9777517Z --- Parse Warning: 2 / 105 --- 2024-12-18T00:16:44.9779490Z /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=431. 2024-12-18T00:16:44.9781707Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:44.9782818Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-12-18T00:16:44.9783608Z 2024-12-18T00:16:44.9784099Z This is helpful when you want to visualize data over some 2024-12-18T00:16:44.9784889Z range of inputs. See below for a plotting example. 2024-12-18T00:16:44.9785496Z 2024-12-18T00:16:44.9785960Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-12-18T00:16:44.9786791Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-12-18T00:16:44.9787674Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-12-18T00:16:44.9788495Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-12-18T00:16:44.9789302Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-12-18T00:16:44.9790013Z to the result shape. 2024-12-18T00:16:44.9790484Z 2024-12-18T00:16:44.9790819Z .. note:: 2024-12-18T00:16:44.9791358Z 0D inputs are treated equivalently to 1D inputs of a 2024-12-18T00:16:44.9792021Z single element. 2024-12-18T00:16:44.9792461Z 2024-12-18T00:16:44.9792809Z .. warning:: 2024-12-18T00:16:44.9793408Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-12-18T00:16:44.9794228Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-12-18T00:16:44.9794866Z 2024-12-18T00:16:44.9795300Z In the future `torch.meshgrid` will transition to 2024-12-18T00:16:44.9795957Z `indexing='xy'` as the default. 2024-12-18T00:16:44.9796500Z 2024-12-18T00:16:44.9797005Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-12-18T00:16:44.9797845Z this issue with the goal of migrating to NumPy's behavior. 2024-12-18T00:16:44.9798586Z 2024-12-18T00:16:44.9798934Z .. seealso:: 2024-12-18T00:16:44.9799356Z 2024-12-18T00:16:44.9799839Z :func:`torch.cartesian_prod` has the same effect but it 2024-12-18T00:16:44.9800578Z collects the data in a tensor of vectors. 2024-12-18T00:16:44.9801164Z 2024-12-18T00:16:44.9801495Z Args: 2024-12-18T00:16:44.9802195Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-12-18T00:16:44.9803161Z treated as tensors of size :math:`(1,)` automatically 2024-12-18T00:16:44.9803803Z 2024-12-18T00:16:44.9804303Z indexing: (str, optional): the indexing mode, either "xy" 2024-12-18T00:16:44.9805121Z or "ij", defaults to "ij". See warning for future changes. 2024-12-18T00:16:44.9805777Z 2024-12-18T00:16:44.9806212Z If "xy" is selected, the first dimension corresponds 2024-12-18T00:16:44.9806976Z to the cardinality of the second input and the second 2024-12-18T00:16:44.9807785Z dimension corresponds to the cardinality of the first 2024-12-18T00:16:44.9808551Z input. 2024-12-18T00:16:44.9809032Z 2024-12-18T00:16:44.9809471Z If "ij" is selected, the dimensions are in the same 2024-12-18T00:16:44.9810174Z order as the cardinality of the inputs. 2024-12-18T00:16:44.9810760Z 2024-12-18T00:16:44.9811093Z Returns: 2024-12-18T00:16:44.9811660Z seq (sequence of Tensors): If the input has :math:`N` 2024-12-18T00:16:44.9812459Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-12-18T00:16:44.9813246Z output will also have :math:`N` tensors, where each tensor 2024-12-18T00:16:44.9813988Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-12-18T00:16:44.9814539Z 2024-12-18T00:16:44.9814881Z Example:: 2024-12-18T00:16:44.9815271Z 2024-12-18T00:16:44.9815622Z >>> x = torch.tensor([1, 2, 3]) 2024-12-18T00:16:44.9816191Z >>> y = torch.tensor([4, 5, 6]) 2024-12-18T00:16:44.9816720Z 2024-12-18T00:16:44.9817237Z Observe the element-wise pairings across the grid, (1, 4), 2024-12-18T00:16:44.9818006Z (1, 5), ..., (3, 6). This is the same thing as the 2024-12-18T00:16:44.9818603Z cartesian product. 2024-12-18T00:16:44.9819222Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-12-18T00:16:44.9819867Z >>> grid_x 2024-12-18T00:16:44.9820307Z tensor([[1, 1, 1], 2024-12-18T00:16:44.9820783Z [2, 2, 2], 2024-12-18T00:16:44.9821245Z [3, 3, 3]]) 2024-12-18T00:16:44.9821722Z >>> grid_y 2024-12-18T00:16:44.9822159Z tensor([[4, 5, 6], 2024-12-18T00:16:44.9822646Z [4, 5, 6], 2024-12-18T00:16:44.9823108Z [4, 5, 6]]) 2024-12-18T00:16:44.9823560Z 2024-12-18T00:16:44.9824030Z This correspondence can be seen when these grids are 2024-12-18T00:16:44.9824706Z stacked properly. 2024-12-18T00:16:44.9825392Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-12-18T00:16:44.9826175Z ... torch.cartesian_prod(x, y)) 2024-12-18T00:16:44.9826730Z True 2024-12-18T00:16:44.9827113Z 2024-12-18T00:16:44.9827594Z `torch.meshgrid` is commonly used to produce a grid for 2024-12-18T00:16:44.9828267Z plotting. 2024-12-18T00:16:44.9828777Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-12-18T00:16:44.9829412Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-12-18T00:16:44.9830049Z >>> import matplotlib.pyplot as plt 2024-12-18T00:16:44.9830683Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-12-18T00:16:44.9831315Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-12-18T00:16:44.9831952Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-12-18T00:16:44.9832608Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-12-18T00:16:44.9833239Z >>> ax = plt.axes(projection='3d') 2024-12-18T00:16:44.9833912Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-12-18T00:16:44.9834551Z >>> plt.show() 2024-12-18T00:16:44.9835002Z 2024-12-18T00:16:44.9835382Z .. image:: ../_static/img/meshgrid.png 2024-12-18T00:16:44.9835938Z :width: 512 2024-12-18T00:16:44.9836360Z 2024-12-18T00:16:44.9836692Z 2024-12-18T00:16:44.9837358Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:44.9838154Z 2024-12-18T00:16:44.9838579Z warnings.warn(msg) 2024-12-18T00:16:44.9839008Z 2024-12-18T00:16:44.9839535Z --- Parse Warning: 3 / 105 --- 2024-12-18T00:16:44.9841512Z /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=820. 2024-12-18T00:16:44.9843858Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:44.9845228Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-12-18T00:16:44.9846287Z 2024-12-18T00:16:44.9846797Z Returns the unique elements of the input tensor. 2024-12-18T00:16:44.9847404Z 2024-12-18T00:16:44.9848106Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-12-18T00:16:44.9849200Z this function also eliminates non-consecutive duplicate values. 2024-12-18T00:16:44.9849915Z 2024-12-18T00:16:44.9850509Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-12-18T00:16:44.9851643Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-12-18T00:16:44.9852885Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-12-18T00:16:44.9853925Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-12-18T00:16:44.9854569Z 2024-12-18T00:16:44.9854889Z Args: 2024-12-18T00:16:44.9855297Z input (Tensor): the input tensor 2024-12-18T00:16:44.9856064Z sorted (bool): Whether to sort the unique elements in ascending order 2024-12-18T00:16:44.9856853Z before returning as output. 2024-12-18T00:16:44.9857597Z return_inverse (bool): Whether to also return the indices for where 2024-12-18T00:16:44.9858570Z elements in the original input ended up in the returned unique list. 2024-12-18T00:16:44.9859576Z return_counts (bool): Whether to also return the counts for each unique 2024-12-18T00:16:44.9860348Z element. 2024-12-18T00:16:44.9860977Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-12-18T00:16:44.9861925Z unique of the flattened input is returned. Otherwise, each of the 2024-12-18T00:16:44.9862852Z tensors indexed by the given dimension is treated as one of the 2024-12-18T00:16:44.9863806Z elements to apply the unique operation upon. See examples for more 2024-12-18T00:16:44.9864597Z details. Default: ``None`` 2024-12-18T00:16:44.9865115Z 2024-12-18T00:16:44.9865450Z Returns: 2024-12-18T00:16:44.9866179Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-12-18T00:16:44.9867057Z 2024-12-18T00:16:44.9867587Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-12-18T00:16:44.9868391Z - **inverse_indices** (*Tensor*): (optional) if 2024-12-18T00:16:44.9869158Z :attr:`return_inverse` is True, there will be an additional 2024-12-18T00:16:44.9870065Z returned tensor (same shape as input) representing the indices 2024-12-18T00:16:44.9870983Z for where elements in the original input map to in the output; 2024-12-18T00:16:44.9871876Z otherwise, this function will only return a single tensor. 2024-12-18T00:16:44.9872637Z - **counts** (*Tensor*): (optional) if 2024-12-18T00:16:44.9873359Z :attr:`return_counts` is True, there will be an additional 2024-12-18T00:16:44.9874349Z returned tensor (same shape as output or output.size(dim), 2024-12-18T00:16:44.9875214Z if dim was specified) representing the number of occurrences 2024-12-18T00:16:44.9875957Z for each unique value or tensor. 2024-12-18T00:16:44.9876501Z 2024-12-18T00:16:44.9876844Z Example:: 2024-12-18T00:16:44.9877221Z 2024-12-18T00:16:44.9877759Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-12-18T00:16:44.9878723Z >>> output 2024-12-18T00:16:44.9879139Z tensor([1, 2, 3]) 2024-12-18T00:16:44.9879589Z 2024-12-18T00:16:44.9879991Z >>> output, inverse_indices = torch.unique( 2024-12-18T00:16:44.9880820Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T00:16:44.9881656Z >>> output 2024-12-18T00:16:44.9882124Z tensor([1, 2, 3]) 2024-12-18T00:16:44.9882590Z >>> inverse_indices 2024-12-18T00:16:44.9883066Z tensor([0, 2, 1, 2]) 2024-12-18T00:16:44.9883510Z 2024-12-18T00:16:44.9883909Z >>> output, inverse_indices = torch.unique( 2024-12-18T00:16:44.9884754Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T00:16:44.9885536Z >>> output 2024-12-18T00:16:44.9885956Z tensor([1, 2, 3]) 2024-12-18T00:16:44.9886411Z >>> inverse_indices 2024-12-18T00:16:44.9886882Z tensor([[0, 2], 2024-12-18T00:16:44.9887314Z [1, 2]]) 2024-12-18T00:16:44.9887744Z 2024-12-18T00:16:44.9888088Z >>> a = torch.tensor([ 2024-12-18T00:16:44.9888558Z ... [ 2024-12-18T00:16:44.9888959Z ... [1, 1, 0, 0], 2024-12-18T00:16:44.9889448Z ... [1, 1, 0, 0], 2024-12-18T00:16:44.9889945Z ... [0, 0, 1, 1], 2024-12-18T00:16:44.9890427Z ... ], 2024-12-18T00:16:44.9890812Z ... [ 2024-12-18T00:16:44.9891211Z ... [0, 0, 1, 1], 2024-12-18T00:16:44.9891710Z ... [0, 0, 1, 1], 2024-12-18T00:16:44.9892202Z ... [1, 1, 1, 1], 2024-12-18T00:16:44.9892684Z ... ], 2024-12-18T00:16:44.9893070Z ... [ 2024-12-18T00:16:44.9893471Z ... [1, 1, 0, 0], 2024-12-18T00:16:44.9893963Z ... [1, 1, 0, 0], 2024-12-18T00:16:44.9894454Z ... [0, 0, 1, 1], 2024-12-18T00:16:44.9894932Z ... ], 2024-12-18T00:16:44.9895316Z ... ]) 2024-12-18T00:16:44.9895680Z 2024-12-18T00:16:44.9896225Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-12-18T00:16:44.9897182Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-12-18T00:16:44.9897995Z >>> # each other, so one of them will be removed. 2024-12-18T00:16:44.9898592Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-12-18T00:16:44.9899121Z tensor(True) 2024-12-18T00:16:44.9899601Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-12-18T00:16:44.9900167Z >>> a_unique_dim0 2024-12-18T00:16:44.9900635Z tensor([[[0, 0, 1, 1], 2024-12-18T00:16:44.9901106Z [0, 0, 1, 1], 2024-12-18T00:16:44.9901576Z [1, 1, 1, 1]], 2024-12-18T00:16:44.9902055Z [[1, 1, 0, 0], 2024-12-18T00:16:44.9902522Z [1, 1, 0, 0], 2024-12-18T00:16:44.9902990Z [0, 0, 1, 1]]]) 2024-12-18T00:16:44.9903442Z 2024-12-18T00:16:44.9903996Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-12-18T00:16:44.9904747Z >>> # `a_unique_dim0`: 2024-12-18T00:16:44.9905284Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-12-18T00:16:44.9905857Z tensor(True) 2024-12-18T00:16:44.9906315Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-12-18T00:16:44.9906876Z tensor(True) 2024-12-18T00:16:44.9907287Z 2024-12-18T00:16:44.9907821Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-12-18T00:16:44.9908712Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-12-18T00:16:44.9909411Z >>> # them will be removed. 2024-12-18T00:16:44.9910049Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-12-18T00:16:44.9910606Z tensor(True) 2024-12-18T00:16:44.9911055Z >>> torch.unique(a, dim=1) 2024-12-18T00:16:44.9911566Z tensor([[[0, 0, 1, 1], 2024-12-18T00:16:44.9912035Z [1, 1, 0, 0]], 2024-12-18T00:16:44.9912512Z [[1, 1, 1, 1], 2024-12-18T00:16:44.9913018Z [0, 0, 1, 1]], 2024-12-18T00:16:44.9913541Z [[0, 0, 1, 1], 2024-12-18T00:16:44.9914018Z [1, 1, 0, 0]]]) 2024-12-18T00:16:44.9914481Z 2024-12-18T00:16:44.9915033Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-12-18T00:16:44.9915897Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-12-18T00:16:44.9916700Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-12-18T00:16:44.9917429Z >>> # sub-tensors will be removed. 2024-12-18T00:16:44.9918012Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-12-18T00:16:44.9918618Z tensor(True) 2024-12-18T00:16:44.9919070Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-12-18T00:16:44.9919610Z tensor(True) 2024-12-18T00:16:44.9920070Z >>> torch.unique(a, dim=2) 2024-12-18T00:16:44.9920602Z tensor([[[0, 1], 2024-12-18T00:16:44.9921047Z [0, 1], 2024-12-18T00:16:44.9921495Z [1, 0]], 2024-12-18T00:16:44.9921955Z [[1, 0], 2024-12-18T00:16:44.9922405Z [1, 0], 2024-12-18T00:16:44.9922854Z [1, 1]], 2024-12-18T00:16:44.9923292Z [[0, 1], 2024-12-18T00:16:44.9923739Z [0, 1], 2024-12-18T00:16:44.9924192Z [1, 0]]]) 2024-12-18T00:16:44.9924647Z 2024-12-18T00:16:44.9925290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:44.9926103Z 2024-12-18T00:16:44.9926456Z warnings.warn(msg) 2024-12-18T00:16:44.9926880Z 2024-12-18T00:16:44.9927418Z --- Parse Warning: 4 / 105 --- 2024-12-18T00:16:44.9929284Z /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. 2024-12-18T00:16:44.9931427Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:44.9932259Z 2024-12-18T00:16:44.9932728Z Load a model from a github repo or a local directory. 2024-12-18T00:16:44.9933348Z 2024-12-18T00:16:44.9933939Z Note: Loading a model is the typical use case, but this can also be used to 2024-12-18T00:16:44.9934926Z for loading other objects such as tokenizers, loss functions, etc. 2024-12-18T00:16:44.9935661Z 2024-12-18T00:16:44.9936132Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-12-18T00:16:44.9936927Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-12-18T00:16:44.9937606Z ref (a tag or a branch). 2024-12-18T00:16:44.9938044Z 2024-12-18T00:16:44.9938517Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-12-18T00:16:44.9939195Z path to a local directory. 2024-12-18T00:16:44.9939667Z 2024-12-18T00:16:44.9939994Z Args: 2024-12-18T00:16:44.9940418Z repo_or_dir (str): If ``source`` is 'github', 2024-12-18T00:16:44.9941373Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-12-18T00:16:44.9942652Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-12-18T00:16:44.9943879Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-12-18T00:16:44.9944927Z If ``source`` is 'local' then it should be a path to a local directory. 2024-12-18T00:16:44.9945907Z model (str): the name of a callable (entrypoint) defined in the 2024-12-18T00:16:44.9946674Z repo/dir's ``hubconf.py``. 2024-12-18T00:16:44.9947393Z *args (optional): the corresponding args for callable ``model``. 2024-12-18T00:16:44.9948260Z source (str, optional): 'github' or 'local'. Specifies how 2024-12-18T00:16:44.9949126Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-12-18T00:16:44.9950050Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-12-18T00:16:44.9951043Z This parameter was introduced in v1.12 and helps ensuring that users 2024-12-18T00:16:44.9951891Z only run code from repos that they trust. 2024-12-18T00:16:44.9952469Z 2024-12-18T00:16:44.9952985Z - If ``False``, a prompt will ask the user whether the repo should 2024-12-18T00:16:44.9953699Z be trusted. 2024-12-18T00:16:44.9954317Z - If ``True``, the repo will be added to the trusted list and loaded 2024-12-18T00:16:44.9955091Z without requiring explicit confirmation. 2024-12-18T00:16:44.9955823Z - If ``"check"``, the repo will be checked against the list of 2024-12-18T00:16:44.9956712Z trusted repos in the cache. If it is not present in that list, the 2024-12-18T00:16:44.9957663Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-12-18T00:16:44.9958642Z - If ``None``: this will raise a warning, inviting the user to set 2024-12-18T00:16:44.9959490Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-12-18T00:16:44.9960399Z is only present for backward compatibility and will be removed in 2024-12-18T00:16:44.9961134Z v2.0. 2024-12-18T00:16:44.9961519Z 2024-12-18T00:16:44.9962066Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-12-18T00:16:44.9963022Z force_reload (bool, optional): whether to force a fresh download of 2024-12-18T00:16:44.9963934Z the github repo unconditionally. Does not have any effect if 2024-12-18T00:16:44.9964693Z ``source = 'local'``. Default is ``False``. 2024-12-18T00:16:44.9965467Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-12-18T00:16:44.9966415Z local caches. Note that the message about first download cannot be 2024-12-18T00:16:44.9967300Z muted. Does not have any effect if ``source = 'local'``. 2024-12-18T00:16:44.9967962Z Default is ``True``. 2024-12-18T00:16:44.9968815Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-12-18T00:16:44.9970075Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-12-18T00:16:44.9971283Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-12-18T00:16:44.9972295Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-12-18T00:16:44.9973225Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-12-18T00:16:44.9974112Z 2024-12-18T00:16:44.9974452Z Returns: 2024-12-18T00:16:44.9974997Z The output of the ``model`` callable when called with the given 2024-12-18T00:16:44.9975706Z ``*args`` and ``**kwargs``. 2024-12-18T00:16:44.9976198Z 2024-12-18T00:16:44.9976522Z Example: 2024-12-18T00:16:44.9976972Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T00:16:44.9977583Z >>> # from a github repo 2024-12-18T00:16:44.9978083Z >>> repo = "pytorch/vision" 2024-12-18T00:16:44.9978609Z >>> model = torch.hub.load( 2024-12-18T00:16:44.9979268Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-12-18T00:16:44.9979946Z ... ) 2024-12-18T00:16:44.9980336Z >>> # from a local directory 2024-12-18T00:16:44.9980915Z >>> path = "/some/local/path/pytorch/vision" 2024-12-18T00:16:44.9981655Z >>> # xdoctest: +SKIP 2024-12-18T00:16:44.9982391Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-12-18T00:16:44.9983190Z 2024-12-18T00:16:44.9983835Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:44.9984650Z 2024-12-18T00:16:44.9985061Z warnings.warn(msg) 2024-12-18T00:16:44.9985537Z 2024-12-18T00:16:44.9986069Z --- Parse Warning: 5 / 105 --- 2024-12-18T00:16:44.9988075Z /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=687. 2024-12-18T00:16:44.9990340Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:44.9991296Z Download object at the given URL to a local path. 2024-12-18T00:16:44.9991910Z 2024-12-18T00:16:44.9992230Z Args: 2024-12-18T00:16:44.9992664Z url (str): URL of the object to download 2024-12-18T00:16:44.9993500Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-12-18T00:16:44.9994738Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-12-18T00:16:44.9995726Z Default: None 2024-12-18T00:16:44.9996473Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-12-18T00:16:44.9997308Z Default: True 2024-12-18T00:16:44.9997752Z 2024-12-18T00:16:44.9998080Z Example: 2024-12-18T00:16:44.9998615Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T00:16:44.9999236Z >>> # xdoctest: +REQUIRES(POSIX) 2024-12-18T00:16:44.9999826Z >>> torch.hub.download_url_to_file( 2024-12-18T00:16:45.0000615Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-12-18T00:16:45.0001413Z ... "/tmp/temporary_file", 2024-12-18T00:16:45.0001938Z ... ) 2024-12-18T00:16:45.0002297Z 2024-12-18T00:16:45.0002625Z 2024-12-18T00:16:45.0003269Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0004076Z 2024-12-18T00:16:45.0004432Z warnings.warn(msg) 2024-12-18T00:16:45.0004845Z 2024-12-18T00:16:45.0005356Z --- Parse Warning: 6 / 105 --- 2024-12-18T00:16:45.0007372Z /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=812. 2024-12-18T00:16:45.0009731Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0010686Z Loads the Torch serialized object at the given URL. 2024-12-18T00:16:45.0011298Z 2024-12-18T00:16:45.0011805Z If downloaded file is a zip file, it will be automatically 2024-12-18T00:16:45.0012477Z decompressed. 2024-12-18T00:16:45.0012885Z 2024-12-18T00:16:45.0013448Z If the object is already present in `model_dir`, it's deserialized and 2024-12-18T00:16:45.0014202Z returned. 2024-12-18T00:16:45.0014814Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-12-18T00:16:45.0015742Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-12-18T00:16:45.0016447Z 2024-12-18T00:16:45.0016778Z Args: 2024-12-18T00:16:45.0017209Z url (str): URL of the object to download 2024-12-18T00:16:45.0017974Z model_dir (str, optional): directory in which to save the object 2024-12-18T00:16:45.0019160Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-12-18T00:16:45.0020467Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-12-18T00:16:45.0021412Z Default: True 2024-12-18T00:16:45.0022309Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-12-18T00:16:45.0023504Z ``filename-.ext`` where ```` is the first eight or more 2024-12-18T00:16:45.0024589Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-12-18T00:16:45.0025537Z ensure unique names and to verify the contents of the file. 2024-12-18T00:16:45.0026255Z Default: False 2024-12-18T00:16:45.0027179Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-12-18T00:16:45.0028591Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-12-18T00:16:45.0029858Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-12-18T00:16:45.0030691Z 2024-12-18T00:16:45.0031010Z Example: 2024-12-18T00:16:45.0031472Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T00:16:45.0032169Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-12-18T00:16:45.0033018Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-12-18T00:16:45.0033758Z ... ) 2024-12-18T00:16:45.0034112Z 2024-12-18T00:16:45.0034444Z 2024-12-18T00:16:45.0035093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0035901Z 2024-12-18T00:16:45.0036248Z warnings.warn(msg) 2024-12-18T00:16:45.0036654Z 2024-12-18T00:16:45.0037159Z --- Parse Warning: 7 / 105 --- 2024-12-18T00:16:45.0039235Z /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=360. 2024-12-18T00:16:45.0041497Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:45.0042594Z Registers the function implementation as the fallback for the given key. 2024-12-18T00:16:45.0043392Z 2024-12-18T00:16:45.0043960Z This function only works for a library with global namespace ("_"). 2024-12-18T00:16:45.0044708Z 2024-12-18T00:16:45.0045037Z Args: 2024-12-18T00:16:45.0045771Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-12-18T00:16:45.0046689Z to register a fallthrough. 2024-12-18T00:16:45.0047652Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-12-18T00:16:45.0048755Z the dispatch key that the library was created with. 2024-12-18T00:16:45.0049949Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-12-18T00:16:45.0051435Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-12-18T00:16:45.0052388Z 2024-12-18T00:16:45.0052751Z Example:: 2024-12-18T00:16:45.0053181Z >>> my_lib = Library("_", "IMPL") 2024-12-18T00:16:45.0053806Z >>> def fallback_kernel(op, *args, **kwargs): 2024-12-18T00:16:45.0054466Z >>> # Handle all autocast ops generically 2024-12-18T00:16:45.0055050Z >>> # ... 2024-12-18T00:16:45.0055597Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T00:16:45.0056186Z 2024-12-18T00:16:45.0057557Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-12-18T00:16:45.0059133Z 2024-12-18T00:16:45.0059589Z my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T00:16:45.0060166Z ^ 2024-12-18T00:16:45.0060511Z warnings.warn(msg) 2024-12-18T00:16:45.0060943Z 2024-12-18T00:16:45.0061455Z --- Parse Warning: 8 / 105 --- 2024-12-18T00:16:45.0063503Z /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=725. 2024-12-18T00:16:45.0065712Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:45.0066788Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-12-18T00:16:45.0067546Z 2024-12-18T00:16:45.0068043Z Also sometimes known as a "meta kernel", "abstract impl". 2024-12-18T00:16:45.0068719Z 2024-12-18T00:16:45.0069343Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-12-18T00:16:45.0070402Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-12-18T00:16:45.0071422Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-12-18T00:16:45.0072310Z what the properties of the output Tensors are. 2024-12-18T00:16:45.0072901Z 2024-12-18T00:16:45.0073498Z The FakeTensor implementation has the same signature as the operator. 2024-12-18T00:16:45.0074643Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-12-18T00:16:45.0075614Z implementation, assume that all Tensor inputs to the operator are 2024-12-18T00:16:45.0076569Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-12-18T00:16:45.0077511Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-12-18T00:16:45.0078577Z The FakeTensor implementation must consist of only PyTorch operations 2024-12-18T00:16:45.0079547Z (and may not directly access the storage or data of any input or 2024-12-18T00:16:45.0080278Z intermediate Tensors). 2024-12-18T00:16:45.0080728Z 2024-12-18T00:16:45.0081181Z This API may be used as a decorator (see examples). 2024-12-18T00:16:45.0081792Z 2024-12-18T00:16:45.0082221Z For a detailed guide on custom ops, please see 2024-12-18T00:16:45.0083083Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-12-18T00:16:45.0083832Z 2024-12-18T00:16:45.0084170Z Examples: 2024-12-18T00:16:45.0084568Z >>> import torch 2024-12-18T00:16:45.0085043Z >>> import numpy as np 2024-12-18T00:16:45.0085573Z >>> from torch import Tensor 2024-12-18T00:16:45.0086078Z >>> 2024-12-18T00:16:45.0086631Z >>> # Example 1: an operator without data-dependent output shape 2024-12-18T00:16:45.0087562Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-12-18T00:16:45.0088554Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-12-18T00:16:45.0089482Z >>> raise NotImplementedError("Implementation goes here") 2024-12-18T00:16:45.0090146Z >>> 2024-12-18T00:16:45.0090669Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-12-18T00:16:45.0091351Z >>> def _(x, weight, bias): 2024-12-18T00:16:45.0091903Z >>> assert x.dim() == 2 2024-12-18T00:16:45.0092450Z >>> assert weight.dim() == 2 2024-12-18T00:16:45.0093019Z >>> assert bias.dim() == 1 2024-12-18T00:16:45.0093597Z >>> assert x.shape[1] == weight.shape[1] 2024-12-18T00:16:45.0094237Z >>> assert weight.shape[0] == bias.shape[0] 2024-12-18T00:16:45.0094883Z >>> assert x.device == weight.device 2024-12-18T00:16:45.0095443Z >>> 2024-12-18T00:16:45.0095862Z >>> return (x @ weight.t()) + bias 2024-12-18T00:16:45.0096500Z >>> 2024-12-18T00:16:45.0097071Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-12-18T00:16:45.0097757Z >>> x = torch.randn(2, 3) 2024-12-18T00:16:45.0098303Z >>> w = torch.randn(3, 3) 2024-12-18T00:16:45.0098836Z >>> b = torch.randn(3) 2024-12-18T00:16:45.0099453Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-12-18T00:16:45.0100086Z >>> 2024-12-18T00:16:45.0100493Z >>> assert y.shape == (2, 3) 2024-12-18T00:16:45.0101005Z >>> 2024-12-18T00:16:45.0101526Z >>> # Example 2: an operator with data-dependent output shape 2024-12-18T00:16:45.0102409Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-12-18T00:16:45.0103228Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-12-18T00:16:45.0103840Z >>> x_np = x.numpy(force=True) 2024-12-18T00:16:45.0104452Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-12-18T00:16:45.0105122Z >>> return torch.tensor(res, device=x.device) 2024-12-18T00:16:45.0105695Z >>> 2024-12-18T00:16:45.0106219Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-12-18T00:16:45.0106885Z >>> def _(x): 2024-12-18T00:16:45.0107418Z >>> # Number of nonzero-elements is data-dependent. 2024-12-18T00:16:45.0108145Z >>> # Since we cannot peek at the data in an fake impl, 2024-12-18T00:16:45.0108898Z >>> # we use the ctx object to construct a new symint that 2024-12-18T00:16:45.0109585Z >>> # represents the data-dependent size. 2024-12-18T00:16:45.0110205Z >>> ctx = torch.library.get_ctx() 2024-12-18T00:16:45.0110805Z >>> nnz = ctx.new_dynamic_size() 2024-12-18T00:16:45.0111381Z >>> shape = [nnz, x.dim()] 2024-12-18T00:16:45.0112006Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-12-18T00:16:45.0112621Z >>> return result 2024-12-18T00:16:45.0113088Z >>> 2024-12-18T00:16:45.0113627Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T00:16:45.0114308Z >>> 2024-12-18T00:16:45.0114725Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-12-18T00:16:45.0115542Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-12-18T00:16:45.0116385Z >>> trace.print_readable() 2024-12-18T00:16:45.0116904Z >>> 2024-12-18T00:16:45.0117524Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-12-18T00:16:45.0118284Z 2024-12-18T00:16:45.0118685Z 2024-12-18T00:16:45.0119863Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-12-18T00:16:45.0121200Z 2024-12-18T00:16:45.0121540Z _._ = None 2024-12-18T00:16:45.0121908Z ^ 2024-12-18T00:16:45.0122257Z warnings.warn(msg) 2024-12-18T00:16:45.0122690Z 2024-12-18T00:16:45.0123231Z --- Parse Warning: 9 / 105 --- 2024-12-18T00:16:45.0125239Z /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=846. 2024-12-18T00:16:45.0127530Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0128478Z Register a backward formula for this custom op. 2024-12-18T00:16:45.0129059Z 2024-12-18T00:16:45.0129617Z In order for an operator to work with autograd, you need to register 2024-12-18T00:16:45.0130376Z a backward formula: 2024-12-18T00:16:45.0131036Z 1. You must tell us how to compute gradients during the backward pass 2024-12-18T00:16:45.0131830Z by providing us a "backward" function. 2024-12-18T00:16:45.0132662Z 2. If you need any values from the forward to compute gradients, you can 2024-12-18T00:16:45.0133550Z use `setup_context` to save values for backward. 2024-12-18T00:16:45.0134141Z 2024-12-18T00:16:45.0134723Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-12-18T00:16:45.0135740Z - ``grads`` is one or more gradients. The number of gradients matches 2024-12-18T00:16:45.0136545Z the number of outputs of the operator. 2024-12-18T00:16:45.0137319Z The ``ctx`` object is `the same ctx object `_ used by 2024-12-18T00:16:45.0138350Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-12-18T00:16:45.0139265Z same as :meth:`torch.autograd.Function.backward`. 2024-12-18T00:16:45.0139875Z 2024-12-18T00:16:45.0140435Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-12-18T00:16:45.0141420Z Please save quantities needed for backward onto the ``ctx`` object via 2024-12-18T00:16:45.0142461Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-12-18T00:16:45.0143443Z or assigning them as attributes of ``ctx``. If your custom op has 2024-12-18T00:16:45.0144382Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-12-18T00:16:45.0145319Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-12-18T00:16:45.0146020Z 2024-12-18T00:16:45.0146573Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-12-18T00:16:45.0147577Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-12-18T00:16:45.0148605Z not depend on or mutate global state. If you need a non-traceable backward, 2024-12-18T00:16:45.0149641Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-12-18T00:16:45.0150403Z 2024-12-18T00:16:45.0150966Z If you need different autograd behavior on different devices, then we 2024-12-18T00:16:45.0151994Z recommend creating two different custom operators, one for each device 2024-12-18T00:16:45.0153029Z that needs different behavior, and switching between them at runtime. 2024-12-18T00:16:45.0153789Z 2024-12-18T00:16:45.0154123Z Examples: 2024-12-18T00:16:45.0154511Z >>> import torch 2024-12-18T00:16:45.0154994Z >>> import numpy as np 2024-12-18T00:16:45.0155522Z >>> from torch import Tensor 2024-12-18T00:16:45.0156043Z >>> 2024-12-18T00:16:45.0156631Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-12-18T00:16:45.0157392Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-12-18T00:16:45.0157980Z >>> x_np = x.cpu().numpy() 2024-12-18T00:16:45.0158606Z >>> y_np = np.sin(x_np) 2024-12-18T00:16:45.0159239Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T00:16:45.0159884Z >>> 2024-12-18T00:16:45.0160377Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-12-18T00:16:45.0161003Z >>> x, = inputs 2024-12-18T00:16:45.0161501Z >>> ctx.save_for_backward(x) 2024-12-18T00:16:45.0162027Z >>> 2024-12-18T00:16:45.0162428Z >>> def backward(ctx, grad): 2024-12-18T00:16:45.0162984Z >>> x, = ctx.saved_tensors 2024-12-18T00:16:45.0163518Z >>> return grad * x.cos() 2024-12-18T00:16:45.0164043Z >>> 2024-12-18T00:16:45.0164472Z >>> torch.library.register_autograd( 2024-12-18T00:16:45.0165205Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-12-18T00:16:45.0165874Z ... ) 2024-12-18T00:16:45.0166231Z >>> 2024-12-18T00:16:45.0166663Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T00:16:45.0167245Z >>> y = numpy_sin(x) 2024-12-18T00:16:45.0167918Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T00:16:45.0168680Z >>> assert torch.allclose(grad_x, x.cos()) 2024-12-18T00:16:45.0169226Z >>> 2024-12-18T00:16:45.0169646Z >>> # Example with a keyword-only arg 2024-12-18T00:16:45.0170406Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T00:16:45.0171299Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-12-18T00:16:45.0171950Z >>> x_np = x.cpu().numpy() 2024-12-18T00:16:45.0172479Z >>> y_np = x_np * val 2024-12-18T00:16:45.0173099Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T00:16:45.0173850Z >>> 2024-12-18T00:16:45.0174478Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-12-18T00:16:45.0175325Z >>> ctx.val = keyword_only_inputs["val"] 2024-12-18T00:16:45.0175874Z >>> 2024-12-18T00:16:45.0176281Z >>> def backward(ctx, grad): 2024-12-18T00:16:45.0176834Z >>> return grad * ctx.val 2024-12-18T00:16:45.0177355Z >>> 2024-12-18T00:16:45.0177783Z >>> torch.library.register_autograd( 2024-12-18T00:16:45.0178503Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-12-18T00:16:45.0179175Z ... ) 2024-12-18T00:16:45.0179546Z >>> 2024-12-18T00:16:45.0179984Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T00:16:45.0180588Z >>> y = numpy_mul(x, val=3.14) 2024-12-18T00:16:45.0181250Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T00:16:45.0182048Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-12-18T00:16:45.0182708Z 2024-12-18T00:16:45.0183043Z 2024-12-18T00:16:45.0183688Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0184501Z 2024-12-18T00:16:45.0184847Z warnings.warn(msg) 2024-12-18T00:16:45.0185266Z 2024-12-18T00:16:45.0185791Z --- Parse Warning: 10 / 105 --- 2024-12-18T00:16:45.0187740Z /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=1258. 2024-12-18T00:16:45.0189939Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0190999Z Given an operator and some sample arguments, tests if the operator is 2024-12-18T00:16:45.0191765Z registered correctly. 2024-12-18T00:16:45.0192220Z 2024-12-18T00:16:45.0192777Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-12-18T00:16:45.0193807Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-12-18T00:16:45.0194856Z and these APIs require that the functions you pass them satisfy certain 2024-12-18T00:16:45.0195876Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-12-18T00:16:45.0196754Z ``opcheck`` tests these metadata and properties. 2024-12-18T00:16:45.0197343Z 2024-12-18T00:16:45.0197723Z Concretely, we test the following: 2024-12-18T00:16:45.0198247Z 2024-12-18T00:16:45.0198776Z - test_schema: If the schema matches the implementation of 2024-12-18T00:16:45.0199689Z the operator. For example: if the schema specifies a Tensor is mutated, 2024-12-18T00:16:45.0200666Z then we check the implementation mutates the Tensor. If the schema 2024-12-18T00:16:45.0201588Z specifies that we return a new Tensor, then we check that the 2024-12-18T00:16:45.0202527Z implementation returns a new Tensor (instead of an existing one or 2024-12-18T00:16:45.0203304Z a view of an existing one). 2024-12-18T00:16:45.0204014Z - test_autograd_registration: If the operator supports training 2024-12-18T00:16:45.0205075Z (autograd): we check that its autograd formula is registered via 2024-12-18T00:16:45.0206013Z torch.library.register_autograd or a manual registration to one 2024-12-18T00:16:45.0206960Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2024-12-18T00:16:45.0207818Z registrations may lead to undefined behavior. 2024-12-18T00:16:45.0208614Z - test_faketensor: If the operator has a FakeTensor kernel 2024-12-18T00:16:45.0209434Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-12-18T00:16:45.0210340Z but not sufficient) for the operator to work with PyTorch compilation 2024-12-18T00:16:45.0211319Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2024-12-18T00:16:45.0212249Z (also sometimes known as a meta kernel) was registered for the 2024-12-18T00:16:45.0213139Z operator and that it is correct. This test takes the result of 2024-12-18T00:16:45.0214028Z running the operator on real tensors and the result of running 2024-12-18T00:16:45.0214928Z the operator on FakeTensors and checks that they have the same 2024-12-18T00:16:45.0215750Z Tensor metadata (sizes/strides/dtype/device/etc). 2024-12-18T00:16:45.0216584Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-12-18T00:16:45.0217466Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-12-18T00:16:45.0218345Z This checks that the outputs (and gradients, if applicable) are the 2024-12-18T00:16:45.0219193Z same under eager-mode PyTorch and torch.compile. 2024-12-18T00:16:45.0219996Z This test is a superset of ``test_faketensor`` and is an e2e test; 2024-12-18T00:16:45.0220830Z other things it tests are that the operator supports 2024-12-18T00:16:45.0221709Z functionalization and that the backward pass (if it exists) also 2024-12-18T00:16:45.0222547Z supports FakeTensor and functionalization. 2024-12-18T00:16:45.0223113Z 2024-12-18T00:16:45.0223645Z For best results, please call ``opcheck`` multiple times with a 2024-12-18T00:16:45.0224513Z representative set of inputs. If your operator supports 2024-12-18T00:16:45.0225452Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-12-18T00:16:45.0226511Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-12-18T00:16:45.0227386Z use ``opcheck`` with inputs on all supported devices. 2024-12-18T00:16:45.0228004Z 2024-12-18T00:16:45.0228337Z Args: 2024-12-18T00:16:45.0228856Z op: The operator. Must either be a function decorated with 2024-12-18T00:16:45.0229754Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-12-18T00:16:45.0230721Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-12-18T00:16:45.0231485Z args: The args to the operator 2024-12-18T00:16:45.0232067Z kwargs: The kwargs to the operator 2024-12-18T00:16:45.0232782Z test_utils: Tests that we should run. Default: all of them. 2024-12-18T00:16:45.0233539Z Example: ("test_schema", "test_faketensor") 2024-12-18T00:16:45.0234311Z raise_exception: If we should raise an exception on the first 2024-12-18T00:16:45.0235145Z error. If False, we will return a dict with information 2024-12-18T00:16:45.0235842Z on if each test passed or not. 2024-12-18T00:16:45.0236375Z 2024-12-18T00:16:45.0236702Z .. warning:: 2024-12-18T00:16:45.0237088Z 2024-12-18T00:16:45.0237637Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-12-18T00:16:45.0238682Z opcheck tests if your usage of torch.library APIs is correct while 2024-12-18T00:16:45.0239645Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-12-18T00:16:45.0240709Z mathematically correct. Use both to test custom ops that support 2024-12-18T00:16:45.0241489Z gradient computation. 2024-12-18T00:16:45.0241973Z 2024-12-18T00:16:45.0242294Z Example: 2024-12-18T00:16:45.0242660Z 2024-12-18T00:16:45.0243082Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:45.0243948Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T00:16:45.0244761Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2024-12-18T00:16:45.0245382Z >>> x_np = x.numpy(force=True) 2024-12-18T00:16:45.0245933Z >>> z_np = x_np * y 2024-12-18T00:16:45.0246501Z >>> return torch.from_numpy(z_np).to(x.device) 2024-12-18T00:16:45.0247087Z >>> 2024-12-18T00:16:45.0247490Z >>> @numpy_mul.register_fake 2024-12-18T00:16:45.0248008Z >>> def _(x, y): 2024-12-18T00:16:45.0248506Z >>> return torch.empty_like(x) 2024-12-18T00:16:45.0249044Z >>> 2024-12-18T00:16:45.0249493Z >>> def setup_context(ctx, inputs, output): 2024-12-18T00:16:45.0250073Z >>> y, = inputs 2024-12-18T00:16:45.0250513Z >>> ctx.y = y 2024-12-18T00:16:45.0250961Z >>> 2024-12-18T00:16:45.0251354Z >>> def backward(ctx, grad): 2024-12-18T00:16:45.0251906Z >>> return grad * ctx.y, None 2024-12-18T00:16:45.0252431Z >>> 2024-12-18T00:16:45.0253028Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2024-12-18T00:16:45.0253789Z >>> 2024-12-18T00:16:45.0254187Z >>> sample_inputs = [ 2024-12-18T00:16:45.0254703Z >>> (torch.randn(3), 3.14), 2024-12-18T00:16:45.0255302Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-12-18T00:16:45.0255979Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-12-18T00:16:45.0256782Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-12-18T00:16:45.0257495Z >>> ] 2024-12-18T00:16:45.0257882Z >>> 2024-12-18T00:16:45.0258297Z >>> for args in sample_inputs: 2024-12-18T00:16:45.0258898Z >>> torch.library.opcheck(numpy_mul, args) 2024-12-18T00:16:45.0259484Z 2024-12-18T00:16:45.0259824Z 2024-12-18T00:16:45.0260479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0261283Z 2024-12-18T00:16:45.0261621Z warnings.warn(msg) 2024-12-18T00:16:45.0262043Z 2024-12-18T00:16:45.0262567Z --- Parse Warning: 11 / 105 --- 2024-12-18T00:16:45.0264529Z /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=1226. 2024-12-18T00:16:45.0266758Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0268032Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2024-12-18T00:16:45.0268981Z 2024-12-18T00:16:45.0269476Z Loads an object saved with :func:`torch.save` from a file. 2024-12-18T00:16:45.0270137Z 2024-12-18T00:16:45.0270738Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-12-18T00:16:45.0295099Z which underlie tensors, specially. They are first deserialized on the 2024-12-18T00:16:45.0296104Z CPU and are then moved to the device they were saved from. If this fails 2024-12-18T00:16:45.0297125Z (e.g. because the run time system doesn't have certain devices), an exception 2024-12-18T00:16:45.0298158Z is raised. However, storages can be dynamically remapped to an alternative 2024-12-18T00:16:45.0299069Z set of devices using the :attr:`map_location` argument. 2024-12-18T00:16:45.0299934Z 2024-12-18T00:16:45.0300548Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-12-18T00:16:45.0301607Z storage with two arguments: storage and location. The storage argument 2024-12-18T00:16:45.0302648Z will be the initial deserialization of the storage, residing on the CPU. 2024-12-18T00:16:45.0303756Z Each serialized storage has a location tag associated with it which 2024-12-18T00:16:45.0304722Z identifies the device it was saved from, and this tag is the second 2024-12-18T00:16:45.0305759Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-12-18T00:16:45.0306830Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-12-18T00:16:45.0307804Z :attr:`map_location` should return either ``None`` or a storage. If 2024-12-18T00:16:45.0308831Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-12-18T00:16:45.0309951Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-12-18T00:16:45.0311034Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-12-18T00:16:45.0311835Z 2024-12-18T00:16:45.0312436Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-12-18T00:16:45.0313493Z a device tag, it indicates the location where all tensors should be loaded. 2024-12-18T00:16:45.0314268Z 2024-12-18T00:16:45.0314899Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-12-18T00:16:45.0315949Z appearing in the file (keys), to ones that specify where to put the 2024-12-18T00:16:45.0316854Z storages (values). 2024-12-18T00:16:45.0317291Z 2024-12-18T00:16:45.0317876Z User extensions can register their own location tags and tagging and 2024-12-18T00:16:45.0319015Z deserialization methods using :func:`torch.serialization.register_package`. 2024-12-18T00:16:45.0319844Z 2024-12-18T00:16:45.0320175Z Args: 2024-12-18T00:16:45.0320964Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-12-18T00:16:45.0322057Z or a string or os.PathLike object containing a file name 2024-12-18T00:16:45.0323159Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-12-18T00:16:45.0324107Z locations 2024-12-18T00:16:45.0324792Z pickle_module: module used for unpickling metadata and objects (has to 2024-12-18T00:16:45.0325717Z match the :attr:`pickle_module` used to serialize file) 2024-12-18T00:16:45.0326612Z weights_only: Indicates whether unpickler should be restricted to 2024-12-18T00:16:45.0327497Z loading only tensors, primitive types, dictionaries 2024-12-18T00:16:45.0328398Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-12-18T00:16:45.0329227Z See :ref:`weights-only` for more details. 2024-12-18T00:16:45.0330264Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-12-18T00:16:45.0331697Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-12-18T00:16:45.0333148Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-12-18T00:16:45.0334574Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-12-18T00:16:45.0335811Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-12-18T00:16:45.0336877Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-12-18T00:16:45.0337980Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-12-18T00:16:45.0338810Z :attr:`errors=...`. 2024-12-18T00:16:45.0339295Z 2024-12-18T00:16:45.0339642Z .. warning:: 2024-12-18T00:16:45.0340265Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-12-18T00:16:45.0341242Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-12-18T00:16:45.0342340Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-12-18T00:16:45.0343480Z during unpickling. Never load data that could have come from an untrusted 2024-12-18T00:16:45.0344668Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-12-18T00:16:45.0345554Z 2024-12-18T00:16:45.0345880Z .. note:: 2024-12-18T00:16:45.0346578Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-12-18T00:16:45.0347722Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-12-18T00:16:45.0348882Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-12-18T00:16:45.0349739Z 2024-12-18T00:16:45.0350069Z .. note:: 2024-12-18T00:16:45.0350737Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-12-18T00:16:45.0351785Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-12-18T00:16:45.0352749Z when loading files saved by Python 2 in Python 3. If this default 2024-12-18T00:16:45.0353789Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-12-18T00:16:45.0354885Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-12-18T00:16:45.0355954Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-12-18T00:16:45.0357010Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-12-18T00:16:45.0357782Z 2024-12-18T00:16:45.0358104Z Example: 2024-12-18T00:16:45.0358623Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-12-18T00:16:45.0359296Z >>> torch.load("tensors.pt", weights_only=True) 2024-12-18T00:16:45.0359940Z # Load all tensors onto the CPU 2024-12-18T00:16:45.0360770Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-12-18T00:16:45.0361670Z # Load all tensors onto the CPU, using a function 2024-12-18T00:16:45.0362278Z >>> torch.load( 2024-12-18T00:16:45.0363001Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-12-18T00:16:45.0363796Z ... ) 2024-12-18T00:16:45.0364216Z # Load all tensors onto GPU 1 2024-12-18T00:16:45.0364755Z >>> torch.load( 2024-12-18T00:16:45.0365197Z ... "tensors.pt", 2024-12-18T00:16:45.0365807Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-12-18T00:16:45.0366479Z ... weights_only=True, 2024-12-18T00:16:45.0367031Z ... ) # type: ignore[attr-defined] 2024-12-18T00:16:45.0367631Z # Map tensors from GPU 1 to GPU 0 2024-12-18T00:16:45.0368442Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-12-18T00:16:45.0369299Z # Load tensor from io.BytesIO object 2024-12-18T00:16:45.0370149Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-12-18T00:16:45.0371015Z >>> with open("tensor.pt", "rb") as f: 2024-12-18T00:16:45.0371623Z ... buffer = io.BytesIO(f.read()) 2024-12-18T00:16:45.0372236Z >>> torch.load(buffer, weights_only=False) 2024-12-18T00:16:45.0372909Z # Load a module with 'ascii' encoding for unpickling 2024-12-18T00:16:45.0374058Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-12-18T00:16:45.0375074Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-12-18T00:16:45.0375783Z 2024-12-18T00:16:45.0376505Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0377306Z 2024-12-18T00:16:45.0377711Z warnings.warn(msg) 2024-12-18T00:16:45.0378140Z 2024-12-18T00:16:45.0378718Z --- Parse Warning: 12 / 105 --- 2024-12-18T00:16:45.0380796Z /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=21. 2024-12-18T00:16:45.0383108Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:45.0384087Z Check if there is an available :ref:`accelerator`. 2024-12-18T00:16:45.0384781Z 2024-12-18T00:16:45.0385109Z Returns: 2024-12-18T00:16:45.0385825Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2024-12-18T00:16:45.0386676Z 2024-12-18T00:16:45.0387009Z Example:: 2024-12-18T00:16:45.0387380Z 2024-12-18T00:16:45.0388049Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:45.0388890Z 2024-12-18T00:16:45.0390105Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2024-12-18T00:16:45.0391464Z 2024-12-18T00:16:45.0392117Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:45.0392968Z ^ 2024-12-18T00:16:45.0393495Z warnings.warn(msg) 2024-12-18T00:16:45.0393920Z 2024-12-18T00:16:45.0394417Z --- Parse Warning: 13 / 105 --- 2024-12-18T00:16:45.0396496Z /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=110. 2024-12-18T00:16:45.0398885Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:45.0399916Z Wait for all kernels in all streams on the given device to complete. 2024-12-18T00:16:45.0400643Z 2024-12-18T00:16:45.0400979Z Args: 2024-12-18T00:16:45.0401747Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2024-12-18T00:16:45.0402945Z the current :ref:`accelerator` device type. If not given, 2024-12-18T00:16:45.0403894Z use :func:`torch.accelerator.current_device_idx` by default. 2024-12-18T00:16:45.0404581Z 2024-12-18T00:16:45.0405328Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2024-12-18T00:16:45.0406245Z 2024-12-18T00:16:45.0406571Z Example:: 2024-12-18T00:16:45.0406943Z 2024-12-18T00:16:45.0407374Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:45.0408299Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:45.0409236Z >>> start_event = torch.Event(enable_timing=True) 2024-12-18T00:16:45.0409925Z >>> end_event = torch.Event(enable_timing=True) 2024-12-18T00:16:45.0410535Z >>> start_event.record() 2024-12-18T00:16:45.0411307Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2024-12-18T00:16:45.0412116Z >>> sum = torch.sum(tensor) 2024-12-18T00:16:45.0412651Z >>> end_event.record() 2024-12-18T00:16:45.0413288Z >>> torch.accelerator.synchronize() 2024-12-18T00:16:45.0414049Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2024-12-18T00:16:45.0414697Z 2024-12-18T00:16:45.0415897Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2024-12-18T00:16:45.0417268Z 2024-12-18T00:16:45.0417974Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T00:16:45.0418825Z ^ 2024-12-18T00:16:45.0419355Z warnings.warn(msg) 2024-12-18T00:16:45.0419775Z 2024-12-18T00:16:45.0420277Z --- Parse Warning: 14 / 105 --- 2024-12-18T00:16:45.0422246Z /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=343. 2024-12-18T00:16:45.0424442Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:45.0425310Z Retrieves the CUDA runtime API module. 2024-12-18T00:16:45.0425849Z 2024-12-18T00:16:45.0426160Z 2024-12-18T00:16:45.0426787Z This function initializes the CUDA runtime environment if it is not already 2024-12-18T00:16:45.0427863Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-12-18T00:16:45.0428880Z runtime API module provides access to various CUDA runtime functions. 2024-12-18T00:16:45.0429016Z 2024-12-18T00:16:45.0429168Z Args: 2024-12-18T00:16:45.0429307Z ``None`` 2024-12-18T00:16:45.0429450Z 2024-12-18T00:16:45.0429585Z Returns: 2024-12-18T00:16:45.0429828Z module: The CUDA runtime API module (_cudart). 2024-12-18T00:16:45.0429969Z 2024-12-18T00:16:45.0430106Z Raises: 2024-12-18T00:16:45.0430519Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-12-18T00:16:45.0431169Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-12-18T00:16:45.0431301Z 2024-12-18T00:16:45.0431532Z Example of CUDA operations with profiling: 2024-12-18T00:16:45.0431689Z >>> import torch 2024-12-18T00:16:45.0431925Z >>> from torch.cuda import cudart, check_error 2024-12-18T00:16:45.0432075Z >>> import os 2024-12-18T00:16:45.0432214Z >>> 2024-12-18T00:16:45.0432411Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-12-18T00:16:45.0432548Z >>> 2024-12-18T00:16:45.0432784Z >>> def perform_cuda_operations_with_streams(): 2024-12-18T00:16:45.0432975Z >>> stream = torch.cuda.Stream() 2024-12-18T00:16:45.0433186Z >>> with torch.cuda.stream(stream): 2024-12-18T00:16:45.0433391Z >>> x = torch.randn(100, 100, device='cuda') 2024-12-18T00:16:45.0433599Z >>> y = torch.randn(100, 100, device='cuda') 2024-12-18T00:16:45.0433785Z >>> z = torch.mul(x, y) 2024-12-18T00:16:45.0433932Z >>> return z 2024-12-18T00:16:45.0434078Z >>> 2024-12-18T00:16:45.0434256Z >>> torch.cuda.synchronize() 2024-12-18T00:16:45.0434473Z >>> print("====== Start nsys profiling ======") 2024-12-18T00:16:45.0434715Z >>> check_error(cudart().cudaProfilerStart()) 2024-12-18T00:16:45.0434949Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-12-18T00:16:45.0435211Z >>> result = perform_cuda_operations_with_streams() 2024-12-18T00:16:45.0435418Z >>> print("CUDA operations completed.") 2024-12-18T00:16:45.0435708Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-12-18T00:16:45.0435916Z >>> print("====== End nsys profiling ======") 2024-12-18T00:16:45.0436051Z 2024-12-18T00:16:45.0436406Z To run this example and save the profiling information, execute: 2024-12-18T00:16:45.0437147Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T00:16:45.0437297Z 2024-12-18T00:16:45.0437726Z This command profiles the CUDA operations in the provided script and saves 2024-12-18T00:16:45.0438109Z the profiling information to a file named `trace_name.prof`. 2024-12-18T00:16:45.0438647Z The `--profile-from-start off` option ensures that profiling starts only 2024-12-18T00:16:45.0438899Z after the `cudaProfilerStart` call in the script. 2024-12-18T00:16:45.0439301Z The `--csv` and `--print-summary` options format the profiling output as a 2024-12-18T00:16:45.0439527Z CSV file and print a summary, respectively. 2024-12-18T00:16:45.0439967Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-12-18T00:16:45.0440227Z overwrite of the output file if it already exists. 2024-12-18T00:16:45.0440386Z 2024-12-18T00:16:45.0441569Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-12-18T00:16:45.0441704Z 2024-12-18T00:16:45.0442337Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T00:16:45.0442477Z ^ 2024-12-18T00:16:45.0442654Z warnings.warn(msg) 2024-12-18T00:16:45.0442787Z 2024-12-18T00:16:45.0443128Z --- Parse Warning: 15 / 105 --- 2024-12-18T00:16:45.0444738Z /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=101. 2024-12-18T00:16:45.0445193Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0445349Z 2024-12-18T00:16:45.0445753Z Append the given callback function to this ``Future``, which will be run 2024-12-18T00:16:45.0446121Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-12-18T00:16:45.0446478Z the same ``Future``, but the order in which they will be executed cannot 2024-12-18T00:16:45.0446804Z be guaranteed (to enforce a certain order consider chaining: 2024-12-18T00:16:45.0447151Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-12-18T00:16:45.0447507Z is the reference to this ``Future``. The callback function can use the 2024-12-18T00:16:45.0447875Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-12-18T00:16:45.0448267Z already completed, the given callback will be run immediately inline. 2024-12-18T00:16:45.0448415Z 2024-12-18T00:16:45.0448745Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-12-18T00:16:45.0449142Z callback might be invoked while the async kernels that are populating 2024-12-18T00:16:45.0449549Z those tensors haven't yet finished executing on the device. However, the 2024-12-18T00:16:45.0449906Z callback will be invoked with some dedicated streams set as current 2024-12-18T00:16:45.0450261Z (fetched from a global pool) which will be synchronized with those 2024-12-18T00:16:45.0450656Z kernels. Hence any operation performed by the callback on these tensors 2024-12-18T00:16:45.0451024Z will be scheduled on the device after the kernels complete. In other 2024-12-18T00:16:45.0451364Z words, as long as the callback doesn't switch streams, it can safely 2024-12-18T00:16:45.0451770Z manipulate the result without any additional synchronization. This is 2024-12-18T00:16:45.0452036Z similar to the non-blocking behavior of :meth:`wait`. 2024-12-18T00:16:45.0452165Z 2024-12-18T00:16:45.0452535Z Similarly, if the callback returns a value that contains tensors that 2024-12-18T00:16:45.0452902Z reside on a GPU, it can do so even if the kernels that are producing 2024-12-18T00:16:45.0453331Z these tensors are still running on the device, as long as the callback 2024-12-18T00:16:45.0453679Z didn't change streams during its execution. If one wants to change 2024-12-18T00:16:45.0454049Z streams, one must be careful to re-synchronize them with the original 2024-12-18T00:16:45.0454558Z streams, that is, those that were current when the callback was invoked. 2024-12-18T00:16:45.0454688Z 2024-12-18T00:16:45.0454836Z Args: 2024-12-18T00:16:45.0455183Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-12-18T00:16:45.0455370Z the only argument. 2024-12-18T00:16:45.0455499Z 2024-12-18T00:16:45.0455641Z Returns: 2024-12-18T00:16:45.0455917Z A new ``Future`` object that holds the return value of the 2024-12-18T00:16:45.0456217Z ``callback`` and will be marked as completed when the given 2024-12-18T00:16:45.0456387Z ``callback`` finishes. 2024-12-18T00:16:45.0456521Z 2024-12-18T00:16:45.0456819Z .. note:: Note that if the callback function throws, either 2024-12-18T00:16:45.0457181Z through the original future being completed with an exception and 2024-12-18T00:16:45.0457507Z calling ``fut.wait()``, or through other code in the callback, the 2024-12-18T00:16:45.0457870Z future returned by ``then`` will be marked appropriately with the 2024-12-18T00:16:45.0458209Z encountered error. However, if this callback later completes 2024-12-18T00:16:45.0458594Z additional futures, those futures are not marked as completed with 2024-12-18T00:16:45.0458944Z an error and the user is responsible for handling completion/waiting 2024-12-18T00:16:45.0459142Z on those futures independently. 2024-12-18T00:16:45.0459276Z 2024-12-18T00:16:45.0459422Z Example:: 2024-12-18T00:16:45.0459678Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T00:16:45.0459842Z >>> def callback(fut): 2024-12-18T00:16:45.0460086Z ... print(f"RPC return value is {fut.wait()}.") 2024-12-18T00:16:45.0460269Z >>> fut = torch.futures.Future() 2024-12-18T00:16:45.0460567Z >>> # The inserted callback will print the return value when 2024-12-18T00:16:45.0460770Z >>> # receiving the response from "worker1" 2024-12-18T00:16:45.0460954Z >>> cb_fut = fut.then(callback) 2024-12-18T00:16:45.0461137Z >>> chain_cb_fut = cb_fut.then( 2024-12-18T00:16:45.0461387Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-12-18T00:16:45.0461537Z ... ) 2024-12-18T00:16:45.0461694Z >>> fut.set_result(5) 2024-12-18T00:16:45.0461859Z RPC return value is 5. 2024-12-18T00:16:45.0462032Z Chained cb done. None 2024-12-18T00:16:45.0462158Z 2024-12-18T00:16:45.0462617Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0462750Z 2024-12-18T00:16:45.0462928Z warnings.warn(msg) 2024-12-18T00:16:45.0463060Z 2024-12-18T00:16:45.0463372Z --- Parse Warning: 16 / 105 --- 2024-12-18T00:16:45.0465047Z /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=209. 2024-12-18T00:16:45.0465509Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0465659Z 2024-12-18T00:16:45.0466018Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-12-18T00:16:45.0466409Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-12-18T00:16:45.0466597Z cannot be marked completed twice. 2024-12-18T00:16:45.0466734Z 2024-12-18T00:16:45.0467130Z If the result contains tensors that reside on GPUs, this method can be 2024-12-18T00:16:45.0467513Z called even if the asynchronous kernels that are populating those 2024-12-18T00:16:45.0467948Z tensors haven't yet completed running on the device, provided that the 2024-12-18T00:16:45.0468342Z streams on which those kernels were enqueued are set as the current ones 2024-12-18T00:16:45.0468717Z when this method is called. Put simply, it's safe to call this method 2024-12-18T00:16:45.0469149Z immediately after launching those kernels, without any additional 2024-12-18T00:16:45.0469545Z synchronization, as long as one doesn't change streams in between. This 2024-12-18T00:16:45.0469937Z method will record events on all the relevant current streams and will 2024-12-18T00:16:45.0470288Z use them to ensure proper scheduling for all the consumers of this 2024-12-18T00:16:45.0470444Z ``Future``. 2024-12-18T00:16:45.0470583Z 2024-12-18T00:16:45.0470723Z Args: 2024-12-18T00:16:45.0471013Z result (object): the result object of this ``Future``. 2024-12-18T00:16:45.0471147Z 2024-12-18T00:16:45.0471309Z Example:: 2024-12-18T00:16:45.0471558Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T00:16:45.0471731Z >>> import threading 2024-12-18T00:16:45.0471883Z >>> import time 2024-12-18T00:16:45.0472072Z >>> def slow_set_future(fut, value): 2024-12-18T00:16:45.0472242Z ... time.sleep(0.5) 2024-12-18T00:16:45.0472417Z ... fut.set_result(value) 2024-12-18T00:16:45.0472616Z >>> fut = torch.futures.Future() 2024-12-18T00:16:45.0472785Z >>> t = threading.Thread( 2024-12-18T00:16:45.0472956Z ... target=slow_set_future, 2024-12-18T00:16:45.0473147Z ... args=(fut, torch.ones(2) * 3) 2024-12-18T00:16:45.0473285Z ... ) 2024-12-18T00:16:45.0473440Z >>> t.start() 2024-12-18T00:16:45.0473744Z >>> print(fut.wait()) 2024-12-18T00:16:45.0473894Z tensor([3., 3.]) 2024-12-18T00:16:45.0474056Z >>> t.join() 2024-12-18T00:16:45.0474200Z 2024-12-18T00:16:45.0474658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0474795Z 2024-12-18T00:16:45.0474965Z warnings.warn(msg) 2024-12-18T00:16:45.0475098Z 2024-12-18T00:16:45.0475415Z --- Parse Warning: 17 / 105 --- 2024-12-18T00:16:45.0477047Z /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. 2024-12-18T00:16:45.0477506Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0477904Z Compiles compute shader from source and allows one to invoke kernels 2024-12-18T00:16:45.0478158Z defined there from the comfort of Python runtime 2024-12-18T00:16:45.0478322Z Example:: 2024-12-18T00:16:45.0478507Z 2024-12-18T00:16:45.0478738Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2024-12-18T00:16:45.0478943Z >>> lib = torch.mps._compile_shader( 2024-12-18T00:16:45.0479634Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2024-12-18T00:16:45.0479788Z ... ) 2024-12-18T00:16:45.0479976Z >>> x = torch.zeros(16, device="mps") 2024-12-18T00:16:45.0480138Z >>> lib.full(x, 3.14) 2024-12-18T00:16:45.0480282Z 2024-12-18T00:16:45.0480728Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0480867Z 2024-12-18T00:16:45.0481029Z warnings.warn(msg) 2024-12-18T00:16:45.0481173Z 2024-12-18T00:16:45.0481475Z --- Parse Warning: 18 / 105 --- 2024-12-18T00:16:45.0482999Z /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. 2024-12-18T00:16:45.0483588Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0483859Z Return the sum of each row of the given sparse tensor. 2024-12-18T00:16:45.0484005Z 2024-12-18T00:16:45.0484401Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-12-18T00:16:45.0484851Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-12-18T00:16:45.0485217Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-12-18T00:16:45.0485472Z returns a dense tensor instead of a sparse tensor. 2024-12-18T00:16:45.0485615Z 2024-12-18T00:16:45.0486068Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-12-18T00:16:45.0486412Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-12-18T00:16:45.0486540Z 2024-12-18T00:16:45.0486932Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-12-18T00:16:45.0487351Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-12-18T00:16:45.0487483Z 2024-12-18T00:16:45.0487630Z Args: 2024-12-18T00:16:45.0487836Z input (Tensor): the input sparse tensor 2024-12-18T00:16:45.0488327Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-12-18T00:16:45.0488481Z over all dims. 2024-12-18T00:16:45.0488929Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-12-18T00:16:45.0489128Z Default: dtype of :attr:`input`. 2024-12-18T00:16:45.0489262Z 2024-12-18T00:16:45.0489422Z Example:: 2024-12-18T00:16:45.0489555Z 2024-12-18T00:16:45.0489709Z >>> nnz = 3 2024-12-18T00:16:45.0489859Z >>> dims = [5, 5, 2, 3] 2024-12-18T00:16:45.0490126Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-12-18T00:16:45.0490460Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-12-18T00:16:45.0490657Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-12-18T00:16:45.0490840Z >>> size = torch.Size(dims) 2024-12-18T00:16:45.0491069Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:45.0491292Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-12-18T00:16:45.0491429Z >>> S 2024-12-18T00:16:45.0491612Z tensor(indices=tensor([[2, 0, 3], 2024-12-18T00:16:45.0491787Z [2, 4, 1]]), 2024-12-18T00:16:45.0491997Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-12-18T00:16:45.0492195Z [ 0.3411, 0.0918, -0.2312]], 2024-12-18T00:16:45.0492327Z 2024-12-18T00:16:45.0492502Z [[ 0.5348, 0.0634, -2.0494], 2024-12-18T00:16:45.0492691Z [-0.7125, -1.0646, 2.1844]], 2024-12-18T00:16:45.0492828Z 2024-12-18T00:16:45.0493021Z [[ 0.1276, 0.1874, -0.6334], 2024-12-18T00:16:45.0493200Z [-1.9682, -0.5340, 0.7483]]]), 2024-12-18T00:16:45.0493450Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-12-18T00:16:45.0493583Z 2024-12-18T00:16:45.0493918Z # when sum over only part of sparse_dims, return a sparse tensor 2024-12-18T00:16:45.0494112Z >>> torch.sparse.sum(S, [1, 3]) 2024-12-18T00:16:45.0494300Z tensor(indices=tensor([[0, 2, 3]]), 2024-12-18T00:16:45.0494499Z values=tensor([[-1.4512, 0.4073], 2024-12-18T00:16:45.0494672Z [-0.8901, 0.2017], 2024-12-18T00:16:45.0494842Z [-0.3183, -1.7539]]), 2024-12-18T00:16:45.0495080Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-12-18T00:16:45.0495244Z 2024-12-18T00:16:45.0495547Z # when sum over all sparse dim, return a dense tensor 2024-12-18T00:16:45.0495721Z # with summed dims squeezed 2024-12-18T00:16:45.0495918Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-12-18T00:16:45.0496084Z tensor([-2.6596, -1.1450]) 2024-12-18T00:16:45.0496222Z 2024-12-18T00:16:45.0496714Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0496879Z 2024-12-18T00:16:45.0497052Z warnings.warn(msg) 2024-12-18T00:16:45.0497190Z 2024-12-18T00:16:45.0497495Z --- Parse Warning: 19 / 105 --- 2024-12-18T00:16:45.0499054Z /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=40. 2024-12-18T00:16:45.0499520Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0499674Z 2024-12-18T00:16:45.0500054Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-12-18T00:16:45.0500406Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-12-18T00:16:45.0500784Z pushes the map into PyTorch operations called by ``func``, effectively 2024-12-18T00:16:45.0500970Z vectorizing those operations. 2024-12-18T00:16:45.0501111Z 2024-12-18T00:16:45.0501485Z vmap is useful for handling batch dimensions: one can write a function 2024-12-18T00:16:45.0501845Z ``func`` that runs on examples and then lift it to a function that can 2024-12-18T00:16:45.0502214Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-12-18T00:16:45.0502505Z compute batched gradients when composed with autograd. 2024-12-18T00:16:45.0502637Z 2024-12-18T00:16:45.0502785Z .. note:: 2024-12-18T00:16:45.0503104Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-12-18T00:16:45.0503320Z convenience. Use whichever one you'd like. 2024-12-18T00:16:45.0503463Z 2024-12-18T00:16:45.0503599Z Args: 2024-12-18T00:16:45.0503969Z func (function): A Python function that takes one or more arguments. 2024-12-18T00:16:45.0504153Z Must return one or more Tensors. 2024-12-18T00:16:45.0504497Z in_dims (int or nested structure): Specifies which dimension of the 2024-12-18T00:16:45.0504806Z inputs should be mapped over. ``in_dims`` should have a 2024-12-18T00:16:45.0505133Z structure like the inputs. If the ``in_dim`` for a particular 2024-12-18T00:16:45.0505454Z input is None, then that indicates there is no map dimension. 2024-12-18T00:16:45.0505603Z Default: 0. 2024-12-18T00:16:45.0505937Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-12-18T00:16:45.0506274Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-12-18T00:16:45.0506527Z it should have one element per output. Default: 0. 2024-12-18T00:16:45.0506848Z randomness (str): Specifies whether the randomness in this 2024-12-18T00:16:45.0507212Z vmap should be the same or different across batches. If 'different', 2024-12-18T00:16:45.0507574Z the randomness for each batch will be different. If 'same', the 2024-12-18T00:16:45.0507949Z randomness will be the same across batches. If 'error', any calls to 2024-12-18T00:16:45.0508328Z random functions will error. Default: 'error'. WARNING: this flag 2024-12-18T00:16:45.0508675Z only applies to random PyTorch operations and does not apply to 2024-12-18T00:16:45.0508894Z Python's random module or numpy randomness. 2024-12-18T00:16:45.0509299Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-12-18T00:16:45.0509671Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-12-18T00:16:45.0510168Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-12-18T00:16:45.0510664Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-12-18T00:16:45.0510808Z 2024-12-18T00:16:45.0510948Z Returns: 2024-12-18T00:16:45.0511267Z Returns a new "batched" function. It takes the same inputs as 2024-12-18T00:16:45.0511652Z ``func``, except each input has an extra dimension at the index 2024-12-18T00:16:45.0511974Z specified by ``in_dims``. It takes returns the same outputs as 2024-12-18T00:16:45.0512305Z ``func``, except each output has an extra dimension at the index 2024-12-18T00:16:45.0512471Z specified by ``out_dims``. 2024-12-18T00:16:45.0512602Z 2024-12-18T00:16:45.0512758Z .. warning: 2024-12-18T00:16:45.0513096Z :func:`vmap` works best with functional-style code. Please do not 2024-12-18T00:16:45.0513417Z perform any side-effects in ``func``, with the exception of 2024-12-18T00:16:45.0513833Z in-place PyTorch operations. Examples of side-effects include mutating 2024-12-18T00:16:45.0514239Z Python data structures and assigning values to variables not captured 2024-12-18T00:16:45.0514385Z in ``func``. 2024-12-18T00:16:45.0514520Z 2024-12-18T00:16:45.0514950Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-12-18T00:16:45.0515333Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-12-18T00:16:45.0515731Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-12-18T00:16:45.0515865Z 2024-12-18T00:16:45.0516108Z >>> torch.dot # [D], [D] -> [] 2024-12-18T00:16:45.0516454Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-12-18T00:16:45.0516658Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T00:16:45.0516841Z >>> batched_dot(x, y) 2024-12-18T00:16:45.0516988Z 2024-12-18T00:16:45.0517393Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-12-18T00:16:45.0517570Z model authoring experience. 2024-12-18T00:16:45.0517708Z 2024-12-18T00:16:45.0517899Z >>> batch_size, feature_size = 3, 5 2024-12-18T00:16:45.0518195Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-12-18T00:16:45.0518437Z >>> 2024-12-18T00:16:45.0518610Z >>> def model(feature_vec): 2024-12-18T00:16:45.0518847Z >>> # Very simple linear model with activation 2024-12-18T00:16:45.0519060Z >>> return feature_vec.dot(weights).relu() 2024-12-18T00:16:45.0519200Z >>> 2024-12-18T00:16:45.0519466Z >>> examples = torch.randn(batch_size, feature_size) 2024-12-18T00:16:45.0519668Z >>> result = torch.vmap(model)(examples) 2024-12-18T00:16:45.0519821Z 2024-12-18T00:16:45.0520258Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-12-18T00:16:45.0520672Z or impossible to batch. One example is higher-order gradient computation. 2024-12-18T00:16:45.0521091Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-12-18T00:16:45.0521496Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-12-18T00:16:45.0521943Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-12-18T00:16:45.0522361Z we can vectorize the whole computation, computing the Jacobian in a single 2024-12-18T00:16:45.0522547Z call to ``autograd.grad``. 2024-12-18T00:16:45.0522684Z 2024-12-18T00:16:45.0522828Z >>> # Setup 2024-12-18T00:16:45.0522988Z >>> N = 5 2024-12-18T00:16:45.0523147Z >>> f = lambda x: x ** 2 2024-12-18T00:16:45.0523366Z >>> x = torch.randn(N, requires_grad=True) 2024-12-18T00:16:45.0523509Z >>> y = f(x) 2024-12-18T00:16:45.0523685Z >>> I_N = torch.eye(N) 2024-12-18T00:16:45.0523870Z >>> 2024-12-18T00:16:45.0524083Z >>> # Sequential approach 2024-12-18T00:16:45.0524473Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-12-18T00:16:45.0524657Z >>> for v in I_N.unbind()] 2024-12-18T00:16:45.0524884Z >>> jacobian = torch.stack(jacobian_rows) 2024-12-18T00:16:45.0525025Z >>> 2024-12-18T00:16:45.0525283Z >>> # vectorized gradient computation 2024-12-18T00:16:45.0525454Z >>> def get_vjp(v): 2024-12-18T00:16:45.0525664Z >>> return torch.autograd.grad(y, x, v) 2024-12-18T00:16:45.0525880Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-12-18T00:16:45.0526012Z 2024-12-18T00:16:45.0526487Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-12-18T00:16:45.0526631Z 2024-12-18T00:16:45.0526871Z >>> torch.dot # [D], [D] -> [] 2024-12-18T00:16:45.0527362Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-12-18T00:16:45.0527598Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-12-18T00:16:45.0527827Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-12-18T00:16:45.0527960Z 2024-12-18T00:16:45.0528380Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-12-18T00:16:45.0528656Z the dimension that each inputs are batched along as 2024-12-18T00:16:45.0528796Z 2024-12-18T00:16:45.0529040Z >>> torch.dot # [N], [N] -> [] 2024-12-18T00:16:45.0529420Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-12-18T00:16:45.0529634Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T00:16:45.0530050Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-12-18T00:16:45.0530186Z 2024-12-18T00:16:45.0530648Z If there are multiple inputs each of which is batched along different dimensions, 2024-12-18T00:16:45.0530991Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-12-18T00:16:45.0531141Z 2024-12-18T00:16:45.0531376Z >>> torch.dot # [D], [D] -> [] 2024-12-18T00:16:45.0531792Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-12-18T00:16:45.0531997Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T00:16:45.0532419Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-12-18T00:16:45.0532560Z 2024-12-18T00:16:45.0532978Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-12-18T00:16:45.0533172Z matching the shape of the input: 2024-12-18T00:16:45.0533305Z 2024-12-18T00:16:45.0533535Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-12-18T00:16:45.0533745Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T00:16:45.0533911Z >>> input = {'x': x, 'y': y} 2024-12-18T00:16:45.0534220Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-12-18T00:16:45.0534386Z >>> batched_dot(input) 2024-12-18T00:16:45.0534535Z 2024-12-18T00:16:45.0535016Z By default, the output is batched along the first dimension. However, it can be batched 2024-12-18T00:16:45.0535223Z along any dimension by using ``out_dims`` 2024-12-18T00:16:45.0535372Z 2024-12-18T00:16:45.0535533Z >>> f = lambda x: x ** 2 2024-12-18T00:16:45.0535712Z >>> x = torch.randn(2, 5) 2024-12-18T00:16:45.0535915Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-12-18T00:16:45.0536077Z >>> batched_pow(x) # [5, 2] 2024-12-18T00:16:45.0536221Z 2024-12-18T00:16:45.0536736Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-12-18T00:16:45.0536900Z accept kwargs 2024-12-18T00:16:45.0537033Z 2024-12-18T00:16:45.0537245Z >>> x = torch.randn([2, 5]) 2024-12-18T00:16:45.0537436Z >>> def fn(x, scale=4.): 2024-12-18T00:16:45.0537594Z >>> return x * scale 2024-12-18T00:16:45.0537750Z >>> 2024-12-18T00:16:45.0537930Z >>> batched_pow = torch.vmap(fn) 2024-12-18T00:16:45.0538180Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-12-18T00:16:45.0538654Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-12-18T00:16:45.0538789Z 2024-12-18T00:16:45.0538942Z .. note:: 2024-12-18T00:16:45.0539327Z vmap does not provide general autobatching or handle variable-length 2024-12-18T00:16:45.0539513Z sequences out of the box. 2024-12-18T00:16:45.0539650Z 2024-12-18T00:16:45.0540106Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0540240Z 2024-12-18T00:16:45.0540403Z warnings.warn(msg) 2024-12-18T00:16:45.0540552Z 2024-12-18T00:16:45.0540895Z --- Parse Warning: 20 / 105 --- 2024-12-18T00:16:45.0542495Z /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=20. 2024-12-18T00:16:45.0542954Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0543411Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-12-18T00:16:45.0543548Z 2024-12-18T00:16:45.0543904Z This is a more structured way of using triton kernels with PyTorch. 2024-12-18T00:16:45.0544366Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2024-12-18T00:16:45.0544798Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2024-12-18T00:16:45.0544973Z that is simpler; 2024-12-18T00:16:45.0545418Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2024-12-18T00:16:45.0545839Z want to create an operator that behaves like PyTorch built-in operators. 2024-12-18T00:16:45.0546209Z For example, you may use a ``torch.library`` wrapper API to define the 2024-12-18T00:16:45.0546584Z behavior of the triton kernel when passed a tensor subclass or under 2024-12-18T00:16:45.0546775Z a TorchDispatchMode. 2024-12-18T00:16:45.0546909Z 2024-12-18T00:16:45.0547365Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2024-12-18T00:16:45.0547535Z when the implementation 2024-12-18T00:16:45.0547913Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-12-18T00:16:45.0548208Z custom operators as opaque (:func:`torch.compile` and 2024-12-18T00:16:45.0548618Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-12-18T00:16:45.0549014Z makes the implementation visible to these subsystems, allowing them 2024-12-18T00:16:45.0549210Z to optimize the triton kernel(s). 2024-12-18T00:16:45.0549361Z 2024-12-18T00:16:45.0549687Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-12-18T00:16:45.0550083Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-12-18T00:16:45.0550438Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2024-12-18T00:16:45.0550576Z 2024-12-18T00:16:45.0550733Z Args: 2024-12-18T00:16:45.0551125Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-12-18T00:16:45.0551505Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-12-18T00:16:45.0551783Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-12-18T00:16:45.0552194Z To avoid name collisions, please use your project name as the namespace; 2024-12-18T00:16:45.0552567Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-12-18T00:16:45.0553123Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-12-18T00:16:45.0553559Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-12-18T00:16:45.0554054Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-12-18T00:16:45.0554412Z schema (None | str): A schema string for the operator. If None 2024-12-18T00:16:45.0554769Z (recommended) we'll infer a schema for the operator from its type 2024-12-18T00:16:45.0555139Z annotations. We recommend letting us infer a schema unless you 2024-12-18T00:16:45.0555334Z have a specific reason not to. 2024-12-18T00:16:45.0555583Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-12-18T00:16:45.0555725Z 2024-12-18T00:16:45.0555877Z Example:: 2024-12-18T00:16:45.0556026Z 2024-12-18T00:16:45.0556260Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:45.0556415Z >>> import torch 2024-12-18T00:16:45.0556686Z >>> from torch._library import triton_op, wrap_triton 2024-12-18T00:16:45.0556829Z >>> 2024-12-18T00:16:45.0557000Z >>> import triton 2024-12-18T00:16:45.0557204Z >>> from triton import language as tl 2024-12-18T00:16:45.0557359Z >>> 2024-12-18T00:16:45.0557510Z >>> @triton.jit 2024-12-18T00:16:45.0557663Z >>> def add_kernel( 2024-12-18T00:16:45.0557823Z >>> in_ptr0, 2024-12-18T00:16:45.0557973Z >>> in_ptr1, 2024-12-18T00:16:45.0558130Z >>> out_ptr, 2024-12-18T00:16:45.0558284Z >>> n_elements, 2024-12-18T00:16:45.0558554Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T00:16:45.0558709Z >>> ): 2024-12-18T00:16:45.0558895Z >>> pid = tl.program_id(axis=0) 2024-12-18T00:16:45.0559106Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T00:16:45.0559362Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T00:16:45.0559556Z >>> mask = offsets < n_elements 2024-12-18T00:16:45.0559768Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T00:16:45.0559981Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T00:16:45.0560163Z >>> output = x + y 2024-12-18T00:16:45.0560408Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T00:16:45.0560558Z >>> 2024-12-18T00:16:45.0560773Z >>> @triton_op("mylib::add", mutates_args={}) 2024-12-18T00:16:45.0561072Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-12-18T00:16:45.0561279Z >>> output = torch.empty_like(x) 2024-12-18T00:16:45.0561467Z >>> n_elements = output.numel() 2024-12-18T00:16:45.0561622Z >>> 2024-12-18T00:16:45.0561784Z >>> def grid(meta): 2024-12-18T00:16:45.0562087Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T00:16:45.0562231Z >>> 2024-12-18T00:16:45.0562543Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2024-12-18T00:16:45.0562882Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-12-18T00:16:45.0563040Z >>> return output 2024-12-18T00:16:45.0563193Z >>> 2024-12-18T00:16:45.0563345Z >>> @torch.compile 2024-12-18T00:16:45.0563509Z >>> def f(x, y): 2024-12-18T00:16:45.0563669Z >>> return add(x, y) 2024-12-18T00:16:45.0563808Z >>> 2024-12-18T00:16:45.0564013Z >>> x = torch.randn(3, device="cuda") 2024-12-18T00:16:45.0564202Z >>> y = torch.randn(3, device="cuda") 2024-12-18T00:16:45.0564357Z >>> 2024-12-18T00:16:45.0564544Z >>> z = f(x, y) 2024-12-18T00:16:45.0564771Z >>> assert torch.allclose(z, x + y) 2024-12-18T00:16:45.0564920Z 2024-12-18T00:16:45.0565063Z 2024-12-18T00:16:45.0565523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0565657Z 2024-12-18T00:16:45.0565818Z warnings.warn(msg) 2024-12-18T00:16:45.0566001Z 2024-12-18T00:16:45.0566364Z --- Parse Warning: 21 / 105 --- 2024-12-18T00:16:45.0567984Z /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=183. 2024-12-18T00:16:45.0568446Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0568794Z Allows capture of a triton kernel into a graph via make_fx or 2024-12-18T00:16:45.0568973Z non-strict ``torch.export``. 2024-12-18T00:16:45.0569116Z 2024-12-18T00:16:45.0569446Z These technologies perform Dispatcher-based tracing (via 2024-12-18T00:16:45.0569789Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-12-18T00:16:45.0570143Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2024-12-18T00:16:45.0570343Z can actually be traced into a graph. 2024-12-18T00:16:45.0570495Z 2024-12-18T00:16:45.0570856Z Please use this API together with :func:`torch.library.triton_op`. 2024-12-18T00:16:45.0570994Z 2024-12-18T00:16:45.0571152Z Examples: 2024-12-18T00:16:45.0571288Z 2024-12-18T00:16:45.0571470Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0571628Z >>> import torch 2024-12-18T00:16:45.0571786Z >>> import triton 2024-12-18T00:16:45.0571997Z >>> from triton import language as tl 2024-12-18T00:16:45.0572302Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T00:16:45.0572542Z >>> from torch.library import wrap_triton 2024-12-18T00:16:45.0572684Z >>> 2024-12-18T00:16:45.0572853Z >>> @triton.jit 2024-12-18T00:16:45.0573009Z >>> def add_kernel( 2024-12-18T00:16:45.0573158Z >>> in_ptr0, 2024-12-18T00:16:45.0573317Z >>> in_ptr1, 2024-12-18T00:16:45.0573468Z >>> out_ptr, 2024-12-18T00:16:45.0573823Z >>> n_elements, 2024-12-18T00:16:45.0574023Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T00:16:45.0574163Z >>> ): 2024-12-18T00:16:45.0574363Z >>> pid = tl.program_id(axis=0) 2024-12-18T00:16:45.0574554Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T00:16:45.0574821Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T00:16:45.0575000Z >>> mask = offsets < n_elements 2024-12-18T00:16:45.0575208Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T00:16:45.0575433Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T00:16:45.0575594Z >>> output = x + y 2024-12-18T00:16:45.0575851Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T00:16:45.0575991Z >>> 2024-12-18T00:16:45.0576156Z >>> def add(x, y): 2024-12-18T00:16:45.0576345Z >>> output = torch.empty_like(x) 2024-12-18T00:16:45.0576532Z >>> n_elements = output.numel() 2024-12-18T00:16:45.0576683Z >>> 2024-12-18T00:16:45.0576848Z >>> def grid_fn(meta): 2024-12-18T00:16:45.0577151Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T00:16:45.0577288Z >>> 2024-12-18T00:16:45.0577635Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-12-18T00:16:45.0577793Z >>> return output 2024-12-18T00:16:45.0577928Z >>> 2024-12-18T00:16:45.0578216Z >>> x = torch.randn(3, device="cuda") 2024-12-18T00:16:45.0578447Z >>> y = torch.randn(3, device="cuda") 2024-12-18T00:16:45.0578630Z >>> gm = make_fx(add)(x, y) 2024-12-18T00:16:45.0578787Z >>> print(gm.code) 2024-12-18T00:16:45.0578976Z >>> # def forward(self, x_1, y_1): 2024-12-18T00:16:45.0579481Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-12-18T00:16:45.0579904Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-12-18T00:16:45.0580130Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-12-18T00:16:45.0580314Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-12-18T00:16:45.0580584Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-12-18T00:16:45.0580788Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-12-18T00:16:45.0580935Z >>> # }) 2024-12-18T00:16:45.0581119Z >>> # return empty_like 2024-12-18T00:16:45.0581252Z 2024-12-18T00:16:45.0581405Z 2024-12-18T00:16:45.0581852Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0581987Z 2024-12-18T00:16:45.0582163Z warnings.warn(msg) 2024-12-18T00:16:45.0582298Z 2024-12-18T00:16:45.0582649Z --- Parse Warning: 22 / 105 --- 2024-12-18T00:16:45.0584370Z /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=330. 2024-12-18T00:16:45.0584844Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0584982Z 2024-12-18T00:16:45.0585339Z Raises an AssertionError if two items are not equal up to desired 2024-12-18T00:16:45.0585500Z precision. 2024-12-18T00:16:45.0585638Z 2024-12-18T00:16:45.0585948Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T00:16:45.0586264Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T00:16:45.0586597Z instead of this function for more consistent floating point 2024-12-18T00:16:45.0586749Z comparisons. 2024-12-18T00:16:45.0586891Z 2024-12-18T00:16:45.0587282Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-12-18T00:16:45.0587418Z 2024-12-18T00:16:45.0587699Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-12-18T00:16:45.0587835Z 2024-12-18T00:16:45.0588229Z That is a looser test than originally documented, but agrees with what the 2024-12-18T00:16:45.0588654Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-12-18T00:16:45.0589066Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-12-18T00:16:45.0589280Z delegates to assert_array_almost_equal 2024-12-18T00:16:45.0589421Z 2024-12-18T00:16:45.0589588Z Parameters 2024-12-18T00:16:45.0589739Z ---------- 2024-12-18T00:16:45.0589891Z actual : array_like 2024-12-18T00:16:45.0590064Z The object to check. 2024-12-18T00:16:45.0590220Z desired : array_like 2024-12-18T00:16:45.0590397Z The expected object. 2024-12-18T00:16:45.0590560Z decimal : int, optional 2024-12-18T00:16:45.0590752Z Desired precision, default is 7. 2024-12-18T00:16:45.0590938Z err_msg : str, optional 2024-12-18T00:16:45.0591199Z The error message to be printed in case of failure. 2024-12-18T00:16:45.0591373Z verbose : bool, optional 2024-12-18T00:16:45.0591727Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:45.0591868Z 2024-12-18T00:16:45.0592006Z Raises 2024-12-18T00:16:45.0592142Z ------ 2024-12-18T00:16:45.0592304Z AssertionError 2024-12-18T00:16:45.0592675Z If actual and desired are not equal up to specified precision. 2024-12-18T00:16:45.0592874Z 2024-12-18T00:16:45.0593016Z See Also 2024-12-18T00:16:45.0593156Z -------- 2024-12-18T00:16:45.0593581Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:45.0593791Z relative and/or absolute precision. 2024-12-18T00:16:45.0594230Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:45.0594359Z 2024-12-18T00:16:45.0594497Z Examples 2024-12-18T00:16:45.0594652Z -------- 2024-12-18T00:16:45.0594928Z >>> from torch._numpy.testing import assert_almost_equal 2024-12-18T00:16:45.0595159Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-12-18T00:16:45.0595453Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-12-18T00:16:45.0595652Z Traceback (most recent call last): 2024-12-18T00:16:45.0595787Z ... 2024-12-18T00:16:45.0595938Z AssertionError: 2024-12-18T00:16:45.0596159Z Arrays are not almost equal to 10 decimals 2024-12-18T00:16:45.0596308Z ACTUAL: 2.3333333333333 2024-12-18T00:16:45.0596468Z DESIRED: 2.33333334 2024-12-18T00:16:45.0596597Z 2024-12-18T00:16:45.0596841Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-12-18T00:16:45.0597056Z ... np.array([1.0,2.33333334]), decimal=9) 2024-12-18T00:16:45.0597246Z Traceback (most recent call last): 2024-12-18T00:16:45.0597391Z ... 2024-12-18T00:16:45.0597544Z AssertionError: 2024-12-18T00:16:45.0597746Z Arrays are not almost equal to 9 decimals 2024-12-18T00:16:45.0597898Z 2024-12-18T00:16:45.0598073Z Mismatched elements: 1 / 2 (50%) 2024-12-18T00:16:45.0598301Z Max absolute difference: 6.666699636781459e-09 2024-12-18T00:16:45.0598577Z Max relative difference: 2.8571569790287484e-09 2024-12-18T00:16:45.0598817Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T00:16:45.0599047Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T00:16:45.0599178Z 2024-12-18T00:16:45.0599322Z 2024-12-18T00:16:45.0599767Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0599911Z 2024-12-18T00:16:45.0600069Z warnings.warn(msg) 2024-12-18T00:16:45.0600202Z 2024-12-18T00:16:45.0600541Z --- Parse Warning: 23 / 105 --- 2024-12-18T00:16:45.0602272Z /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=455. 2024-12-18T00:16:45.0602748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0602881Z 2024-12-18T00:16:45.0603282Z Raises an AssertionError if two items are not equal up to significant 2024-12-18T00:16:45.0603416Z digits. 2024-12-18T00:16:45.0603554Z 2024-12-18T00:16:45.0603854Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T00:16:45.0604167Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T00:16:45.0604498Z instead of this function for more consistent floating point 2024-12-18T00:16:45.0604649Z comparisons. 2024-12-18T00:16:45.0604799Z 2024-12-18T00:16:45.0605119Z Given two numbers, check that they are approximately equal. 2024-12-18T00:16:45.0605500Z Approximately equal is defined as the number of significant digits 2024-12-18T00:16:45.0605657Z that agree. 2024-12-18T00:16:45.0605788Z 2024-12-18T00:16:45.0605941Z Parameters 2024-12-18T00:16:45.0606088Z ---------- 2024-12-18T00:16:45.0606238Z actual : scalar 2024-12-18T00:16:45.0606414Z The object to check. 2024-12-18T00:16:45.0606565Z desired : scalar 2024-12-18T00:16:45.0606743Z The expected object. 2024-12-18T00:16:45.0606908Z significant : int, optional 2024-12-18T00:16:45.0607189Z Desired precision, default is 7. 2024-12-18T00:16:45.0607365Z err_msg : str, optional 2024-12-18T00:16:45.0607625Z The error message to be printed in case of failure. 2024-12-18T00:16:45.0607805Z verbose : bool, optional 2024-12-18T00:16:45.0608159Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:45.0608343Z 2024-12-18T00:16:45.0608483Z Raises 2024-12-18T00:16:45.0608660Z ------ 2024-12-18T00:16:45.0608821Z AssertionError 2024-12-18T00:16:45.0609154Z If actual and desired are not equal up to specified precision. 2024-12-18T00:16:45.0609302Z 2024-12-18T00:16:45.0609444Z See Also 2024-12-18T00:16:45.0609584Z -------- 2024-12-18T00:16:45.0610012Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:45.0610232Z relative and/or absolute precision. 2024-12-18T00:16:45.0610613Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:45.0610755Z 2024-12-18T00:16:45.0610913Z Examples 2024-12-18T00:16:45.0611056Z -------- 2024-12-18T00:16:45.0611535Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-12-18T00:16:45.0611991Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-12-18T00:16:45.0612183Z ... significant=8) 2024-12-18T00:16:45.0612641Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-12-18T00:16:45.0612833Z ... significant=8) 2024-12-18T00:16:45.0613022Z Traceback (most recent call last): 2024-12-18T00:16:45.0613179Z ... 2024-12-18T00:16:45.0613336Z AssertionError: 2024-12-18T00:16:45.0613573Z Items are not equal to 8 significant digits: 2024-12-18T00:16:45.0613731Z ACTUAL: 1.234567e-21 2024-12-18T00:16:45.0613904Z DESIRED: 1.2345672e-21 2024-12-18T00:16:45.0614048Z 2024-12-18T00:16:45.0614329Z the evaluated condition that raises the exception is 2024-12-18T00:16:45.0614484Z 2024-12-18T00:16:45.0614773Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-12-18T00:16:45.0614928Z True 2024-12-18T00:16:45.0615065Z 2024-12-18T00:16:45.0615199Z 2024-12-18T00:16:45.0615664Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0615803Z 2024-12-18T00:16:45.0615979Z warnings.warn(msg) 2024-12-18T00:16:45.0616118Z 2024-12-18T00:16:45.0616435Z --- Parse Warning: 24 / 105 --- 2024-12-18T00:16:45.0618158Z /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=734. 2024-12-18T00:16:45.0618623Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0618782Z 2024-12-18T00:16:45.0619137Z Raises an AssertionError if two array_like objects are not equal. 2024-12-18T00:16:45.0619286Z 2024-12-18T00:16:45.0619643Z Given two array_like objects, check that the shape is equal and all 2024-12-18T00:16:45.0620024Z elements of these objects are equal (but see the Notes for the special 2024-12-18T00:16:45.0620385Z handling of a scalar). An exception is raised at shape mismatch or 2024-12-18T00:16:45.0620774Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-12-18T00:16:45.0621172Z are compared like numbers, no assertion is raised if both objects have 2024-12-18T00:16:45.0621346Z NaNs in the same positions. 2024-12-18T00:16:45.0621491Z 2024-12-18T00:16:45.0621890Z The usual caution for verifying equality with floating point numbers is 2024-12-18T00:16:45.0622027Z advised. 2024-12-18T00:16:45.0622178Z 2024-12-18T00:16:45.0622319Z Parameters 2024-12-18T00:16:45.0622513Z ---------- 2024-12-18T00:16:45.0622693Z x : array_like 2024-12-18T00:16:45.0622866Z The actual object to check. 2024-12-18T00:16:45.0623021Z y : array_like 2024-12-18T00:16:45.0623204Z The desired, expected object. 2024-12-18T00:16:45.0623384Z err_msg : str, optional 2024-12-18T00:16:45.0623675Z The error message to be printed in case of failure. 2024-12-18T00:16:45.0623886Z verbose : bool, optional 2024-12-18T00:16:45.0624241Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:45.0624402Z strict : bool, optional 2024-12-18T00:16:45.0624758Z If True, raise an AssertionError when either the shape or the data 2024-12-18T00:16:45.0625056Z type of the array_like objects does not match. The special 2024-12-18T00:16:45.0625426Z handling for scalars mentioned in the Notes section is disabled. 2024-12-18T00:16:45.0625559Z 2024-12-18T00:16:45.0625698Z Raises 2024-12-18T00:16:45.0625856Z ------ 2024-12-18T00:16:45.0626010Z AssertionError 2024-12-18T00:16:45.0626249Z If actual and desired objects are not equal. 2024-12-18T00:16:45.0626386Z 2024-12-18T00:16:45.0626524Z See Also 2024-12-18T00:16:45.0626675Z -------- 2024-12-18T00:16:45.0627092Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:45.0627320Z relative and/or absolute precision. 2024-12-18T00:16:45.0627688Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:45.0627835Z 2024-12-18T00:16:45.0627975Z Notes 2024-12-18T00:16:45.0628113Z ----- 2024-12-18T00:16:45.0628438Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-12-18T00:16:45.0628834Z function checks that each element of the array_like object is equal to 2024-12-18T00:16:45.0629239Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-12-18T00:16:45.0629376Z 2024-12-18T00:16:45.0629517Z Examples 2024-12-18T00:16:45.0629674Z -------- 2024-12-18T00:16:45.0629899Z The first assert does not raise an exception: 2024-12-18T00:16:45.0630051Z 2024-12-18T00:16:45.0630311Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-12-18T00:16:45.0630531Z ... [np.exp(0),2.33333, np.nan]) 2024-12-18T00:16:45.0630668Z 2024-12-18T00:16:45.0631070Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-12-18T00:16:45.0631276Z functions for these cases instead: 2024-12-18T00:16:45.0631413Z 2024-12-18T00:16:45.0631665Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-12-18T00:16:45.0631870Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-12-18T00:16:45.0632052Z ... rtol=1e-10, atol=0) 2024-12-18T00:16:45.0632202Z 2024-12-18T00:16:45.0632560Z As mentioned in the Notes section, `assert_array_equal` has special 2024-12-18T00:16:45.0632974Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-12-18T00:16:45.0633109Z 2024-12-18T00:16:45.0633299Z >>> x = np.full((2, 5), fill_value=3) 2024-12-18T00:16:45.0633499Z >>> np.testing.assert_array_equal(x, 3) 2024-12-18T00:16:45.0633637Z 2024-12-18T00:16:45.0634029Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-12-18T00:16:45.0634171Z array: 2024-12-18T00:16:45.0634324Z 2024-12-18T00:16:45.0634576Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-12-18T00:16:45.0634762Z Traceback (most recent call last): 2024-12-18T00:16:45.0634919Z ... 2024-12-18T00:16:45.0635073Z AssertionError: 2024-12-18T00:16:45.0635251Z Arrays are not equal 2024-12-18T00:16:45.0635397Z 2024-12-18T00:16:45.0635559Z (shapes (2, 5), () mismatch) 2024-12-18T00:16:45.0635745Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-12-18T00:16:45.0635893Z [3, 3, 3, 3, 3]]) 2024-12-18T00:16:45.0636143Z y: torch.ndarray(3) 2024-12-18T00:16:45.0636276Z 2024-12-18T00:16:45.0636659Z The `strict` parameter also ensures that the array data types match: 2024-12-18T00:16:45.0636791Z 2024-12-18T00:16:45.0636951Z >>> x = np.array([2, 2, 2]) 2024-12-18T00:16:45.0637181Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-12-18T00:16:45.0637464Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-12-18T00:16:45.0637696Z Traceback (most recent call last): 2024-12-18T00:16:45.0637833Z ... 2024-12-18T00:16:45.0637988Z AssertionError: 2024-12-18T00:16:45.0638167Z Arrays are not equal 2024-12-18T00:16:45.0638315Z 2024-12-18T00:16:45.0638654Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-12-18T00:16:45.0638824Z x: torch.ndarray([2, 2, 2]) 2024-12-18T00:16:45.0638994Z y: torch.ndarray([2., 2., 2.]) 2024-12-18T00:16:45.0639140Z 2024-12-18T00:16:45.0639587Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0639740Z 2024-12-18T00:16:45.0639896Z warnings.warn(msg) 2024-12-18T00:16:45.0640042Z 2024-12-18T00:16:45.0640372Z --- Parse Warning: 25 / 105 --- 2024-12-18T00:16:45.0642139Z /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=840. 2024-12-18T00:16:45.0642613Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0642746Z 2024-12-18T00:16:45.0643135Z Raises an AssertionError if two objects are not equal up to desired 2024-12-18T00:16:45.0643283Z precision. 2024-12-18T00:16:45.0643428Z 2024-12-18T00:16:45.0643721Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T00:16:45.0644026Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T00:16:45.0644364Z instead of this function for more consistent floating point 2024-12-18T00:16:45.0644518Z comparisons. 2024-12-18T00:16:45.0644666Z 2024-12-18T00:16:45.0645089Z The test verifies identical shapes and that the elements of ``actual`` and 2024-12-18T00:16:45.0645242Z ``desired`` satisfy. 2024-12-18T00:16:45.0645392Z 2024-12-18T00:16:45.0645613Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-12-18T00:16:45.0645762Z 2024-12-18T00:16:45.0646161Z That is a looser test than originally documented, but agrees with what the 2024-12-18T00:16:45.0646597Z actual implementation did up to rounding vagaries. An exception is raised 2024-12-18T00:16:45.0647011Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-12-18T00:16:45.0647391Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-12-18T00:16:45.0647611Z objects have NaNs in the same positions. 2024-12-18T00:16:45.0647752Z 2024-12-18T00:16:45.0647914Z Parameters 2024-12-18T00:16:45.0648061Z ---------- 2024-12-18T00:16:45.0648208Z x : array_like 2024-12-18T00:16:45.0648391Z The actual object to check. 2024-12-18T00:16:45.0648532Z y : array_like 2024-12-18T00:16:45.0648728Z The desired, expected object. 2024-12-18T00:16:45.0648893Z decimal : int, optional 2024-12-18T00:16:45.0649104Z Desired precision, default is 6. 2024-12-18T00:16:45.0649260Z err_msg : str, optional 2024-12-18T00:16:45.0649518Z The error message to be printed in case of failure. 2024-12-18T00:16:45.0649696Z verbose : bool, optional 2024-12-18T00:16:45.0650047Z If True, the conflicting values are appended to the error message. 2024-12-18T00:16:45.0650195Z 2024-12-18T00:16:45.0650336Z Raises 2024-12-18T00:16:45.0650477Z ------ 2024-12-18T00:16:45.0650643Z AssertionError 2024-12-18T00:16:45.0650977Z If actual and desired are not equal up to specified precision. 2024-12-18T00:16:45.0651227Z 2024-12-18T00:16:45.0651367Z See Also 2024-12-18T00:16:45.0651515Z -------- 2024-12-18T00:16:45.0651943Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T00:16:45.0652152Z relative and/or absolute precision. 2024-12-18T00:16:45.0652570Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T00:16:45.0652742Z 2024-12-18T00:16:45.0652900Z Examples 2024-12-18T00:16:45.0653047Z -------- 2024-12-18T00:16:45.0653262Z the first assert does not raise an exception 2024-12-18T00:16:45.0653411Z 2024-12-18T00:16:45.0653705Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-12-18T00:16:45.0653910Z ... [1.0,2.333,np.nan]) 2024-12-18T00:16:45.0654048Z 2024-12-18T00:16:45.0654355Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T00:16:45.0654584Z ... [1.0,2.33339,np.nan], decimal=5) 2024-12-18T00:16:45.0654774Z Traceback (most recent call last): 2024-12-18T00:16:45.0654930Z ... 2024-12-18T00:16:45.0655083Z AssertionError: 2024-12-18T00:16:45.0655292Z Arrays are not almost equal to 5 decimals 2024-12-18T00:16:45.0655457Z 2024-12-18T00:16:45.0655638Z Mismatched elements: 1 / 3 (33.3%) 2024-12-18T00:16:45.0655875Z Max absolute difference: 5.999999999994898e-05 2024-12-18T00:16:45.0656094Z Max relative difference: 2.5713661239633743e-05 2024-12-18T00:16:45.0656380Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T00:16:45.0656653Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-12-18T00:16:45.0656787Z 2024-12-18T00:16:45.0657103Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T00:16:45.0657301Z ... [1.0,2.33333, 5], decimal=5) 2024-12-18T00:16:45.0657505Z Traceback (most recent call last): 2024-12-18T00:16:45.0657648Z ... 2024-12-18T00:16:45.0657822Z AssertionError: 2024-12-18T00:16:45.0658023Z Arrays are not almost equal to 5 decimals 2024-12-18T00:16:45.0658170Z 2024-12-18T00:16:45.0658364Z x and y nan location mismatch: 2024-12-18T00:16:45.0658640Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T00:16:45.0658928Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-12-18T00:16:45.0659065Z 2024-12-18T00:16:45.0659204Z 2024-12-18T00:16:45.0659661Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0659800Z 2024-12-18T00:16:45.0659976Z warnings.warn(msg) 2024-12-18T00:16:45.0660113Z 2024-12-18T00:16:45.0660433Z --- Parse Warning: 26 / 105 --- 2024-12-18T00:16:45.0662211Z /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=1790. 2024-12-18T00:16:45.0662677Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0663065Z Context manager that resets warning registry for catching warnings 2024-12-18T00:16:45.0663200Z 2024-12-18T00:16:45.0663642Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-12-18T00:16:45.0664039Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-12-18T00:16:45.0664469Z it impossible to retrigger the warning in this module, whatever you put in 2024-12-18T00:16:45.0664897Z the warnings filters. This context manager accepts a sequence of `modules` 2024-12-18T00:16:45.0665128Z as a keyword argument to its constructor and: 2024-12-18T00:16:45.0665281Z 2024-12-18T00:16:45.0665678Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-12-18T00:16:45.0665904Z on entry; 2024-12-18T00:16:45.0666224Z * resets ``__warningregistry__`` to its previous state on exit. 2024-12-18T00:16:45.0666361Z 2024-12-18T00:16:45.0666760Z This makes it possible to trigger any warning afresh inside the context 2024-12-18T00:16:45.0667073Z manager without disturbing the state of warnings outside. 2024-12-18T00:16:45.0667253Z 2024-12-18T00:16:45.0667696Z For compatibility with Python 3.0, please consider all arguments to be 2024-12-18T00:16:45.0667869Z keyword-only. 2024-12-18T00:16:45.0668006Z 2024-12-18T00:16:45.0668154Z Parameters 2024-12-18T00:16:45.0668311Z ---------- 2024-12-18T00:16:45.0668478Z record : bool, optional 2024-12-18T00:16:45.0668814Z Specifies whether warnings should be captured by a custom 2024-12-18T00:16:45.0669227Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-12-18T00:16:45.0669594Z returned by the context manager. Otherwise None is returned by the 2024-12-18T00:16:45.0670013Z context manager. The objects appended to the list are arguments whose 2024-12-18T00:16:45.0670301Z attributes mirror the arguments to ``showwarning()``. 2024-12-18T00:16:45.0670494Z modules : sequence, optional 2024-12-18T00:16:45.0670886Z Sequence of modules for which to reset warnings registry on entry and 2024-12-18T00:16:45.0671230Z restore on exit. To work correctly, all 'ignore' filters should 2024-12-18T00:16:45.0671418Z filter by one of these modules. 2024-12-18T00:16:45.0671555Z 2024-12-18T00:16:45.0671704Z Examples 2024-12-18T00:16:45.0671851Z -------- 2024-12-18T00:16:45.0672025Z >>> import warnings 2024-12-18T00:16:45.0672343Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-12-18T00:16:45.0672559Z ... modules=[np.core.fromnumeric]): 2024-12-18T00:16:45.0672765Z ... warnings.simplefilter('always') 2024-12-18T00:16:45.0673154Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-12-18T00:16:45.0673460Z ... # do something that raises a warning but ignore those in 2024-12-18T00:16:45.0673777Z ... # np.core.fromnumeric 2024-12-18T00:16:45.0673934Z 2024-12-18T00:16:45.0674380Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0674512Z 2024-12-18T00:16:45.0674694Z warnings.warn(msg) 2024-12-18T00:16:45.0674834Z 2024-12-18T00:16:45.0675164Z --- Parse Warning: 27 / 105 --- 2024-12-18T00:16:45.0676867Z /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. 2024-12-18T00:16:45.0677343Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0677715Z Applies a 1D convolution over a quantized input signal composed of 2024-12-18T00:16:45.0677909Z several quantized input planes. 2024-12-18T00:16:45.0678067Z 2024-12-18T00:16:45.0678510Z For details on input arguments, parameters, and implementation see 2024-12-18T00:16:45.0678699Z :class:`~torch.nn.Conv1d`. 2024-12-18T00:16:45.0678835Z 2024-12-18T00:16:45.0679007Z .. note:: 2024-12-18T00:16:45.0679347Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-12-18T00:16:45.0679487Z 2024-12-18T00:16:45.0679643Z .. note:: 2024-12-18T00:16:45.0679950Z Only `torch.quint8` is supported for the input data type. 2024-12-18T00:16:45.0680098Z 2024-12-18T00:16:45.0680236Z 2024-12-18T00:16:45.0680386Z Attributes: 2024-12-18T00:16:45.0680764Z weight (Tensor): packed tensor derived from the learnable weight 2024-12-18T00:16:45.0681020Z parameter. 2024-12-18T00:16:45.0681323Z scale (Tensor): scalar for the output scale 2024-12-18T00:16:45.0681605Z zero_point (Tensor): scalar for the output zero point 2024-12-18T00:16:45.0681759Z 2024-12-18T00:16:45.0682015Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-12-18T00:16:45.0682149Z 2024-12-18T00:16:45.0682450Z Examples:: 2024-12-18T00:16:45.0682623Z 2024-12-18T00:16:45.0682888Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-12-18T00:16:45.0683108Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-12-18T00:16:45.0683291Z >>> input = torch.randn(20, 16, 100) 2024-12-18T00:16:45.0683490Z >>> # quantize input to quint8 2024-12-18T00:16:45.0683652Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0684029Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-12-18T00:16:45.0684240Z ... dtype=torch.quint8) 2024-12-18T00:16:45.0684427Z >>> output = m(q_input) 2024-12-18T00:16:45.0684567Z 2024-12-18T00:16:45.0684703Z 2024-12-18T00:16:45.0685162Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0685297Z 2024-12-18T00:16:45.0685471Z warnings.warn(msg) 2024-12-18T00:16:45.0685606Z 2024-12-18T00:16:45.0685924Z --- Parse Warning: 28 / 105 --- 2024-12-18T00:16:45.0687595Z /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. 2024-12-18T00:16:45.0688059Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0688287Z A quantized long short-term memory (LSTM). 2024-12-18T00:16:45.0688426Z 2024-12-18T00:16:45.0688929Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-12-18T00:16:45.0689063Z 2024-12-18T00:16:45.0689211Z Attributes: 2024-12-18T00:16:45.0689423Z layers : instances of the `_LSTMLayer` 2024-12-18T00:16:45.0689559Z 2024-12-18T00:16:45.0689711Z .. note:: 2024-12-18T00:16:45.0690081Z To access the weights and biases, you need to access them per layer. 2024-12-18T00:16:45.0690370Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-12-18T00:16:45.0690517Z 2024-12-18T00:16:45.0690665Z Examples:: 2024-12-18T00:16:45.0690839Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0691014Z >>> custom_module_config = { 2024-12-18T00:16:45.0691253Z ... 'float_to_observed_custom_module_class': { 2024-12-18T00:16:45.0691459Z ... nn.LSTM: nn.quantizable.LSTM, 2024-12-18T00:16:45.0691595Z ... }, 2024-12-18T00:16:45.0691867Z ... 'observed_to_quantized_custom_module_class': { 2024-12-18T00:16:45.0692106Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-12-18T00:16:45.0692264Z ... } 2024-12-18T00:16:45.0692399Z ... } 2024-12-18T00:16:45.0692783Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-12-18T00:16:45.0693171Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-12-18T00:16:45.0693313Z 2024-12-18T00:16:45.0693634Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0693722Z 2024-12-18T00:16:45.0693840Z warnings.warn(msg) 2024-12-18T00:16:45.0693926Z 2024-12-18T00:16:45.0694115Z --- Parse Warning: 29 / 105 --- 2024-12-18T00:16:45.0695185Z /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. 2024-12-18T00:16:45.0695511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0695698Z Squashes the sparse masks into the appropriate tensors. 2024-12-18T00:16:45.0695785Z 2024-12-18T00:16:45.0696036Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-12-18T00:16:45.0696269Z the module will have a `sparse_params` dict attached to it. 2024-12-18T00:16:45.0696355Z 2024-12-18T00:16:45.0696458Z Args: 2024-12-18T00:16:45.0696646Z params_to_keep: List of keys to save in the module or a dict 2024-12-18T00:16:45.0696821Z representing the modules and keys that will have 2024-12-18T00:16:45.0696948Z sparsity parameters saved 2024-12-18T00:16:45.0697173Z params_to_keep_per_layer: Dict to specify the params that should be 2024-12-18T00:16:45.0697336Z saved for specific layers. The keys in the dict 2024-12-18T00:16:45.0697498Z should be the module fqn, while the values should 2024-12-18T00:16:45.0697675Z be a list of strings with the names of the variables 2024-12-18T00:16:45.0697806Z to save in the `sparse_params` 2024-12-18T00:16:45.0697915Z 2024-12-18T00:16:45.0698010Z Examples: 2024-12-18T00:16:45.0698161Z >>> # xdoctest: +SKIP("locals are undefined") 2024-12-18T00:16:45.0698280Z >>> # Don't save any sparse params 2024-12-18T00:16:45.0698400Z >>> sparsifier.squash_mask() 2024-12-18T00:16:45.0698557Z >>> hasattr(model.submodule1, 'sparse_params') 2024-12-18T00:16:45.0698648Z False 2024-12-18T00:16:45.0698748Z 2024-12-18T00:16:45.0698869Z >>> # Keep sparse params per layer 2024-12-18T00:16:45.0698986Z >>> sparsifier.squash_mask( 2024-12-18T00:16:45.0699122Z ... params_to_keep_per_layer={ 2024-12-18T00:16:45.0699259Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-12-18T00:16:45.0699405Z ... 'submodule2.linear42': ('baz',) 2024-12-18T00:16:45.0699501Z ... }) 2024-12-18T00:16:45.0699678Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T00:16:45.0699783Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:45.0699944Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T00:16:45.0700054Z {'baz': 0.1} 2024-12-18T00:16:45.0700141Z 2024-12-18T00:16:45.0700281Z >>> # Keep sparse params for all layers 2024-12-18T00:16:45.0700460Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-12-18T00:16:45.0700629Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T00:16:45.0700735Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:45.0700899Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T00:16:45.0701015Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:45.0701107Z 2024-12-18T00:16:45.0701319Z >>> # Keep some sparse params for all layers, and specific ones for 2024-12-18T00:16:45.0701427Z >>> # some other layers 2024-12-18T00:16:45.0701547Z >>> sparsifier.squash_mask( 2024-12-18T00:16:45.0701683Z ... params_to_keep=('foo', 'bar'), 2024-12-18T00:16:45.0701803Z ... params_to_keep_per_layer={ 2024-12-18T00:16:45.0701944Z ... 'submodule2.linear42': ('baz',) 2024-12-18T00:16:45.0702036Z ... }) 2024-12-18T00:16:45.0702211Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T00:16:45.0702314Z {'foo': 42, 'bar': 24} 2024-12-18T00:16:45.0702509Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T00:16:45.0702658Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-12-18T00:16:45.0702747Z 2024-12-18T00:16:45.0703014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0703101Z 2024-12-18T00:16:45.0703201Z warnings.warn(msg) 2024-12-18T00:16:45.0703322Z 2024-12-18T00:16:45.0703539Z --- Parse Warning: 30 / 105 --- 2024-12-18T00:16:45.0704586Z /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. 2024-12-18T00:16:45.0704849Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0704944Z 2024-12-18T00:16:45.0705196Z Config object that specifies the supported data types passed as arguments to 2024-12-18T00:16:45.0705451Z quantize ops in the reference model spec, for input and output activations, 2024-12-18T00:16:45.0705549Z weights, and biases. 2024-12-18T00:16:45.0705635Z 2024-12-18T00:16:45.0705810Z For example, consider the following reference model: 2024-12-18T00:16:45.0705896Z 2024-12-18T00:16:45.0706070Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-12-18T00:16:45.0706154Z 2024-12-18T00:16:45.0706370Z The pattern in the square brackets refers to the reference pattern of 2024-12-18T00:16:45.0706616Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-12-18T00:16:45.0706848Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-12-18T00:16:45.0707082Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-12-18T00:16:45.0707303Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-12-18T00:16:45.0707425Z the second quantize op (quant2). 2024-12-18T00:16:45.0707514Z 2024-12-18T00:16:45.0707732Z Note that the dtype here does not refer to the interface dtypes of the 2024-12-18T00:16:45.0707950Z op. For example, the "input dtype" here is not the dtype of the input 2024-12-18T00:16:45.0708168Z tensor passed to the quantized linear op. Though it can still be the 2024-12-18T00:16:45.0708384Z same as the interface dtype, this is not always the case, e.g. the 2024-12-18T00:16:45.0708604Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-12-18T00:16:45.0708827Z specified in the DTypeConfig would still be quint8. The semantics of 2024-12-18T00:16:45.0709036Z dtypes here are the same as the semantics of the dtypes specified in 2024-12-18T00:16:45.0709132Z the observers. 2024-12-18T00:16:45.0709228Z 2024-12-18T00:16:45.0709435Z These dtypes are matched against the ones specified in the user's 2024-12-18T00:16:45.0709659Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-12-18T00:16:45.0709882Z specified in the DTypeConfig (if any), then we will quantize the given 2024-12-18T00:16:45.0710117Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-12-18T00:16:45.0710233Z the pattern will not be quantized. 2024-12-18T00:16:45.0710319Z 2024-12-18T00:16:45.0710428Z Example usage:: 2024-12-18T00:16:45.0710515Z 2024-12-18T00:16:45.0710641Z >>> # xdoctest: +SKIP(failing) 2024-12-18T00:16:45.0710755Z >>> dtype_config1 = DTypeConfig( 2024-12-18T00:16:45.0710868Z ... input_dtype=torch.quint8, 2024-12-18T00:16:45.0710993Z ... output_dtype=torch.quint8, 2024-12-18T00:16:45.0711100Z ... weight_dtype=torch.qint8, 2024-12-18T00:16:45.0711217Z ... bias_dtype=torch.float) 2024-12-18T00:16:45.0711301Z 2024-12-18T00:16:45.0711414Z >>> dtype_config2 = DTypeConfig( 2024-12-18T00:16:45.0711557Z ... input_dtype=DTypeWithConstraints( 2024-12-18T00:16:45.0711713Z ... dtype=torch.quint8, 2024-12-18T00:16:45.0711831Z ... quant_min_lower_bound=0, 2024-12-18T00:16:45.0711947Z ... quant_max_upper_bound=255, 2024-12-18T00:16:45.0712047Z ... ), 2024-12-18T00:16:45.0712179Z ... output_dtype=DTypeWithConstraints( 2024-12-18T00:16:45.0712309Z ... dtype=torch.quint8, 2024-12-18T00:16:45.0712456Z ... quant_min_lower_bound=0, 2024-12-18T00:16:45.0712572Z ... quant_max_upper_bound=255, 2024-12-18T00:16:45.0712672Z ... ), 2024-12-18T00:16:45.0712805Z ... weight_dtype=DTypeWithConstraints( 2024-12-18T00:16:45.0712911Z ... dtype=torch.qint8, 2024-12-18T00:16:45.0713039Z ... quant_min_lower_bound=-128, 2024-12-18T00:16:45.0713153Z ... quant_max_upper_bound=127, 2024-12-18T00:16:45.0713249Z ... ), 2024-12-18T00:16:45.0713355Z ... bias_dtype=torch.float) 2024-12-18T00:16:45.0713460Z 2024-12-18T00:16:45.0713572Z >>> dtype_config1.input_dtype 2024-12-18T00:16:45.0713667Z torch.quint8 2024-12-18T00:16:45.0713768Z 2024-12-18T00:16:45.0713878Z >>> dtype_config2.input_dtype 2024-12-18T00:16:45.0713984Z torch.quint8 2024-12-18T00:16:45.0714068Z 2024-12-18T00:16:45.0714209Z >>> dtype_config2.input_dtype_with_constraints 2024-12-18T00:16:45.0714759Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-12-18T00:16:45.0714845Z 2024-12-18T00:16:45.0715113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0715198Z 2024-12-18T00:16:45.0715299Z warnings.warn(msg) 2024-12-18T00:16:45.0715396Z 2024-12-18T00:16:45.0715590Z --- Parse Warning: 31 / 105 --- 2024-12-18T00:16:45.0716852Z /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. 2024-12-18T00:16:45.0717119Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0717217Z 2024-12-18T00:16:45.0717490Z Takes in optional filter values and generates two tables with desired information. 2024-12-18T00:16:45.0717587Z 2024-12-18T00:16:45.0717797Z The generated tables are presented in both a list-of-lists format 2024-12-18T00:16:45.0717883Z 2024-12-18T00:16:45.0718104Z The reason for the two tables are that they handle different things: 2024-12-18T00:16:45.0718267Z 1.) the first table handles all tensor level information 2024-12-18T00:16:45.0718596Z 2.) the second table handles and displays all channel based information 2024-12-18T00:16:45.0718689Z 2024-12-18T00:16:45.0719009Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T00:16:45.0719349Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T00:16:45.0719708Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T00:16:45.0719806Z 2024-12-18T00:16:45.0719909Z Tensor table columns: 2024-12-18T00:16:45.0720118Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:45.0720275Z ---- --------- --------- --------- --------- --------- 2024-12-18T00:16:45.0720360Z 2024-12-18T00:16:45.0720486Z Per-Channel table columns: 2024-12-18T00:16:45.0720705Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:45.0720887Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T00:16:45.0721031Z 2024-12-18T00:16:45.0721131Z Args: 2024-12-18T00:16:45.0721395Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T00:16:45.0721511Z contain this filter substring 2024-12-18T00:16:45.0721685Z Default = "", results in all the features being printed 2024-12-18T00:16:45.0721999Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:45.0722260Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:45.0722347Z 2024-12-18T00:16:45.0722476Z Returns a dictionary with two keys: 2024-12-18T00:16:45.0722653Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-12-18T00:16:45.0722782Z "tensor_level_info", "channel_level_info" 2024-12-18T00:16:45.0722909Z Each key maps to a tuple with: 2024-12-18T00:16:45.0723035Z A list of the headers of each table 2024-12-18T00:16:45.0723231Z A list of lists containing the table information row by row 2024-12-18T00:16:45.0723405Z The 0th index row will contain the headers of the columns 2024-12-18T00:16:45.0723532Z The rest of the rows will contain data 2024-12-18T00:16:45.0723633Z 2024-12-18T00:16:45.0723729Z Example Use: 2024-12-18T00:16:45.0723878Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.0724032Z >>> mod_report_visualizer.generate_filtered_tables( 2024-12-18T00:16:45.0724167Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:45.0724279Z ... module_fqn_filter = "block1" 2024-12-18T00:16:45.0724559Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-12-18T00:16:45.0724656Z 2024-12-18T00:16:45.0724912Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0725016Z 2024-12-18T00:16:45.0725121Z warnings.warn(msg) 2024-12-18T00:16:45.0725205Z 2024-12-18T00:16:45.0725409Z --- Parse Warning: 32 / 105 --- 2024-12-18T00:16:45.0726675Z /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. 2024-12-18T00:16:45.0726948Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0727033Z 2024-12-18T00:16:45.0727315Z Takes in optional filter values and prints out formatted tables of the information. 2024-12-18T00:16:45.0727401Z 2024-12-18T00:16:45.0727754Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-12-18T00:16:45.0727918Z 1.) the first table handles all tensor level information 2024-12-18T00:16:45.0728145Z 2.) the second table handles and displays all channel based information 2024-12-18T00:16:45.0728239Z 2024-12-18T00:16:45.0728560Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T00:16:45.0728900Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T00:16:45.0729258Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T00:16:45.0729356Z 2024-12-18T00:16:45.0729459Z Tensor table columns: 2024-12-18T00:16:45.0729653Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:45.0729821Z ---- --------- --------- --------- --------- --------- 2024-12-18T00:16:45.0729908Z 2024-12-18T00:16:45.0730033Z Per-Channel table columns: 2024-12-18T00:16:45.0730122Z 2024-12-18T00:16:45.0730344Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T00:16:45.0730585Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T00:16:45.0730671Z 2024-12-18T00:16:45.0730774Z Args: 2024-12-18T00:16:45.0731044Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T00:16:45.0731216Z contain this filter substring 2024-12-18T00:16:45.0731406Z Default = "", results in all the features being printed 2024-12-18T00:16:45.0731667Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:45.0731928Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:45.0732012Z 2024-12-18T00:16:45.0732117Z Example Use: 2024-12-18T00:16:45.0732253Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.0732435Z >>> mod_report_visualizer.generate_table_visualization( 2024-12-18T00:16:45.0732569Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:45.0732685Z ... module_fqn_filter = "block1" 2024-12-18T00:16:45.0732794Z ... ) 2024-12-18T00:16:45.0732990Z >>> # prints out neatly formatted table with per_channel_min info 2024-12-18T00:16:45.0733132Z >>> # for all modules in block 1 of the model 2024-12-18T00:16:45.0733221Z 2024-12-18T00:16:45.0733482Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0733578Z 2024-12-18T00:16:45.0733683Z warnings.warn(msg) 2024-12-18T00:16:45.0733784Z 2024-12-18T00:16:45.0733973Z --- Parse Warning: 33 / 105 --- 2024-12-18T00:16:45.0735245Z /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. 2024-12-18T00:16:45.0735511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0735595Z 2024-12-18T00:16:45.0735847Z Takes in a feature and optional module_filter and plots of the desired data. 2024-12-18T00:16:45.0735934Z 2024-12-18T00:16:45.0736223Z For per channel features, it averages the value across the channels and plots a point 2024-12-18T00:16:45.0736480Z per module. The reason for this is that for models with hundreds of channels, it can 2024-12-18T00:16:45.0736772Z be hard to differentiate one channel line from another, and so the point of generating 2024-12-18T00:16:45.0737046Z a single average point per module is to give a sense of general trends that encourage 2024-12-18T00:16:45.0737161Z further deep dives. 2024-12-18T00:16:45.0737260Z 2024-12-18T00:16:45.0737350Z Note: 2024-12-18T00:16:45.0737624Z Only features in the report that have tensor value data are plottable by this class 2024-12-18T00:16:45.0737795Z When the tensor information is plotted, it will plot: 2024-12-18T00:16:45.0737946Z idx as the x val, feature value as the y_val 2024-12-18T00:16:45.0738114Z When the channel information is plotted, it will plot: 2024-12-18T00:16:45.0738383Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-12-18T00:16:45.0738622Z The reason for this is that we want to be able to compare values across the 2024-12-18T00:16:45.0738856Z channels for same layer, and it will be hard if values are staggered by idx 2024-12-18T00:16:45.0739037Z This means each module is represented by only 1 x value 2024-12-18T00:16:45.0739128Z Args: 2024-12-18T00:16:45.0739363Z feature_filter (str): Filters the features presented to only those that 2024-12-18T00:16:45.0739477Z contain this filter substring 2024-12-18T00:16:45.0739738Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:45.0740055Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:45.0740141Z 2024-12-18T00:16:45.0740248Z Example Use: 2024-12-18T00:16:45.0740377Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.0740577Z >>> mod_report_visualizer.generate_plot_visualization( 2024-12-18T00:16:45.0740730Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:45.0740847Z ... module_fqn_filter = "block1" 2024-12-18T00:16:45.0740952Z ... ) 2024-12-18T00:16:45.0741137Z >>> # outputs line plot of per_channel_min information for all 2024-12-18T00:16:45.0741334Z >>> # modules in block1 of model each channel gets it's own line, 2024-12-18T00:16:45.0741510Z >>> # and it's plotted across the in-order modules on the x-axis 2024-12-18T00:16:45.0741597Z 2024-12-18T00:16:45.0741863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0741996Z 2024-12-18T00:16:45.0742175Z warnings.warn(msg) 2024-12-18T00:16:45.0742295Z 2024-12-18T00:16:45.0742574Z --- Parse Warning: 34 / 105 --- 2024-12-18T00:16:45.0744011Z /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. 2024-12-18T00:16:45.0744326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0744491Z 2024-12-18T00:16:45.0744805Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-12-18T00:16:45.0744977Z 2024-12-18T00:16:45.0745078Z Note: 2024-12-18T00:16:45.0745478Z Only features in the report that have tensor value data can be viewed as a histogram 2024-12-18T00:16:45.0745780Z If you want to plot a histogram from all the channel values of a specific feature for 2024-12-18T00:16:45.0746062Z a specific model, make sure to specify both the model and the feature properly 2024-12-18T00:16:45.0746462Z in the filters and you should be able to see a distribution of the channel data 2024-12-18T00:16:45.0746584Z 2024-12-18T00:16:45.0746763Z Args: 2024-12-18T00:16:45.0747079Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T00:16:45.0747272Z contain this filter substring 2024-12-18T00:16:45.0760474Z Default = "", results in all the features being printed 2024-12-18T00:16:45.0760853Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T00:16:45.0761118Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T00:16:45.0761362Z num_bins (int, optional): The number of bins to create the histogram with 2024-12-18T00:16:45.0761573Z Default = 10, the values will be split into 10 equal sized bins 2024-12-18T00:16:45.0761659Z 2024-12-18T00:16:45.0761754Z Example Use: 2024-12-18T00:16:45.0761866Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0762170Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-12-18T00:16:45.0762312Z ... feature_filter = "per_channel_min", 2024-12-18T00:16:45.0762425Z ... module_fqn_filter = "block1" 2024-12-18T00:16:45.0762516Z ... ) 2024-12-18T00:16:45.0762809Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-12-18T00:16:45.0763067Z information is gathered across all channels for all modules in block 1 for the 2024-12-18T00:16:45.0763301Z per_channel_min and is displayed in a histogram of equally sized bins 2024-12-18T00:16:45.0763522Z 2024-12-18T00:16:45.0763792Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0763880Z 2024-12-18T00:16:45.0763981Z warnings.warn(msg) 2024-12-18T00:16:45.0764078Z 2024-12-18T00:16:45.0764330Z --- Parse Warning: 35 / 105 --- 2024-12-18T00:16:45.0765383Z /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=660. 2024-12-18T00:16:45.0765632Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T00:16:45.0765731Z 2024-12-18T00:16:45.0766003Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-12-18T00:16:45.0766268Z The submesh created consists of the dimensions and the communicators indicated by 2024-12-18T00:16:45.0766382Z ``mesh_dim_names`` 2024-12-18T00:16:45.0766471Z 2024-12-18T00:16:45.0766569Z Args: 2024-12-18T00:16:45.0766806Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-12-18T00:16:45.0767004Z mesh dimension of the DeviceMesh to create the submesh for. 2024-12-18T00:16:45.0767095Z Returns: 2024-12-18T00:16:45.0767208Z A :class:`DeviceMesh` object 2024-12-18T00:16:45.0767308Z 2024-12-18T00:16:45.0767595Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-12-18T00:16:45.0767707Z In the first example: 2024-12-18T00:16:45.0767961Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-12-18T00:16:45.0768212Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-12-18T00:16:45.0768449Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-12-18T00:16:45.0768675Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-12-18T00:16:45.0768912Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-12-18T00:16:45.0769133Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-12-18T00:16:45.0769234Z 2024-12-18T00:16:45.0769339Z In the second example: 2024-12-18T00:16:45.0769609Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-12-18T00:16:45.0769885Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-12-18T00:16:45.0770147Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-12-18T00:16:45.0770420Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-12-18T00:16:45.0770505Z 2024-12-18T00:16:45.0770618Z Example:: 2024-12-18T00:16:45.0770734Z >>> # xdoctest: +SKIP("no rank") 2024-12-18T00:16:45.0770921Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-12-18T00:16:45.0771009Z >>> 2024-12-18T00:16:45.0771214Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-12-18T00:16:45.0771375Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-12-18T00:16:45.0771629Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T00:16:45.0771745Z >>> tp_mesh = mesh_2d["tp"] 2024-12-18T00:16:45.0771849Z >>> dp_mesh = mesh_2d["dp"] 2024-12-18T00:16:45.0771936Z >>> 2024-12-18T00:16:45.0772054Z >>> # Initialize a 3D mesh. 2024-12-18T00:16:45.0772333Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-12-18T00:16:45.0772648Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-12-18T00:16:45.0772824Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-12-18T00:16:45.0772948Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-12-18T00:16:45.0773034Z 2024-12-18T00:16:45.0773997Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-12-18T00:16:45.0774134Z 2024-12-18T00:16:45.0774383Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T00:16:45.0774519Z ^ 2024-12-18T00:16:45.0774624Z warnings.warn(msg) 2024-12-18T00:16:45.0774721Z 2024-12-18T00:16:45.0774937Z --- Parse Warning: 36 / 105 --- 2024-12-18T00:16:45.0775896Z /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=3063. 2024-12-18T00:16:45.0776179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0776264Z 2024-12-18T00:16:45.0776504Z Gathers picklable objects from the whole group in a single process. 2024-12-18T00:16:45.0776591Z 2024-12-18T00:16:45.0776839Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-12-18T00:16:45.0776992Z object must be picklable in order to be gathered. 2024-12-18T00:16:45.0777080Z 2024-12-18T00:16:45.0777182Z Args: 2024-12-18T00:16:45.0777315Z obj (Any): Input object. Must be picklable. 2024-12-18T00:16:45.0777537Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-12-18T00:16:45.0777725Z should be correctly sized as the size of the group for this 2024-12-18T00:16:45.0777956Z collective and will contain the output. Must be ``None`` on non-dst 2024-12-18T00:16:45.0778070Z ranks. (default is ``None``) 2024-12-18T00:16:45.0778391Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2024-12-18T00:16:45.0778600Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2024-12-18T00:16:45.0778836Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T00:16:45.0779040Z the default process group will be used. Default is ``None``. 2024-12-18T00:16:45.0779389Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2024-12-18T00:16:45.0779484Z 2024-12-18T00:16:45.0779575Z Returns: 2024-12-18T00:16:45.0779760Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-12-18T00:16:45.0779883Z output of the collective. 2024-12-18T00:16:45.0779970Z 2024-12-18T00:16:45.0780205Z .. note:: Note that this API differs slightly from the gather collective 2024-12-18T00:16:45.0780429Z since it does not provide an async_op handle and thus will be a blocking 2024-12-18T00:16:45.0780533Z call. 2024-12-18T00:16:45.0780622Z 2024-12-18T00:16:45.0780856Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-12-18T00:16:45.0781081Z of objects must be moved to the GPU device before communication takes 2024-12-18T00:16:45.0781229Z place. In this case, the device used is given by 2024-12-18T00:16:45.0781458Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-12-18T00:16:45.0781672Z ensure that this is set so that each rank has an individual GPU, via 2024-12-18T00:16:45.0781794Z ``torch.cuda.set_device()``. 2024-12-18T00:16:45.0781883Z 2024-12-18T00:16:45.0781978Z .. warning:: 2024-12-18T00:16:45.0782189Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-12-18T00:16:45.0782485Z known to be insecure. It is possible to construct malicious pickle data 2024-12-18T00:16:45.0782719Z which will execute arbitrary code during unpickling. Only call this 2024-12-18T00:16:45.0782831Z function with data you trust. 2024-12-18T00:16:45.0782920Z 2024-12-18T00:16:45.0783029Z .. warning:: 2024-12-18T00:16:45.0783303Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-12-18T00:16:45.0783545Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-12-18T00:16:45.0783721Z pickled. Please consider using :func:`gather` instead. 2024-12-18T00:16:45.0783821Z 2024-12-18T00:16:45.0783917Z Example:: 2024-12-18T00:16:45.0784060Z >>> # xdoctest: +SKIP("need process group init") 2024-12-18T00:16:45.0784256Z >>> # Note: Process group initialization omitted on each rank. 2024-12-18T00:16:45.0784382Z >>> import torch.distributed as dist 2024-12-18T00:16:45.0784507Z >>> # Assumes world_size of 3. 2024-12-18T00:16:45.0784690Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-12-18T00:16:45.0784820Z >>> output = [None for _ in gather_objects] 2024-12-18T00:16:45.0784939Z >>> dist.gather_object( 2024-12-18T00:16:45.0785064Z ... gather_objects[dist.get_rank()], 2024-12-18T00:16:45.0785213Z ... output if dist.get_rank() == 0 else None, 2024-12-18T00:16:45.0785306Z ... dst=0 2024-12-18T00:16:45.0785400Z ... ) 2024-12-18T00:16:45.0785509Z >>> # On rank 0 2024-12-18T00:16:45.0785596Z >>> output 2024-12-18T00:16:45.0785691Z ['foo', 12, {1: 2}] 2024-12-18T00:16:45.0785788Z 2024-12-18T00:16:45.0786043Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0786140Z 2024-12-18T00:16:45.0786240Z warnings.warn(msg) 2024-12-18T00:16:45.0786324Z 2024-12-18T00:16:45.0786533Z --- Parse Warning: 37 / 105 --- 2024-12-18T00:16:45.0787379Z /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. 2024-12-18T00:16:45.0787653Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0787738Z 2024-12-18T00:16:45.0787872Z Module ``torch.distributed.launch``. 2024-12-18T00:16:45.0787958Z 2024-12-18T00:16:45.0788210Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-12-18T00:16:45.0788379Z training processes on each of the training nodes. 2024-12-18T00:16:45.0788465Z 2024-12-18T00:16:45.0788571Z .. warning:: 2024-12-18T00:16:45.0788654Z 2024-12-18T00:16:45.0788913Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-12-18T00:16:45.0789013Z 2024-12-18T00:16:45.0789256Z The utility can be used for single-node distributed training, in which one or 2024-12-18T00:16:45.0789501Z more processes per node will be spawned. The utility can be used for either 2024-12-18T00:16:45.0789720Z CPU training or GPU training. If the utility is used for GPU training, 2024-12-18T00:16:45.0789981Z each distributed process will be operating on a single GPU. This can achieve 2024-12-18T00:16:45.0790218Z well-improved single-node training performance. It can also be used in 2024-12-18T00:16:45.0790485Z multi-node distributed training, by spawning up multiple processes on each node 2024-12-18T00:16:45.0790726Z for well-improved multi-node distributed training performance as well. 2024-12-18T00:16:45.0790956Z This will especially be beneficial for systems with multiple Infiniband 2024-12-18T00:16:45.0791221Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-12-18T00:16:45.0791340Z aggregated communication bandwidth. 2024-12-18T00:16:45.0791465Z 2024-12-18T00:16:45.0791729Z In both cases of single-node distributed training or multi-node distributed 2024-12-18T00:16:45.0791963Z training, this utility will launch the given number of processes per node 2024-12-18T00:16:45.0792201Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-12-18T00:16:45.0792448Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-12-18T00:16:45.0792687Z and each process will be operating on a single GPU from *GPU 0 to 2024-12-18T00:16:45.0792798Z GPU (nproc_per_node - 1)*. 2024-12-18T00:16:45.0792898Z 2024-12-18T00:16:45.0793003Z **How to use this module:** 2024-12-18T00:16:45.0793088Z 2024-12-18T00:16:45.0793257Z 1. Single-Node multi-process distributed training 2024-12-18T00:16:45.0793343Z 2024-12-18T00:16:45.0793443Z :: 2024-12-18T00:16:45.0793527Z 2024-12-18T00:16:45.0793766Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T00:16:45.0793972Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-12-18T00:16:45.0794101Z arguments of your training script) 2024-12-18T00:16:45.0794196Z 2024-12-18T00:16:45.0794407Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-12-18T00:16:45.0794502Z 2024-12-18T00:16:45.0794586Z 2024-12-18T00:16:45.0794736Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-12-18T00:16:45.0794835Z 2024-12-18T00:16:45.0794922Z :: 2024-12-18T00:16:45.0795019Z 2024-12-18T00:16:45.0795251Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T00:16:45.0795415Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-12-18T00:16:45.0795638Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T00:16:45.0795797Z and all other arguments of your training script) 2024-12-18T00:16:45.0795894Z 2024-12-18T00:16:45.0795984Z Node 2: 2024-12-18T00:16:45.0796066Z 2024-12-18T00:16:45.0796165Z :: 2024-12-18T00:16:45.0796250Z 2024-12-18T00:16:45.0796489Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T00:16:45.0796647Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-12-18T00:16:45.0796867Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T00:16:45.0797020Z and all other arguments of your training script) 2024-12-18T00:16:45.0797107Z 2024-12-18T00:16:45.0797284Z 3. To look up what optional arguments this module offers: 2024-12-18T00:16:45.0797368Z 2024-12-18T00:16:45.0797463Z :: 2024-12-18T00:16:45.0797547Z 2024-12-18T00:16:45.0797687Z python -m torch.distributed.launch --help 2024-12-18T00:16:45.0797782Z 2024-12-18T00:16:45.0797866Z 2024-12-18T00:16:45.0797977Z **Important Notices:** 2024-12-18T00:16:45.0798063Z 2024-12-18T00:16:45.0798254Z 1. This utility and multi-process distributed (single-node or 2024-12-18T00:16:45.0798609Z multi-node) GPU training currently only achieves the best performance using 2024-12-18T00:16:45.0798864Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-12-18T00:16:45.0798975Z use for GPU training. 2024-12-18T00:16:45.0799064Z 2024-12-18T00:16:45.0799295Z 2. In your training program, you must parse the command-line argument: 2024-12-18T00:16:45.0799529Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-12-18T00:16:45.0799759Z If your training program uses GPUs, you should ensure that your code only 2024-12-18T00:16:45.0799967Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-12-18T00:16:45.0800053Z 2024-12-18T00:16:45.0800176Z Parsing the local_rank argument 2024-12-18T00:16:45.0800263Z 2024-12-18T00:16:45.0800403Z :: 2024-12-18T00:16:45.0800530Z 2024-12-18T00:16:45.0800632Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0800742Z >>> import argparse 2024-12-18T00:16:45.0800873Z >>> parser = argparse.ArgumentParser() 2024-12-18T00:16:45.0801083Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-12-18T00:16:45.0801195Z >>> args = parser.parse_args() 2024-12-18T00:16:45.0801309Z 2024-12-18T00:16:45.0801473Z Set your device to local rank using either 2024-12-18T00:16:45.0801560Z 2024-12-18T00:16:45.0801657Z :: 2024-12-18T00:16:45.0801741Z 2024-12-18T00:16:45.0801944Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-12-18T00:16:45.0802037Z 2024-12-18T00:16:45.0802123Z or 2024-12-18T00:16:45.0802213Z 2024-12-18T00:16:45.0802301Z :: 2024-12-18T00:16:45.0802385Z 2024-12-18T00:16:45.0802538Z >>> with torch.cuda.device(args.local_rank): 2024-12-18T00:16:45.0802641Z >>> # your code to run 2024-12-18T00:16:45.0802744Z >>> ... 2024-12-18T00:16:45.0802830Z 2024-12-18T00:16:45.0802938Z .. versionchanged:: 2.0.0 2024-12-18T00:16:45.0803033Z 2024-12-18T00:16:45.0803285Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-12-18T00:16:45.0803537Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-12-18T00:16:45.0803684Z previously used underscored ``--local_rank``. 2024-12-18T00:16:45.0803781Z 2024-12-18T00:16:45.0804019Z For backward compatibility, it may be necessary for users to handle both 2024-12-18T00:16:45.0804288Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-12-18T00:16:45.0804517Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-12-18T00:16:45.0804767Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-12-18T00:16:45.0805010Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-12-18T00:16:45.0805167Z including ``"--local-rank"`` should be sufficient. 2024-12-18T00:16:45.0805260Z 2024-12-18T00:16:45.0805498Z 3. In your training program, you are supposed to call the following function 2024-12-18T00:16:45.0805739Z at the beginning to start the distributed backend. It is strongly recommended 2024-12-18T00:16:45.0805974Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-12-18T00:16:45.0806168Z but ``env://`` is the one that is officially supported by this module. 2024-12-18T00:16:45.0806264Z 2024-12-18T00:16:45.0806358Z :: 2024-12-18T00:16:45.0806440Z 2024-12-18T00:16:45.0806652Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-12-18T00:16:45.0806790Z >>> init_method='env://') 2024-12-18T00:16:45.0806874Z 2024-12-18T00:16:45.0807116Z 4. In your training program, you can either use regular distributed functions 2024-12-18T00:16:45.0807357Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-12-18T00:16:45.0807573Z training program uses GPUs for training and you would like to use 2024-12-18T00:16:45.0807762Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-12-18T00:16:45.0807876Z here is how to configure it. 2024-12-18T00:16:45.0807959Z 2024-12-18T00:16:45.0808048Z :: 2024-12-18T00:16:45.0808145Z 2024-12-18T00:16:45.0808340Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-12-18T00:16:45.0808489Z >>> device_ids=[args.local_rank], 2024-12-18T00:16:45.0808630Z >>> output_device=args.local_rank) 2024-12-18T00:16:45.0808715Z 2024-12-18T00:16:45.0808971Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-12-18T00:16:45.0809236Z that your code will be operating on. This is generally the local rank of the 2024-12-18T00:16:45.0809513Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-12-18T00:16:45.0809732Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-12-18T00:16:45.0809831Z utility 2024-12-18T00:16:45.0809914Z 2024-12-18T00:16:45.0810212Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-12-18T00:16:45.0810436Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-12-18T00:16:45.0810657Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-12-18T00:16:45.0810859Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-12-18T00:16:45.0811028Z will not pass ``--local-rank`` when you specify this flag. 2024-12-18T00:16:45.0811122Z 2024-12-18T00:16:45.0811218Z .. warning:: 2024-12-18T00:16:45.0811305Z 2024-12-18T00:16:45.0811519Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-12-18T00:16:45.0811704Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-12-18T00:16:45.0811833Z write to a networked filesystem. See 2024-12-18T00:16:45.0812055Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-12-18T00:16:45.0812228Z how things can go wrong if you don't do this correctly. 2024-12-18T00:16:45.0812308Z 2024-12-18T00:16:45.0812392Z 2024-12-18T00:16:45.0812483Z 2024-12-18T00:16:45.0812563Z 2024-12-18T00:16:45.0812828Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0812912Z 2024-12-18T00:16:45.0813008Z warnings.warn(msg) 2024-12-18T00:16:45.0813101Z 2024-12-18T00:16:45.0813316Z --- Parse Warning: 38 / 105 --- 2024-12-18T00:16:45.0814368Z /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. 2024-12-18T00:16:45.0814631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0814727Z 2024-12-18T00:16:45.0814967Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-12-18T00:16:45.0815117Z Needs to be called on all ranks in an SPMD fashion. 2024-12-18T00:16:45.0815209Z 2024-12-18T00:16:45.0815299Z Args: 2024-12-18T00:16:45.0815583Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-12-18T00:16:45.0815756Z of shards that represent the local shards on this rank. 2024-12-18T00:16:45.0815991Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-12-18T00:16:45.0816111Z shape of the overall sharded tensor. 2024-12-18T00:16:45.0816199Z 2024-12-18T00:16:45.0816300Z Keyword args: 2024-12-18T00:16:45.0816563Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T00:16:45.0816698Z the default process group will be used. 2024-12-18T00:16:45.0816872Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T00:16:45.0817081Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T00:16:45.0817285Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T00:16:45.0817382Z Default: ``False``. 2024-12-18T00:16:45.0817471Z 2024-12-18T00:16:45.0817558Z Returns: 2024-12-18T00:16:45.0817719Z A :class:`ShardedTensor` object handle on this rank 2024-12-18T00:16:45.0817801Z 2024-12-18T00:16:45.0817885Z 2024-12-18T00:16:45.0817980Z Examples: 2024-12-18T00:16:45.0818231Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-12-18T00:16:45.0818480Z each shard have a (5, 5) local tensor, we can do it like below: 2024-12-18T00:16:45.0818563Z 2024-12-18T00:16:45.0818653Z on rank 0: 2024-12-18T00:16:45.0818783Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T00:16:45.0818906Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T00:16:45.0819043Z >>> shard_offsets=[0, 0], 2024-12-18T00:16:45.0819171Z >>> shard_lengths=[5, 5], 2024-12-18T00:16:45.0819282Z >>> placement="rank:0/cuda:0" 2024-12-18T00:16:45.0819378Z >>> ) 2024-12-18T00:16:45.0819574Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T00:16:45.0819772Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T00:16:45.0819855Z 2024-12-18T00:16:45.0819953Z on rank 1: 2024-12-18T00:16:45.0820070Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T00:16:45.0820195Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T00:16:45.0820311Z >>> shard_offsets=[5, 0], 2024-12-18T00:16:45.0820413Z >>> shard_lengths=[5, 5], 2024-12-18T00:16:45.0820535Z >>> placement="rank:1/cuda:1" 2024-12-18T00:16:45.0820620Z >>> ) 2024-12-18T00:16:45.0820816Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T00:16:45.0821023Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T00:16:45.0821106Z 2024-12-18T00:16:45.0821366Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0821448Z 2024-12-18T00:16:45.0821558Z warnings.warn(msg) 2024-12-18T00:16:45.0821643Z 2024-12-18T00:16:45.0821836Z --- Parse Warning: 39 / 105 --- 2024-12-18T00:16:45.0822926Z /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=784. 2024-12-18T00:16:45.0823191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0823283Z 2024-12-18T00:16:45.0823539Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-12-18T00:16:45.0823667Z size and sharding spec on each rank. 2024-12-18T00:16:45.0823750Z 2024-12-18T00:16:45.0823835Z Args: 2024-12-18T00:16:45.0824066Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-12-18T00:16:45.0824323Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-12-18T00:16:45.0824504Z The specification describing how to shard the Tensor. 2024-12-18T00:16:45.0824672Z global_size (Sequence[int]): Size of the sharded tensor. 2024-12-18T00:16:45.0824934Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-12-18T00:16:45.0825030Z Default: None 2024-12-18T00:16:45.0825202Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T00:16:45.0825416Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T00:16:45.0825611Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T00:16:45.0825723Z Default: ``False``. 2024-12-18T00:16:45.0825808Z 2024-12-18T00:16:45.0825894Z Returns: 2024-12-18T00:16:45.0826145Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-12-18T00:16:45.0826263Z tensor stored in the current rank. 2024-12-18T00:16:45.0826356Z 2024-12-18T00:16:45.0826446Z Examples: 2024-12-18T00:16:45.0826557Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0826691Z >>> # All tensors below are of torch.int64 type. 2024-12-18T00:16:45.0826811Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T00:16:45.0827027Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T00:16:45.0827250Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-12-18T00:16:45.0827354Z >>> local_tensor 2024-12-18T00:16:45.0827453Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-12-18T00:16:45.0827562Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-12-18T00:16:45.0827685Z >>> sharding_dim = 0 2024-12-18T00:16:45.0827837Z >>> sharding_spec = ChunkShardingSpec( 2024-12-18T00:16:45.0827947Z dim=sharding_dim, 2024-12-18T00:16:45.0828041Z placements=[ 2024-12-18T00:16:45.0828151Z "rank:0/cuda:0", 2024-12-18T00:16:45.0828245Z "rank:1/cuda:1", 2024-12-18T00:16:45.0828332Z ], 2024-12-18T00:16:45.0828430Z ) 2024-12-18T00:16:45.0828685Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-12-18T00:16:45.0828786Z >>> st 2024-12-18T00:16:45.0828881Z ShardedTensor( 2024-12-18T00:16:45.0828990Z ShardedTensorMetadata( 2024-12-18T00:16:45.0829097Z shards_metadata=[ 2024-12-18T00:16:45.0829362Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-12-18T00:16:45.0829634Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-12-18T00:16:45.0829720Z ], 2024-12-18T00:16:45.0829839Z size=torch.Size([2, 4]) 2024-12-18T00:16:45.0829922Z ) 2024-12-18T00:16:45.0830018Z >>> st.local_tensor() 2024-12-18T00:16:45.0830126Z tensor([1, 2, 3, 4]) # Rank 0 2024-12-18T00:16:45.0830225Z tensor([3, 4, 5, 6]) # Rank 1 2024-12-18T00:16:45.0830323Z 2024-12-18T00:16:45.0830591Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-12-18T00:16:45.0830835Z rank validations, and we only validate the local shard on the current rank. 2024-12-18T00:16:45.0831064Z We fully rely on the user to ensure local tensor is sharded based on the 2024-12-18T00:16:45.0831160Z sharding spec. 2024-12-18T00:16:45.0831254Z 2024-12-18T00:16:45.0831506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0831598Z 2024-12-18T00:16:45.0831697Z warnings.warn(msg) 2024-12-18T00:16:45.0831781Z 2024-12-18T00:16:45.0831983Z --- Parse Warning: 40 / 105 --- 2024-12-18T00:16:45.0833020Z /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=1023. 2024-12-18T00:16:45.0833292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0833375Z 2024-12-18T00:16:45.0833639Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-12-18T00:16:45.0833736Z single local shard. 2024-12-18T00:16:45.0833817Z 2024-12-18T00:16:45.0834050Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-12-18T00:16:45.0834290Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-12-18T00:16:45.0834409Z we swap local shards directly. 2024-12-18T00:16:45.0834667Z For more generic cases, we merge different shards across different ranks and split 2024-12-18T00:16:45.0834922Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-12-18T00:16:45.0835004Z 2024-12-18T00:16:45.0835088Z Args: 2024-12-18T00:16:45.0835389Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-12-18T00:16:45.0835554Z specification describing how the tensor is sharded. 2024-12-18T00:16:45.0835647Z 2024-12-18T00:16:45.0835777Z Returns: 2024-12-18T00:16:45.0836007Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-12-18T00:16:45.0836103Z 2024-12-18T00:16:45.0836192Z Examples: 2024-12-18T00:16:45.0836305Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0836427Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T00:16:45.0836643Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T00:16:45.0836794Z >>> tensor = torch.stack([tensor, tensor]) 2024-12-18T00:16:45.0836881Z >>> tensor 2024-12-18T00:16:45.0837017Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-12-18T00:16:45.0837138Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-12-18T00:16:45.0837262Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-12-18T00:16:45.0837386Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-12-18T00:16:45.0837485Z >>> sharding_dim = 0 2024-12-18T00:16:45.0837611Z >>> spec = ChunkShardingSpec( 2024-12-18T00:16:45.0837714Z dim=sharding_dim, 2024-12-18T00:16:45.0837827Z placements=[ 2024-12-18T00:16:45.0837925Z "rank:0/cuda:0", 2024-12-18T00:16:45.0838030Z "rank:1/cuda:1", 2024-12-18T00:16:45.0838127Z "rank:2/cuda:2", 2024-12-18T00:16:45.0838222Z "rank:3/cuda:3", 2024-12-18T00:16:45.0838325Z ], 2024-12-18T00:16:45.0838510Z ) 2024-12-18T00:16:45.0838634Z >>> current_offsets = [0] * 2 2024-12-18T00:16:45.0838745Z >>> current_offsets[0] = rank * 2 2024-12-18T00:16:45.0838862Z >>> shard_metadata = ShardMetadata( 2024-12-18T00:16:45.0839032Z shard_offsets=copy.deepcopy(current_offsets), 2024-12-18T00:16:45.0839144Z shard_sizes=tensor.size(), 2024-12-18T00:16:45.0839287Z placement=spec.placements[rank], 2024-12-18T00:16:45.0839372Z ) 2024-12-18T00:16:45.0839477Z >>> local_shards = [ 2024-12-18T00:16:45.0839571Z Shard( 2024-12-18T00:16:45.0839668Z tensor=tensor, 2024-12-18T00:16:45.0839792Z metadata=shard_metadata, 2024-12-18T00:16:45.0839877Z ) 2024-12-18T00:16:45.0839975Z ] 2024-12-18T00:16:45.0840204Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-12-18T00:16:45.0840305Z >>> sharding_dim = 1 2024-12-18T00:16:45.0840442Z >>> resharding_spec = ChunkShardingSpec( 2024-12-18T00:16:45.0840539Z dim=sharding_dim, 2024-12-18T00:16:45.0840648Z placements=[ 2024-12-18T00:16:45.0840741Z "rank:0/cuda:0", 2024-12-18T00:16:45.0840836Z "rank:1/cuda:1", 2024-12-18T00:16:45.0840942Z "rank:2/cuda:2", 2024-12-18T00:16:45.0841034Z "rank:3/cuda:3", 2024-12-18T00:16:45.0841131Z ], 2024-12-18T00:16:45.0841214Z ) 2024-12-18T00:16:45.0841335Z >>> st.reshard(resharding_spec) 2024-12-18T00:16:45.0841453Z >>> tensor = st.local_shards()[0].tensor 2024-12-18T00:16:45.0841538Z >>> tensor 2024-12-18T00:16:45.0841692Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-12-18T00:16:45.0841829Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-12-18T00:16:45.0841981Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-12-18T00:16:45.0842126Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-12-18T00:16:45.0842210Z 2024-12-18T00:16:45.0842477Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0842560Z 2024-12-18T00:16:45.0842670Z warnings.warn(msg) 2024-12-18T00:16:45.0842753Z 2024-12-18T00:16:45.0842966Z --- Parse Warning: 41 / 105 --- 2024-12-18T00:16:45.0843944Z /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. 2024-12-18T00:16:45.0844271Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0844370Z 2024-12-18T00:16:45.0844587Z Representation of a sharding plan, describes how to shard a module 2024-12-18T00:16:45.0844925Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-12-18T00:16:45.0845216Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-12-18T00:16:45.0845474Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-12-18T00:16:45.0845559Z 2024-12-18T00:16:45.0845646Z Args: 2024-12-18T00:16:45.0845924Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-12-18T00:16:45.0846093Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-12-18T00:16:45.0846383Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-12-18T00:16:45.0846637Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-12-18T00:16:45.0846775Z a parameter to a `ShardingSpec`. 2024-12-18T00:16:45.0847036Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-12-18T00:16:45.0847141Z to a `Sharder` object. 2024-12-18T00:16:45.0847487Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-12-18T00:16:45.0847745Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-12-18T00:16:45.0847991Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-12-18T00:16:45.0848088Z Default: `None` 2024-12-18T00:16:45.0848349Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-12-18T00:16:45.0848594Z a module's sharded output to be returned as a Tensor from its local shards to 2024-12-18T00:16:45.0848848Z ensure further processing in a data parallel fashion. ("" in list means the 2024-12-18T00:16:45.0848943Z root module). 2024-12-18T00:16:45.0849035Z Default: None 2024-12-18T00:16:45.0849133Z Example: 2024-12-18T00:16:45.0849416Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-12-18T00:16:45.0849709Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-12-18T00:16:45.0849793Z 2024-12-18T00:16:45.0849962Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-12-18T00:16:45.0850081Z >>> class MyModule(nn.Module): 2024-12-18T00:16:45.0850188Z >>> def __init__(self) -> None: 2024-12-18T00:16:45.0850304Z >>> super().__init__() 2024-12-18T00:16:45.0850416Z >>> self.fc1 = nn.Linear() 2024-12-18T00:16:45.0850528Z >>> self.gelu = nn.GELU() 2024-12-18T00:16:45.0850634Z >>> self.fc2 = nn.Linear() 2024-12-18T00:16:45.0850738Z >>> self.relu = nn.Linear() 2024-12-18T00:16:45.0850836Z >>> 2024-12-18T00:16:45.0850945Z >>> def forward(self, input): 2024-12-18T00:16:45.0851132Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-12-18T00:16:45.0851216Z 2024-12-18T00:16:45.0851300Z 2024-12-18T00:16:45.0851444Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-12-18T00:16:45.0851554Z >>> sharding_plan = ShardingPlan( 2024-12-18T00:16:45.0851656Z >>> plan={ 2024-12-18T00:16:45.0851758Z >>> "fc1.weight": spec1, 2024-12-18T00:16:45.0851870Z >>> "fc2.weight": spec2 2024-12-18T00:16:45.0851957Z >>> }, 2024-12-18T00:16:45.0852083Z >>> output_plan={ 2024-12-18T00:16:45.0852223Z >>> "fc2": output_spec 2024-12-18T00:16:45.0852307Z >>> }, 2024-12-18T00:16:45.0852425Z >>> return_local_tensor=["fc2"] 2024-12-18T00:16:45.0852511Z >>> ) 2024-12-18T00:16:45.0852591Z 2024-12-18T00:16:45.0852860Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0852968Z 2024-12-18T00:16:45.0853108Z warnings.warn(msg) 2024-12-18T00:16:45.0853191Z 2024-12-18T00:16:45.0853375Z --- Parse Warning: 42 / 105 --- 2024-12-18T00:16:45.0854489Z /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. 2024-12-18T00:16:45.0854748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0854843Z 2024-12-18T00:16:45.0854953Z Run post-localSGD algorithm. 2024-12-18T00:16:45.0855046Z 2024-12-18T00:16:45.0855279Z This DDP communication hook is used for running post-localSGD algorithm, 2024-12-18T00:16:45.0855434Z by combining with a model averaging component (e.g., 2024-12-18T00:16:45.0855772Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-12-18T00:16:45.0855888Z that runs after the optimizer step. 2024-12-18T00:16:45.0855983Z 2024-12-18T00:16:45.0856068Z Args: 2024-12-18T00:16:45.0856302Z state (PostLocalSGDState): State information to run post-localSGD. 2024-12-18T00:16:45.0856578Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-12-18T00:16:45.0857001Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T00:16:45.0857260Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T00:16:45.0857414Z only exactly one tensor is stored in this bucket. 2024-12-18T00:16:45.0857509Z 2024-12-18T00:16:45.0857598Z Returns: 2024-12-18T00:16:45.0857847Z Future handler of the communication, which updates the gradients in place. 2024-12-18T00:16:45.0857933Z 2024-12-18T00:16:45.0858025Z Example:: 2024-12-18T00:16:45.0858141Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0858394Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-12-18T00:16:45.0858524Z start_localSGD_iter=10) 2024-12-18T00:16:45.0858698Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:45.0859039Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-12-18T00:16:45.0859386Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-12-18T00:16:45.0859472Z 2024-12-18T00:16:45.0859732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0859818Z 2024-12-18T00:16:45.0859927Z warnings.warn(msg) 2024-12-18T00:16:45.0860011Z 2024-12-18T00:16:45.0860191Z --- Parse Warning: 43 / 105 --- 2024-12-18T00:16:45.0861275Z /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=343. 2024-12-18T00:16:45.0861534Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0861627Z 2024-12-18T00:16:45.0861742Z Implement PowerSGD algorithm. 2024-12-18T00:16:45.0861833Z 2024-12-18T00:16:45.0862059Z This DDP communication hook implements PowerSGD gradient compression 2024-12-18T00:16:45.0862319Z algorithm described in the `paper `_. 2024-12-18T00:16:45.0862599Z Once gradient tensors are aggregated across all workers, this hook applies 2024-12-18T00:16:45.0862706Z compression as follows: 2024-12-18T00:16:45.0862800Z 2024-12-18T00:16:45.0863266Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-12-18T00:16:45.0863393Z 2024-12-18T00:16:45.0863816Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-12-18T00:16:45.0863901Z 2024-12-18T00:16:45.0864317Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-12-18T00:16:45.0864401Z 2024-12-18T00:16:45.0864519Z 2. Handles uncompressed tensors: 2024-12-18T00:16:45.0864602Z 2024-12-18T00:16:45.0865116Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-12-18T00:16:45.0865205Z 2024-12-18T00:16:45.0865544Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-12-18T00:16:45.0865639Z 2024-12-18T00:16:45.0865876Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-12-18T00:16:45.0865971Z 2024-12-18T00:16:45.0866209Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-12-18T00:16:45.0866524Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-12-18T00:16:45.0866607Z 2024-12-18T00:16:45.0866753Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-12-18T00:16:45.0866845Z 2024-12-18T00:16:45.0866960Z 3.3. Allreduces Ps as a batch; 2024-12-18T00:16:45.0867057Z 2024-12-18T00:16:45.0867176Z 3.4. Orthogonalizes each P in Ps; 2024-12-18T00:16:45.0867265Z 2024-12-18T00:16:45.0867477Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-12-18T00:16:45.0867564Z 2024-12-18T00:16:45.0867687Z 3.6. Allreduces Qs as a batch; 2024-12-18T00:16:45.0867776Z 2024-12-18T00:16:45.0868075Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-12-18T00:16:45.0868172Z 2024-12-18T00:16:45.0868580Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-12-18T00:16:45.0868876Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-12-18T00:16:45.0869308Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-12-18T00:16:45.0869412Z 2024-12-18T00:16:45.0869501Z Args: 2024-12-18T00:16:45.0869934Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-12-18T00:16:45.0870306Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-12-18T00:16:45.0870421Z and ``min_compression_rate``. 2024-12-18T00:16:45.0870853Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T00:16:45.0871105Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T00:16:45.0871278Z only exactly one tensor is stored in this bucket. 2024-12-18T00:16:45.0871366Z 2024-12-18T00:16:45.0871460Z Returns: 2024-12-18T00:16:45.0871721Z Future handler of the communication, which updates the gradients in place. 2024-12-18T00:16:45.0871813Z 2024-12-18T00:16:45.0871921Z Example:: 2024-12-18T00:16:45.0872024Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0872406Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-12-18T00:16:45.0872596Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-12-18T00:16:45.0872760Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-12-18T00:16:45.0872864Z 2024-12-18T00:16:45.0873146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0873274Z 2024-12-18T00:16:45.0873385Z warnings.warn(msg) 2024-12-18T00:16:45.0873472Z 2024-12-18T00:16:45.0873875Z --- Parse Warning: 44 / 105 --- 2024-12-18T00:16:45.0874987Z /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=37. 2024-12-18T00:16:45.0875267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0875358Z 2024-12-18T00:16:45.0875563Z Averages parameters periodically after the warm-up stage. 2024-12-18T00:16:45.0875650Z 2024-12-18T00:16:45.0875922Z This can be used for running `post-local SGD `_, 2024-12-18T00:16:45.0876119Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-12-18T00:16:45.0876360Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-12-18T00:16:45.0876456Z 2024-12-18T00:16:45.0876547Z Args: 2024-12-18T00:16:45.0876733Z period (int): The number of steps per model averaging. 2024-12-18T00:16:45.0877006Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-12-18T00:16:45.0877141Z Otherwise, only DDP needs to be used. 2024-12-18T00:16:45.0877366Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-12-18T00:16:45.0877486Z model averaging is skipped. 2024-12-18T00:16:45.0877684Z process_group: The process group to be used for all-reduce. 2024-12-18T00:16:45.0877827Z If ``None``, the default process group, which 2024-12-18T00:16:45.0878033Z is created by :func:`torch.distributed.init_process_group`, 2024-12-18T00:16:45.0878156Z will be used. (default: ``None``) 2024-12-18T00:16:45.0878245Z 2024-12-18T00:16:45.0878412Z Example:: 2024-12-18T00:16:45.0878497Z 2024-12-18T00:16:45.0878641Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.0878734Z >>> import torch 2024-12-18T00:16:45.0878867Z >>> import torch.distributed as dist 2024-12-18T00:16:45.0879182Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-12-18T00:16:45.0879449Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T00:16:45.0879569Z >>> import torch.nn as nn 2024-12-18T00:16:45.0879654Z >>> 2024-12-18T00:16:45.0879846Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T00:16:45.0879955Z >>> torch.cuda.set_device(rank) 2024-12-18T00:16:45.0880086Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-12-18T00:16:45.0880255Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T00:16:45.0880399Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T00:16:45.0880499Z >>> ) 2024-12-18T00:16:45.0880646Z >>> # Register a post-localSGD communication hook. 2024-12-18T00:16:45.0880951Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T00:16:45.0881111Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:45.0881199Z >>> 2024-12-18T00:16:45.0881482Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T00:16:45.0881760Z >>> # After 100 steps, run model averaging every 4 steps. 2024-12-18T00:16:45.0882090Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T00:16:45.0882336Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T00:16:45.0882489Z >>> for step in range(0, 200): 2024-12-18T00:16:45.0882630Z >>> optimizer.zero_grad() 2024-12-18T00:16:45.0882747Z >>> loss = loss_fn(output, labels) 2024-12-18T00:16:45.0882860Z >>> loss.backward() 2024-12-18T00:16:45.0882961Z >>> optimizer.step() 2024-12-18T00:16:45.0883166Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-12-18T00:16:45.0883371Z >>> # inter-node communication only occurs every 4 iterations after 2024-12-18T00:16:45.0883508Z >>> # the initial ``warmup_steps`` period. 2024-12-18T00:16:45.0883672Z >>> averager.average_parameters(model.parameters()) 2024-12-18T00:16:45.0883758Z 2024-12-18T00:16:45.0884027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0884112Z 2024-12-18T00:16:45.0884228Z warnings.warn(msg) 2024-12-18T00:16:45.0884314Z 2024-12-18T00:16:45.0884527Z --- Parse Warning: 45 / 105 --- 2024-12-18T00:16:45.0885752Z /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=18. 2024-12-18T00:16:45.0886014Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0886114Z 2024-12-18T00:16:45.0886451Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-12-18T00:16:45.0886550Z 2024-12-18T00:16:45.0886865Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-12-18T00:16:45.0887082Z by using different periods concurrently after the warm-up stage. 2024-12-18T00:16:45.0887482Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-12-18T00:16:45.0887821Z that supports `post-local SGD `_, which essentially only supports 2024-12-18T00:16:45.0888140Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-12-18T00:16:45.0888489Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-12-18T00:16:45.0888802Z Similarly, the process groups within this class do not have such an intra-machine process 2024-12-18T00:16:45.0889076Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-12-18T00:16:45.0889177Z 2024-12-18T00:16:45.0889267Z Args: 2024-12-18T00:16:45.0889526Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-12-18T00:16:45.0889739Z process group size, used for initializing process groups of 2024-12-18T00:16:45.0889968Z different sizes in a hierarchy to average parameters concurrently. 2024-12-18T00:16:45.0890194Z Particularly, at each iteration, there will be at most a single 2024-12-18T00:16:45.0890429Z process group that runs averaging -- the period of such group should 2024-12-18T00:16:45.0890656Z have the largest period which the current step can be divided by. 2024-12-18T00:16:45.0890826Z For example, if the dict has three keys: 2, 4, and 8, 2024-12-18T00:16:45.0891042Z then this means totally three process groups will be created to 2024-12-18T00:16:45.0891307Z average parameters every 2, 4, and 8 iterations, respectively. 2024-12-18T00:16:45.0891497Z At the 4th iteration, only the second process group will run 2024-12-18T00:16:45.0891690Z averaging, because the first process group should be a 2024-12-18T00:16:45.0891965Z subset of the second process group, and no need to execute the first 2024-12-18T00:16:45.0892105Z process group redundantly. 2024-12-18T00:16:45.0892309Z On the other hand, the third process group can only be triggered 2024-12-18T00:16:45.0892543Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-12-18T00:16:45.0892847Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-12-18T00:16:45.0893306Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-12-18T00:16:45.0893485Z If ``None``, the default process group, which is created 2024-12-18T00:16:45.0893759Z by :func:`torch.distributed.init_process_group`, will be used. 2024-12-18T00:16:45.0893891Z (default: ``None``) 2024-12-18T00:16:45.0893980Z 2024-12-18T00:16:45.0894087Z Example:: 2024-12-18T00:16:45.0894209Z >>> # xdoctest: +SKIP('undefined rank') 2024-12-18T00:16:45.0894341Z >>> from collections import OrderedDict 2024-12-18T00:16:45.0894438Z >>> import torch 2024-12-18T00:16:45.0894561Z >>> import torch.distributed as dist 2024-12-18T00:16:45.0894844Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T00:16:45.0894954Z >>> PostLocalSGDState, 2024-12-18T00:16:45.0895072Z >>> post_localSGD_hook, 2024-12-18T00:16:45.0895159Z >>> ) 2024-12-18T00:16:45.0895531Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-12-18T00:16:45.0895647Z >>> import torch.nn as nn 2024-12-18T00:16:45.0895736Z >>> 2024-12-18T00:16:45.0895924Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T00:16:45.0896035Z >>> torch.cuda.set_device(rank) 2024-12-18T00:16:45.0896184Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T00:16:45.0896341Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T00:16:45.0896483Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T00:16:45.0896580Z >>> ) 2024-12-18T00:16:45.0896729Z >>> # Register a post-localSGD communication hook. 2024-12-18T00:16:45.0897013Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-12-18T00:16:45.0897139Z >>> subgroup, _ = dist.new_subgroups() 2024-12-18T00:16:45.0897463Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-12-18T00:16:45.0897629Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:45.0897714Z >>> 2024-12-18T00:16:45.0898007Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-12-18T00:16:45.0898130Z >>> # the 16 processes every 16 iterations. 2024-12-18T00:16:45.0898323Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-12-18T00:16:45.0898558Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-12-18T00:16:45.0898886Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T00:16:45.0899154Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T00:16:45.0899363Z >>> # After 100 steps, run model averaging at two levels. 2024-12-18T00:16:45.0899479Z >>> for step in range(0, 200): 2024-12-18T00:16:45.0899587Z >>> optimizer.zero_grad() 2024-12-18T00:16:45.0899714Z >>> loss = loss_fn(output, labels) 2024-12-18T00:16:45.0899857Z >>> loss.backward() 2024-12-18T00:16:45.0899995Z >>> optimizer.step() 2024-12-18T00:16:45.0900152Z >>> # Average parameters after ``optimizer.step()``. 2024-12-18T00:16:45.0900436Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-12-18T00:16:45.0900609Z >>> averager.average_parameters(model.parameters()) 2024-12-18T00:16:45.0900698Z 2024-12-18T00:16:45.0900803Z .. warning :: 2024-12-18T00:16:45.0901056Z The last group size in the dict must be the size of the provided ``process_group``, 2024-12-18T00:16:45.0901293Z which indicates model averaging at the highest level of the hierarchy. 2024-12-18T00:16:45.0901607Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-12-18T00:16:45.0901694Z 2024-12-18T00:16:45.0901797Z .. warning :: 2024-12-18T00:16:45.0902032Z `HierarchicalModelAverager` is experimental and subject to change. 2024-12-18T00:16:45.0902130Z 2024-12-18T00:16:45.0902384Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0902470Z 2024-12-18T00:16:45.0902580Z warnings.warn(msg) 2024-12-18T00:16:45.0902665Z 2024-12-18T00:16:45.0902874Z --- Parse Warning: 46 / 105 --- 2024-12-18T00:16:45.0903946Z /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. 2024-12-18T00:16:45.0904224Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0904308Z 2024-12-18T00:16:45.0904605Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-12-18T00:16:45.0904864Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-12-18T00:16:45.0904951Z 2024-12-18T00:16:45.0905131Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-12-18T00:16:45.0905216Z 2024-12-18T00:16:45.0905322Z .. warning:: 2024-12-18T00:16:45.0905499Z Current implementation only supports loading Tensors. 2024-12-18T00:16:45.0905585Z 2024-12-18T00:16:45.0905720Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0905821Z >>> sd = {"mode": model} 2024-12-18T00:16:45.0905926Z >>> dcp.load( 2024-12-18T00:16:45.0906018Z >>> sd, 2024-12-18T00:16:45.0906172Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T00:16:45.0906312Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T00:16:45.0906430Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T00:16:45.0906531Z >>> ) 2024-12-18T00:16:45.0906616Z 2024-12-18T00:16:45.0906875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0906973Z 2024-12-18T00:16:45.0907075Z warnings.warn(msg) 2024-12-18T00:16:45.0907171Z 2024-12-18T00:16:45.0907355Z --- Parse Warning: 47 / 105 --- 2024-12-18T00:16:45.0908405Z /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. 2024-12-18T00:16:45.0908667Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0908755Z 2024-12-18T00:16:45.0909132Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-12-18T00:16:45.0909510Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-12-18T00:16:45.0909638Z metadata file, like Torch Save files. 2024-12-18T00:16:45.0909728Z 2024-12-18T00:16:45.0909943Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-12-18T00:16:45.0910026Z 2024-12-18T00:16:45.0910144Z .. warning:: 2024-12-18T00:16:45.0910328Z Current implementation only supports loading Tensors. 2024-12-18T00:16:45.0910414Z 2024-12-18T00:16:45.0910544Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0910645Z >>> sd = {"mode": model} 2024-12-18T00:16:45.0910737Z >>> dcp.load( 2024-12-18T00:16:45.0910838Z >>> sd, 2024-12-18T00:16:45.0910991Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T00:16:45.0911126Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T00:16:45.0911247Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T00:16:45.0911344Z >>> ) 2024-12-18T00:16:45.0911430Z 2024-12-18T00:16:45.0911681Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0911773Z 2024-12-18T00:16:45.0911873Z warnings.warn(msg) 2024-12-18T00:16:45.0911969Z 2024-12-18T00:16:45.0912155Z --- Parse Warning: 48 / 105 --- 2024-12-18T00:16:45.0913207Z /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=220. 2024-12-18T00:16:45.0913483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0913568Z 2024-12-18T00:16:45.0913794Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-12-18T00:16:45.0913881Z 2024-12-18T00:16:45.0914054Z This is the current recommended way to checkpoint FSDP. 2024-12-18T00:16:45.0914154Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0914309Z >>> import torch.distributed.checkpoint as dist_cp 2024-12-18T00:16:45.0914410Z >>> # Save 2024-12-18T00:16:45.0914514Z >>> model: torch.nn.Model 2024-12-18T00:16:45.0914648Z >>> optim_params = model.parameters() 2024-12-18T00:16:45.0914796Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-12-18T00:16:45.0914886Z >>> # Save 2024-12-18T00:16:45.0915118Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-12-18T00:16:45.0915217Z >>> state_dict = { 2024-12-18T00:16:45.0915384Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-12-18T00:16:45.0915496Z >>> "model": model.state_dict() 2024-12-18T00:16:45.0915593Z >>> } 2024-12-18T00:16:45.0915698Z >>> dist_cp.save_state_dict( 2024-12-18T00:16:45.0915807Z >>> state_dict=optim_state, 2024-12-18T00:16:45.0916000Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-12-18T00:16:45.0916134Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-12-18T00:16:45.0916233Z >>> ) 2024-12-18T00:16:45.0916320Z >>> 2024-12-18T00:16:45.0916407Z >>> # Load 2024-12-18T00:16:45.0916652Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-12-18T00:16:45.0916784Z >>> model_state_dict = model_tp.state_dict() 2024-12-18T00:16:45.0916893Z >>> checkpoint = { 2024-12-18T00:16:45.0917001Z >>> "model": model_state_dict 2024-12-18T00:16:45.0917098Z >>> } 2024-12-18T00:16:45.0917202Z >>> dist_cp.load_state_dict( 2024-12-18T00:16:45.0917312Z >>> state_dict=checkpoint, 2024-12-18T00:16:45.0917511Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-12-18T00:16:45.0917646Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-12-18T00:16:45.0917774Z >>> ) 2024-12-18T00:16:45.0917959Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-12-18T00:16:45.0918046Z >>> 2024-12-18T00:16:45.0918227Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-12-18T00:16:45.0918328Z >>> model_state_dict, 2024-12-18T00:16:45.0918544Z >>> optimizer_key="optimizer", 2024-12-18T00:16:45.0918801Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-12-18T00:16:45.0918908Z >>> ) 2024-12-18T00:16:45.0918997Z >>> 2024-12-18T00:16:45.0919148Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-12-18T00:16:45.0919292Z >>> model, optim, optim_state["optimizer"] 2024-12-18T00:16:45.0919381Z >>> ) 2024-12-18T00:16:45.0919480Z >>> 2024-12-18T00:16:45.0919605Z >>> optim.load_state_dict(flattened_osd) 2024-12-18T00:16:45.0919692Z 2024-12-18T00:16:45.0919958Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0920048Z 2024-12-18T00:16:45.0920162Z warnings.warn(msg) 2024-12-18T00:16:45.0920250Z 2024-12-18T00:16:45.0920445Z --- Parse Warning: 49 / 105 --- 2024-12-18T00:16:45.0921411Z /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=110. 2024-12-18T00:16:45.0921673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0921766Z 2024-12-18T00:16:45.0922056Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-12-18T00:16:45.0922157Z 2024-12-18T00:16:45.0922453Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-12-18T00:16:45.0922548Z 2024-12-18T00:16:45.0922828Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T00:16:45.0922951Z will be visible to the whole process. 2024-12-18T00:16:45.0923044Z 2024-12-18T00:16:45.0923318Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-12-18T00:16:45.0923414Z 2024-12-18T00:16:45.0923538Z 1) set_up_planner - called on all ranks. 2024-12-18T00:16:45.0923670Z Signals the start of a checkpoint save. 2024-12-18T00:16:45.0923766Z 2024-12-18T00:16:45.0923896Z 2) create_local_plan - called on all ranks. 2024-12-18T00:16:45.0924194Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-12-18T00:16:45.0924282Z 2024-12-18T00:16:45.0924475Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T00:16:45.0924672Z Takes the SavePlan from all ranks and make any global decision. 2024-12-18T00:16:45.0924760Z 2024-12-18T00:16:45.0924887Z 4) finish_plan - called on all ranks. 2024-12-18T00:16:45.0925108Z This gives each rank a chance to adjust to global planning decisions. 2024-12-18T00:16:45.0925204Z 2024-12-18T00:16:45.0925358Z 5) resolve_data - called multiple times on each rank 2024-12-18T00:16:45.0925564Z Lookups a value on the `state_dict` for the storage layer to write. 2024-12-18T00:16:45.0925658Z 2024-12-18T00:16:45.0925965Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-12-18T00:16:45.0926162Z most changes can be expressed by changes in a single method. 2024-12-18T00:16:45.0926246Z 2024-12-18T00:16:45.0926381Z There are 3 usual patterns of extension: 2024-12-18T00:16:45.0926466Z 2024-12-18T00:16:45.0926721Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-12-18T00:16:45.0926958Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-12-18T00:16:45.0927041Z 2024-12-18T00:16:45.0927168Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0927330Z >>> class RenamePlanner(DefaultSavePlanner): 2024-12-18T00:16:45.0927460Z >>> def set_up_planner( 2024-12-18T00:16:45.0927561Z >>> self, 2024-12-18T00:16:45.0927675Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T00:16:45.0927814Z >>> storage_meta: Optional[StorageMeta], 2024-12-18T00:16:45.0927921Z >>> is_coordinator: bool, 2024-12-18T00:16:45.0928055Z >>> ) -> None: 2024-12-18T00:16:45.0928197Z >>> # prefix all keys with `foo_`` 2024-12-18T00:16:45.0928496Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-12-18T00:16:45.0928594Z 2024-12-18T00:16:45.0928926Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-12-18T00:16:45.0929021Z 2024-12-18T00:16:45.0929137Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0929267Z >>> class FP16Planner(DefaultSavePlanner): 2024-12-18T00:16:45.0929393Z >>> def create_local_plan(self): 2024-12-18T00:16:45.0929516Z >>> plan = super().create_local_plan() 2024-12-18T00:16:45.0929628Z >>> for p in plan: 2024-12-18T00:16:45.0929746Z >>> if p.tensor_data is not None: 2024-12-18T00:16:45.0929918Z >>> p.tensor_data.properties.dtype = torch.float16 2024-12-18T00:16:45.0930017Z >>> return plan 2024-12-18T00:16:45.0930109Z >>> 2024-12-18T00:16:45.0930240Z >>> def resolve_data(self, write_item): 2024-12-18T00:16:45.0930366Z >>> item = super().resolve_data(write_item) 2024-12-18T00:16:45.0930652Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-12-18T00:16:45.0930736Z 2024-12-18T00:16:45.0931088Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-12-18T00:16:45.0931173Z 2024-12-18T00:16:45.0931292Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0931417Z >>> from itertools import zip_longest 2024-12-18T00:16:45.0931530Z >>> from dataclasses import replace 2024-12-18T00:16:45.0931709Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-12-18T00:16:45.0931985Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-12-18T00:16:45.0932126Z >>> # This sample doesn't handle ShardedTensors 2024-12-18T00:16:45.0932269Z >>> def create_global_plan(self, all_plans): 2024-12-18T00:16:45.0932421Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2024-12-18T00:16:45.0932542Z >>> items_per_rank = [ 2024-12-18T00:16:45.0932683Z >>> [item for item in items if item is not None] 2024-12-18T00:16:45.0932859Z >>> for items in zip(*zip_longest(*iters), strict=True) 2024-12-18T00:16:45.0932950Z >>> ] 2024-12-18T00:16:45.0933049Z >>> all_plans = [ 2024-12-18T00:16:45.0933182Z >>> replace(plan, items=items) 2024-12-18T00:16:45.0933376Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2024-12-18T00:16:45.0933479Z >>> ] 2024-12-18T00:16:45.0933624Z >>> return super().create_global_plan(all_plans) 2024-12-18T00:16:45.0933725Z 2024-12-18T00:16:45.0933996Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-12-18T00:16:45.0934268Z accomplished by having each rank contribute their data items in the local plan and 2024-12-18T00:16:45.0934397Z the global planner aggregate them: 2024-12-18T00:16:45.0934484Z 2024-12-18T00:16:45.0934621Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0934782Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-12-18T00:16:45.0934915Z >>> def create_local_plan(self) -> SavePlan: 2024-12-18T00:16:45.0935051Z >>> plan = super().create_local_plan() 2024-12-18T00:16:45.0935251Z >>> return replace(plan, planner_data="per-rank-data") 2024-12-18T00:16:45.0935393Z >>> 2024-12-18T00:16:45.0935695Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-12-18T00:16:45.0935911Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-12-18T00:16:45.0936101Z >>> merged_data = [p.planner_data for p in global_plan] 2024-12-18T00:16:45.0936304Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-12-18T00:16:45.0936434Z >>> return global_plan, metadata 2024-12-18T00:16:45.0936522Z 2024-12-18T00:16:45.0936789Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0936877Z 2024-12-18T00:16:45.0936994Z warnings.warn(msg) 2024-12-18T00:16:45.0937080Z 2024-12-18T00:16:45.0937280Z --- Parse Warning: 50 / 105 --- 2024-12-18T00:16:45.0938261Z /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=272. 2024-12-18T00:16:45.0938529Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0938627Z 2024-12-18T00:16:45.0938920Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-12-18T00:16:45.0939019Z 2024-12-18T00:16:45.0939306Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-12-18T00:16:45.0939393Z 2024-12-18T00:16:45.0939686Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T00:16:45.0939806Z will be visible to the whole process. 2024-12-18T00:16:45.0939910Z 2024-12-18T00:16:45.0940185Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-12-18T00:16:45.0940271Z 2024-12-18T00:16:45.0940415Z 1) set_up_planner - called on all ranks. 2024-12-18T00:16:45.0940547Z Signals the start of loading a checkpoint. 2024-12-18T00:16:45.0940643Z 2024-12-18T00:16:45.0940774Z 2) create_local_plan - called on all ranks. 2024-12-18T00:16:45.0941072Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-12-18T00:16:45.0941156Z 2024-12-18T00:16:45.0941340Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T00:16:45.0941548Z Takes the LoadPlan from all ranks and make any global decision. 2024-12-18T00:16:45.0941634Z 2024-12-18T00:16:45.0941795Z 4) load_bytes - called multiple times on each rank 2024-12-18T00:16:45.0941966Z This is called once per non-tensor value in state_dict. 2024-12-18T00:16:45.0942051Z 2024-12-18T00:16:45.0942285Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-12-18T00:16:45.0942470Z They are called in pair for each Tensor value in state_dict. 2024-12-18T00:16:45.0942569Z 2024-12-18T00:16:45.0942872Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-12-18T00:16:45.0943067Z most changes can be expressed by changes in a single method. 2024-12-18T00:16:45.0943154Z 2024-12-18T00:16:45.0943289Z There are two usual patterns of extension: 2024-12-18T00:16:45.0943385Z 2024-12-18T00:16:45.0943641Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-12-18T00:16:45.0943908Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-12-18T00:16:45.0944136Z to keep a reference to the original state_dict as load happens in place so 2024-12-18T00:16:45.0944278Z we need to be able to perform it in place 2024-12-18T00:16:45.0944363Z 2024-12-18T00:16:45.0944483Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0944667Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-12-18T00:16:45.0944799Z >>> def set_up_planner( 2024-12-18T00:16:45.0944901Z >>> self, 2024-12-18T00:16:45.0945015Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T00:16:45.0945118Z >>> metadata: Metadata, 2024-12-18T00:16:45.0945239Z >>> is_coordinator: bool, 2024-12-18T00:16:45.0945332Z >>> ) -> None: 2024-12-18T00:16:45.0945526Z >>> self.original_state_dict = state_dict 2024-12-18T00:16:45.0945706Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-12-18T00:16:45.0945795Z >>> 2024-12-18T00:16:45.0945924Z >>> if self.flatten_sharded_tensors: 2024-12-18T00:16:45.0946081Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-12-18T00:16:45.0946180Z >>> 2024-12-18T00:16:45.0946294Z >>> if self.flatten_state_dict: 2024-12-18T00:16:45.0946493Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-12-18T00:16:45.0946585Z >>> 2024-12-18T00:16:45.0946700Z >>> self.state_dict = state_dict 2024-12-18T00:16:45.0946824Z >>> self.metadata = metadata 2024-12-18T00:16:45.0946951Z >>> self.is_coordinator = is_coordinator 2024-12-18T00:16:45.0947055Z >>> 2024-12-18T00:16:45.0947182Z >>> def load_bytes(self, read_item, value): 2024-12-18T00:16:45.0947300Z >>> # Remove the "foo_" prefix 2024-12-18T00:16:45.0947638Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-12-18T00:16:45.0947725Z 2024-12-18T00:16:45.0947820Z 2024-12-18T00:16:45.0948084Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-12-18T00:16:45.0948182Z 2024-12-18T00:16:45.0948301Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.0948461Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-12-18T00:16:45.0948594Z >>> def resolve_tensor(self, read_item): 2024-12-18T00:16:45.0948734Z >>> tensor = super().resolve_tensor(read_item) 2024-12-18T00:16:45.0948892Z >>> return torch.empty_like(tensor, device="cpu") 2024-12-18T00:16:45.0948980Z >>> 2024-12-18T00:16:45.0949126Z >>> def commit_tensor(self, read_item, tensor): 2024-12-18T00:16:45.0949288Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-12-18T00:16:45.0949374Z 2024-12-18T00:16:45.0949643Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0949731Z 2024-12-18T00:16:45.0949843Z warnings.warn(msg) 2024-12-18T00:16:45.0949930Z 2024-12-18T00:16:45.0950127Z --- Parse Warning: 51 / 105 --- 2024-12-18T00:16:45.0951102Z /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=61. 2024-12-18T00:16:45.0951369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0951467Z 2024-12-18T00:16:45.0951615Z Load a distributed ``state_dict`` in SPMD style. 2024-12-18T00:16:45.0951716Z 2024-12-18T00:16:45.0951902Z Each rank will try to read the least amount of data necessary 2024-12-18T00:16:45.0952146Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-12-18T00:16:45.0952405Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-12-18T00:16:45.0952492Z 2024-12-18T00:16:45.0952771Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T00:16:45.0953028Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-12-18T00:16:45.0953210Z ``load_state_dict`` once the deserialization is complete. 2024-12-18T00:16:45.0953471Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2024-12-18T00:16:45.0953685Z it in the ``state_dict`` with the deserialized object. 2024-12-18T00:16:45.0953785Z 2024-12-18T00:16:45.0953886Z .. warning:: 2024-12-18T00:16:45.0954072Z All tensors in ``state_dict`` must be allocated on their 2024-12-18T00:16:45.0954239Z destination device *prior to* calling this function. 2024-12-18T00:16:45.0954363Z 2024-12-18T00:16:45.0954618Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-12-18T00:16:45.0954716Z on state_dict. 2024-12-18T00:16:45.0954813Z 2024-12-18T00:16:45.0954906Z .. warning:: 2024-12-18T00:16:45.0955119Z Users must call `load_state_dict` on the root module to ensure load 2024-12-18T00:16:45.0955303Z pos-processing and non-tensor data properly propagates. 2024-12-18T00:16:45.0955388Z 2024-12-18T00:16:45.0955490Z .. note: 2024-12-18T00:16:45.0955717Z If no process group is initialized, this function will assume the intent 2024-12-18T00:16:45.0955958Z is to load a checkpoint into the local process. This can be useful in the 2024-12-18T00:16:45.0956208Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-12-18T00:16:45.0956320Z or ShardedTensor) 2024-12-18T00:16:45.0956406Z 2024-12-18T00:16:45.0956497Z .. note: 2024-12-18T00:16:45.0956653Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T00:16:45.0956739Z 2024-12-18T00:16:45.0956838Z Args: 2024-12-18T00:16:45.0956996Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T00:16:45.0957142Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T00:16:45.0957366Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T00:16:45.0957573Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T00:16:45.0957756Z It can also be a key if the storage is a key-value store. 2024-12-18T00:16:45.0957863Z (Default: ``None``) 2024-12-18T00:16:45.0958008Z storage_reader (Optional[StorageReader]): 2024-12-18T00:16:45.0958216Z Instance of StorageWriter used to perform reads. If this is not 2024-12-18T00:16:45.0958517Z specified, DCP will automatically infer the reader based on the 2024-12-18T00:16:45.0958737Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T00:16:45.0958851Z be raised. (Default: ``None``) 2024-12-18T00:16:45.0958985Z planner (Optional[LoadPlanner]): 2024-12-18T00:16:45.0959192Z Instance of LoadPlanner. If this is not specificed, the default 2024-12-18T00:16:45.0959336Z planner will be used. (Default: ``None``) 2024-12-18T00:16:45.0959468Z process_group (Optional[ProcessGroup]): 2024-12-18T00:16:45.0959651Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T00:16:45.0959765Z (Default: ``None``) 2024-12-18T00:16:45.0959856Z 2024-12-18T00:16:45.0959959Z Returns: 2024-12-18T00:16:45.0960047Z None. 2024-12-18T00:16:45.0960132Z 2024-12-18T00:16:45.0960234Z Examples 2024-12-18T00:16:45.0960336Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0960451Z >>> my_model = MyModule() 2024-12-18T00:16:45.0960593Z >>> optimizer = Adagrad(my_model.parameters()) 2024-12-18T00:16:45.0960728Z >>> model_state_dict = my_model.state_dict() 2024-12-18T00:16:45.0961042Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-12-18T00:16:45.0961133Z 2024-12-18T00:16:45.0961307Z >>> torch.distributed.checkpoint.load_state_dict( 2024-12-18T00:16:45.0961427Z >>> state_dict=model_state_dict, 2024-12-18T00:16:45.0961563Z >>> storage_reader=fs_storage_reader, 2024-12-18T00:16:45.0961653Z >>> ) 2024-12-18T00:16:45.0961743Z 2024-12-18T00:16:45.0961962Z >>> # module.load_state_dict() function might have customized steps 2024-12-18T00:16:45.0962159Z >>> # to flush the state_dict, must call it to 2024-12-18T00:16:45.0962287Z >>> # ensure correct behavior. 2024-12-18T00:16:45.0962424Z >>> my_model.load_state_dict(model_state_dict) 2024-12-18T00:16:45.0962514Z 2024-12-18T00:16:45.0962622Z .. note:: 2024-12-18T00:16:45.0962886Z load_state_dict uses collectives to coordinate reads across ranks. 2024-12-18T00:16:45.0963121Z For NCCL-based process groups, internal tensor representations of 2024-12-18T00:16:45.0963357Z objects must be moved to the GPU device before communication takes place. 2024-12-18T00:16:45.0963598Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T00:16:45.0963831Z and it is the user's responsibility to ensure that this is set so that each 2024-12-18T00:16:45.0964021Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T00:16:45.0964124Z 2024-12-18T00:16:45.0964383Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0964486Z 2024-12-18T00:16:45.0964591Z warnings.warn(msg) 2024-12-18T00:16:45.0964696Z 2024-12-18T00:16:45.0964908Z --- Parse Warning: 52 / 105 --- 2024-12-18T00:16:45.0965874Z /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=67. 2024-12-18T00:16:45.0966158Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0966245Z 2024-12-18T00:16:45.0966385Z Save a distributed model in SPMD style. 2024-12-18T00:16:45.0966474Z 2024-12-18T00:16:45.0966684Z This function is different from ``torch.save()`` as it handles 2024-12-18T00:16:45.0966949Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-12-18T00:16:45.0967041Z 2024-12-18T00:16:45.0967316Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T00:16:45.0967474Z save will call ``state_dict`` before serialization. 2024-12-18T00:16:45.0967573Z 2024-12-18T00:16:45.0967667Z .. warning:: 2024-12-18T00:16:45.0967912Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-12-18T00:16:45.0968029Z for saved state_dicts. 2024-12-18T00:16:45.0968114Z 2024-12-18T00:16:45.0968222Z .. warning:: 2024-12-18T00:16:45.0968433Z If using the `process_group` argument, make sure that only its ranks 2024-12-18T00:16:45.0968652Z call `save_state_dict` and that all data in state_dict belong to it. 2024-12-18T00:16:45.0968738Z 2024-12-18T00:16:45.0968830Z .. note:: 2024-12-18T00:16:45.0969100Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-12-18T00:16:45.0969364Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-12-18T00:16:45.0969490Z group needs to be passed in. 2024-12-18T00:16:45.0969578Z 2024-12-18T00:16:45.0969668Z .. note:: 2024-12-18T00:16:45.0969949Z If no process group is available, this function assumes the intention is to save the 2024-12-18T00:16:45.0970067Z state_dict in the local process. 2024-12-18T00:16:45.0970166Z 2024-12-18T00:16:45.0970255Z .. note: 2024-12-18T00:16:45.0970411Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T00:16:45.0970496Z 2024-12-18T00:16:45.0970584Z 2024-12-18T00:16:45.0970683Z Args: 2024-12-18T00:16:45.0970843Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T00:16:45.0971002Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T00:16:45.0971217Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T00:16:45.0971472Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T00:16:45.0971678Z It can also be a key if the storage is a key-value store. 2024-12-18T00:16:45.0971778Z (Default: ``None``) 2024-12-18T00:16:45.0971920Z storage_writer (Optional[StorageWriter]): 2024-12-18T00:16:45.0972158Z Instance of StorageWriter used to perform writes. If this is not 2024-12-18T00:16:45.0972404Z specified, DCP will automatically infer the writer based on the 2024-12-18T00:16:45.0972607Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T00:16:45.0972723Z be raised. (Default: ``None``) 2024-12-18T00:16:45.0972856Z planner (Optional[SavePlanner]): 2024-12-18T00:16:45.0973057Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T00:16:45.0973201Z planner will be used. (Default: ``None``) 2024-12-18T00:16:45.0973334Z process_group (Optional[ProcessGroup]): 2024-12-18T00:16:45.0973534Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T00:16:45.0973818Z (Default: ``None``) 2024-12-18T00:16:45.0973908Z 2024-12-18T00:16:45.0974011Z Returns: 2024-12-18T00:16:45.0974176Z Metadata: Metadata object for the saved checkpoint. 2024-12-18T00:16:45.0974275Z 2024-12-18T00:16:45.0974366Z Example: 2024-12-18T00:16:45.0974471Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0974587Z >>> my_model = MyModule() 2024-12-18T00:16:45.0974674Z 2024-12-18T00:16:45.0974800Z >>> state_dict = {"model": my_model} 2024-12-18T00:16:45.0974885Z 2024-12-18T00:16:45.0975184Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T00:16:45.0975332Z >>> torch.distributed.checkpoint.save( 2024-12-18T00:16:45.0975440Z >>> state_dict=state_dict, 2024-12-18T00:16:45.0975572Z >>> storage_writer=fs_storage_writer, 2024-12-18T00:16:45.0975664Z >>> ) 2024-12-18T00:16:45.0975761Z 2024-12-18T00:16:45.0975854Z .. note:: 2024-12-18T00:16:45.0976072Z save_state_dict uses collectives to coordinate writes across ranks. 2024-12-18T00:16:45.0976301Z For NCCL-based process groups, internal tensor representations of 2024-12-18T00:16:45.0976533Z objects must be moved to the GPU device before communication takes place. 2024-12-18T00:16:45.0976765Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T00:16:45.0976970Z and it is the user's responsibility to ensure that this is set so that 2024-12-18T00:16:45.0977180Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T00:16:45.0977267Z 2024-12-18T00:16:45.0977520Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0977619Z 2024-12-18T00:16:45.0977719Z warnings.warn(msg) 2024-12-18T00:16:45.0977820Z 2024-12-18T00:16:45.0978036Z --- Parse Warning: 53 / 105 --- 2024-12-18T00:16:45.0979036Z /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=170. 2024-12-18T00:16:45.0979319Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0979587Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-12-18T00:16:45.0979893Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-12-18T00:16:45.0979981Z 2024-12-18T00:16:45.0980090Z .. warning:: 2024-12-18T00:16:45.0980254Z This feature is experimental and subject to change. 2024-12-18T00:16:45.0980353Z 2024-12-18T00:16:45.0980441Z Args: 2024-12-18T00:16:45.0980599Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T00:16:45.0980878Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T00:16:45.0981093Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T00:16:45.0981312Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T00:16:45.0981521Z It can also be a key if the storage is a key-value store. 2024-12-18T00:16:45.0981654Z (Default: ``None``) 2024-12-18T00:16:45.0981806Z storage_writer (Optional[StorageWriter]): 2024-12-18T00:16:45.0982019Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-12-18T00:16:45.0982273Z this is not specified, DCP will automatically infer the writer based on the 2024-12-18T00:16:45.0982480Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T00:16:45.0982608Z be raised. (Default: ``None``) 2024-12-18T00:16:45.0982733Z planner (Optional[SavePlanner]): 2024-12-18T00:16:45.0982940Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T00:16:45.0983089Z planner will be used. (Default: ``None``) 2024-12-18T00:16:45.0983226Z process_group (Optional[ProcessGroup]): 2024-12-18T00:16:45.0983425Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T00:16:45.0983528Z (Default: ``None``) 2024-12-18T00:16:45.0983626Z 2024-12-18T00:16:45.0983718Z Returns: 2024-12-18T00:16:45.0983936Z Future: A future holding the resultant Metadata object from `save`. 2024-12-18T00:16:45.0984033Z 2024-12-18T00:16:45.0984125Z Example: 2024-12-18T00:16:45.0984240Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.0984347Z >>> my_model = MyModule() 2024-12-18T00:16:45.0984432Z 2024-12-18T00:16:45.0984563Z >>> state_dict = {"model": my_model} 2024-12-18T00:16:45.0984655Z 2024-12-18T00:16:45.0984965Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T00:16:45.0985176Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-12-18T00:16:45.0985297Z >>> state_dict=state_dict, 2024-12-18T00:16:45.0985425Z >>> storage_writer=fs_storage_writer, 2024-12-18T00:16:45.0985514Z >>> ) 2024-12-18T00:16:45.0985617Z >>> 2024-12-18T00:16:45.0985720Z >>> # ... do some work ... 2024-12-18T00:16:45.0985821Z >>> 2024-12-18T00:16:45.0985938Z >>> checkpoint_future.result() 2024-12-18T00:16:45.0986024Z 2024-12-18T00:16:45.0986122Z 2024-12-18T00:16:45.0986372Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0986470Z 2024-12-18T00:16:45.0986571Z warnings.warn(msg) 2024-12-18T00:16:45.0986655Z 2024-12-18T00:16:45.0986866Z --- Parse Warning: 54 / 105 --- 2024-12-18T00:16:45.0987916Z /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=91. 2024-12-18T00:16:45.0988192Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0988276Z 2024-12-18T00:16:45.0988490Z Initialize rendezvous event object and record its operations. 2024-12-18T00:16:45.0988575Z 2024-12-18T00:16:45.0988663Z Args: 2024-12-18T00:16:45.0988812Z run_id (str): The run id of the rendezvous. 2024-12-18T00:16:45.0988964Z message (str): The message describing the event. 2024-12-18T00:16:45.0989233Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-12-18T00:16:45.0989421Z name (str): Event name. (E.g. Current action being performed). 2024-12-18T00:16:45.0989619Z hostname (str): Hostname of the node. 2024-12-18T00:16:45.0989767Z pid (Optional[int]): The process id of the node. 2024-12-18T00:16:45.0990013Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-12-18T00:16:45.0990322Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-12-18T00:16:45.0990513Z rank (Optional[int]): The rank of the node, if known. 2024-12-18T00:16:45.0990621Z Returns: 2024-12-18T00:16:45.0990712Z None 2024-12-18T00:16:45.0990814Z Example: 2024-12-18T00:16:45.0990944Z >>> # See DynamicRendezvousHandler class 2024-12-18T00:16:45.0991040Z >>> def _record( 2024-12-18T00:16:45.0991146Z ... self, 2024-12-18T00:16:45.0991245Z ... message: str, 2024-12-18T00:16:45.0991402Z ... node_state: NodeState = NodeState.RUNNING, 2024-12-18T00:16:45.0991516Z ... rank: Optional[int] = None, 2024-12-18T00:16:45.0991611Z ... ) -> None: 2024-12-18T00:16:45.0991748Z ... construct_and_record_rdzv_event( 2024-12-18T00:16:45.0991916Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-12-18T00:16:45.0992054Z ... run_id=self._settings.run_id, 2024-12-18T00:16:45.0992156Z ... message=message, 2024-12-18T00:16:45.0992281Z ... node_state=node_state, 2024-12-18T00:16:45.0992403Z ... hostname=self._this_node.addr, 2024-12-18T00:16:45.0992522Z ... pid=self._this_node.pid, 2024-12-18T00:16:45.0992661Z ... local_id=self._this_node.local_id, 2024-12-18T00:16:45.0992758Z ... rank=rank, 2024-12-18T00:16:45.0992858Z ... ) 2024-12-18T00:16:45.0992944Z 2024-12-18T00:16:45.0993199Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.0993297Z 2024-12-18T00:16:45.0993403Z warnings.warn(msg) 2024-12-18T00:16:45.0993501Z 2024-12-18T00:16:45.0993692Z --- Parse Warning: 55 / 105 --- 2024-12-18T00:16:45.0994616Z /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=113. 2024-12-18T00:16:45.0994885Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.0994972Z 2024-12-18T00:16:45.0995157Z This configures FSDP-native mixed precision training. 2024-12-18T00:16:45.0995242Z 2024-12-18T00:16:45.0995347Z Attributes: 2024-12-18T00:16:45.0995584Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-12-18T00:16:45.0995799Z parameters during forward and backward and thus the dtype for 2024-12-18T00:16:45.0996024Z forward and backward computation. Outside forward and backward, the 2024-12-18T00:16:45.0996223Z *sharded* parameters are kept in full precision (e.g. for the 2024-12-18T00:16:45.0996449Z optimizer step), and for model checkpointing, the parameters are 2024-12-18T00:16:45.0996610Z always saved in full precision. (Default: ``None``) 2024-12-18T00:16:45.0996840Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T00:16:45.0997060Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-12-18T00:16:45.0997257Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-12-18T00:16:45.0997460Z the ``param_dtype`` value, still running gradient reduction in low 2024-12-18T00:16:45.0997673Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-12-18T00:16:45.0997882Z to force gradient reduction to run in full precision. (Default: 2024-12-18T00:16:45.0997975Z ``None``) 2024-12-18T00:16:45.0998237Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T00:16:45.0998549Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-12-18T00:16:45.0998763Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-12-18T00:16:45.0998974Z dtype thereafter. For model checkpointing, the buffers are saved 2024-12-18T00:16:45.0999229Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-12-18T00:16:45.0999332Z ``None``) 2024-12-18T00:16:45.0999535Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-12-18T00:16:45.0999770Z gradients to full precision after the backward pass in preparation 2024-12-18T00:16:45.0999975Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-12-18T00:16:45.1000188Z in the dtype used for gradient reduction, which can save memory if 2024-12-18T00:16:45.1000397Z using a custom optimizer that supports running in low precision. 2024-12-18T00:16:45.1000499Z (Default: ``False``) 2024-12-18T00:16:45.1000718Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-12-18T00:16:45.1000919Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-12-18T00:16:45.1001149Z that parameter and input dtypes match for forward computation, as 2024-12-18T00:16:45.1001355Z required by many ops. This may need to be set to ``True`` when only 2024-12-18T00:16:45.1001589Z applying mixed precision to some but not all FSDP modules, in which 2024-12-18T00:16:45.1001801Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-12-18T00:16:45.1001902Z (Default: ``False``) 2024-12-18T00:16:45.1002131Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-12-18T00:16:45.1002331Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-12-18T00:16:45.1002535Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-12-18T00:16:45.1002681Z this does not do anything. (Default: ``True``) 2024-12-18T00:16:45.1002913Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-12-18T00:16:45.1003097Z module classes to ignore for mixed precision when using an 2024-12-18T00:16:45.1003281Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-12-18T00:16:45.1003503Z applied to them separately with mixed precision disabled (meaning 2024-12-18T00:16:45.1003707Z that the final FSDP construction would deviate from the specified 2024-12-18T00:16:45.1003908Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-12-18T00:16:45.1004109Z not do anything. This API is experimental and subject to change. 2024-12-18T00:16:45.1004231Z (Default: ``(_BatchNorm,)``) 2024-12-18T00:16:45.1004322Z 2024-12-18T00:16:45.1004495Z .. note:: This API is experimental and subject to change. 2024-12-18T00:16:45.1004593Z 2024-12-18T00:16:45.1004820Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-12-18T00:16:45.1004919Z 2024-12-18T00:16:45.1005102Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-12-18T00:16:45.1005230Z precision, but buffers are not. 2024-12-18T00:16:45.1005315Z 2024-12-18T00:16:45.1005523Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-12-18T00:16:45.1005742Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-12-18T00:16:45.1005972Z Disabling FSDP's mixed precision for those norm modules only means that 2024-12-18T00:16:45.1006194Z the affine parameters are kept in ``float32``. However, this incurs 2024-12-18T00:16:45.1006432Z separate all-gathers and reduce-scatters for those norm modules, which 2024-12-18T00:16:45.1006726Z may be inefficient, so if the workload permits, the user should prefer 2024-12-18T00:16:45.1006879Z to still apply mixed precision to those modules. 2024-12-18T00:16:45.1006964Z 2024-12-18T00:16:45.1007181Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-12-18T00:16:45.1007413Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-12-18T00:16:45.1007742Z modules will have FSDP applied to them separately with mixed precision 2024-12-18T00:16:45.1007920Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-12-18T00:16:45.1008018Z 2024-12-18T00:16:45.1008226Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-12-18T00:16:45.1008439Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-12-18T00:16:45.1008627Z its ``cast_root_forward_inputs`` takes precedence over its 2024-12-18T00:16:45.1008808Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-12-18T00:16:45.1009038Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-12-18T00:16:45.1009258Z sufficient for the typical case where each FSDP instance has the same 2024-12-18T00:16:45.1009500Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-12-18T00:16:45.1009684Z ``param_dtype`` at the beginning of the model's forward pass. 2024-12-18T00:16:45.1009771Z 2024-12-18T00:16:45.1009992Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-12-18T00:16:45.1010229Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-12-18T00:16:45.1010448Z values to configure casting inputs or not before each instance's 2024-12-18T00:16:45.1010641Z forward. In such a case, since the casts happen before each FSDP 2024-12-18T00:16:45.1010871Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-12-18T00:16:45.1011105Z submodules run before its FSDP submodules to avoid the activation dtype 2024-12-18T00:16:45.1011328Z being changed due to a different ``MixedPrecision`` configuration. 2024-12-18T00:16:45.1011414Z 2024-12-18T00:16:45.1011507Z Example:: 2024-12-18T00:16:45.1011606Z 2024-12-18T00:16:45.1011741Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1011923Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-12-18T00:16:45.1012025Z >>> model[1] = FSDP( 2024-12-18T00:16:45.1012117Z >>> model[1], 2024-12-18T00:16:45.1012437Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-12-18T00:16:45.1012526Z >>> ) 2024-12-18T00:16:45.1012635Z >>> model = FSDP( 2024-12-18T00:16:45.1012727Z >>> model, 2024-12-18T00:16:45.1013043Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-12-18T00:16:45.1013134Z >>> ) 2024-12-18T00:16:45.1013217Z 2024-12-18T00:16:45.1013441Z The above shows a working example. On the other hand, if ``model[1]`` 2024-12-18T00:16:45.1013643Z were replaced with ``model[0]``, meaning that the submodule using 2024-12-18T00:16:45.1013884Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-12-18T00:16:45.1014105Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-12-18T00:16:45.1014195Z ones. 2024-12-18T00:16:45.1014294Z 2024-12-18T00:16:45.1014378Z 2024-12-18T00:16:45.1014641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1014725Z 2024-12-18T00:16:45.1014839Z warnings.warn(msg) 2024-12-18T00:16:45.1014922Z 2024-12-18T00:16:45.1015135Z --- Parse Warning: 56 / 105 --- 2024-12-18T00:16:45.1016366Z /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=649. 2024-12-18T00:16:45.1016655Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1016962Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T00:16:45.1017048Z 2024-12-18T00:16:45.1017326Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-12-18T00:16:45.1017531Z The target module does not have to be a FSDP module. If the target 2024-12-18T00:16:45.1017742Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-12-18T00:16:45.1017841Z 2024-12-18T00:16:45.1018035Z .. note:: This API should be called for only the top-level (root) 2024-12-18T00:16:45.1018144Z module. 2024-12-18T00:16:45.1018228Z 2024-12-18T00:16:45.1018448Z .. note:: This API enables users to transparently use the conventional 2024-12-18T00:16:45.1018638Z ``state_dict`` API to take model checkpoints in cases where the 2024-12-18T00:16:45.1018848Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-12-18T00:16:45.1019072Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-12-18T00:16:45.1019309Z instances, while dispatching into `sharded_state_dict` implementation 2024-12-18T00:16:45.1019416Z for FSDP: 2024-12-18T00:16:45.1019501Z 2024-12-18T00:16:45.1019612Z Example:: 2024-12-18T00:16:45.1019697Z 2024-12-18T00:16:45.1019830Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1019952Z >>> model = DDP(FSDP(...)) 2024-12-18T00:16:45.1020068Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:45.1020175Z >>> model, 2024-12-18T00:16:45.1020310Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T00:16:45.1020527Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-12-18T00:16:45.1020767Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-12-18T00:16:45.1020857Z >>> ) 2024-12-18T00:16:45.1021002Z >>> param_state_dict = model.state_dict() 2024-12-18T00:16:45.1021175Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T00:16:45.1021274Z 2024-12-18T00:16:45.1021363Z Args: 2024-12-18T00:16:45.1021494Z module (torch.nn.Module): Root module. 2024-12-18T00:16:45.1021741Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T00:16:45.1021981Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-12-18T00:16:45.1022119Z target ``state_dict_type``. 2024-12-18T00:16:45.1022374Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-12-18T00:16:45.1022506Z for the optimizer state dict. 2024-12-18T00:16:45.1022592Z 2024-12-18T00:16:45.1022683Z Returns: 2024-12-18T00:16:45.1022922Z A StateDictSettings that include the previous state_dict type and 2024-12-18T00:16:45.1023041Z configuration for the module. 2024-12-18T00:16:45.1023143Z 2024-12-18T00:16:45.1023393Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1023477Z 2024-12-18T00:16:45.1023586Z warnings.warn(msg) 2024-12-18T00:16:45.1023674Z 2024-12-18T00:16:45.1023877Z --- Parse Warning: 57 / 105 --- 2024-12-18T00:16:45.1025047Z /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=805. 2024-12-18T00:16:45.1025378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1025651Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T00:16:45.1025775Z 2024-12-18T00:16:45.1026096Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-12-18T00:16:45.1026230Z :meth:`set_state_dict_type` for the detail. 2024-12-18T00:16:45.1026333Z 2024-12-18T00:16:45.1026432Z Example:: 2024-12-18T00:16:45.1026532Z 2024-12-18T00:16:45.1026668Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1026780Z >>> model = DDP(FSDP(...)) 2024-12-18T00:16:45.1026913Z >>> with FSDP.state_dict_type( 2024-12-18T00:16:45.1027009Z >>> model, 2024-12-18T00:16:45.1027157Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T00:16:45.1027248Z >>> ): 2024-12-18T00:16:45.1027392Z >>> checkpoint = model.state_dict() 2024-12-18T00:16:45.1027475Z 2024-12-18T00:16:45.1027567Z Args: 2024-12-18T00:16:45.1027713Z module (torch.nn.Module): Root module. 2024-12-18T00:16:45.1027948Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T00:16:45.1028196Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-12-18T00:16:45.1028358Z configuration for the target ``state_dict_type``. 2024-12-18T00:16:45.1028594Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-12-18T00:16:45.1028806Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-12-18T00:16:45.1028898Z 2024-12-18T00:16:45.1029162Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1029247Z 2024-12-18T00:16:45.1029363Z warnings.warn(msg) 2024-12-18T00:16:45.1029452Z 2024-12-18T00:16:45.1029644Z --- Parse Warning: 58 / 105 --- 2024-12-18T00:16:45.1030834Z /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=1818. 2024-12-18T00:16:45.1031096Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1031198Z 2024-12-18T00:16:45.1031440Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-12-18T00:16:45.1031538Z 2024-12-18T00:16:45.1031736Z The given state-dict can be transformed to one of three types: 2024-12-18T00:16:45.1032038Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-12-18T00:16:45.1032137Z 2024-12-18T00:16:45.1032372Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-12-18T00:16:45.1032600Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-12-18T00:16:45.1032693Z avoid OOM. 2024-12-18T00:16:45.1032791Z 2024-12-18T00:16:45.1033024Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-12-18T00:16:45.1033234Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-12-18T00:16:45.1033334Z memory. 2024-12-18T00:16:45.1033419Z 2024-12-18T00:16:45.1033653Z For local state_dict, no transformation will be performed. But a state 2024-12-18T00:16:45.1033893Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-12-18T00:16:45.1034038Z nature (this is not supported yet). 2024-12-18T00:16:45.1034162Z 2024-12-18T00:16:45.1034255Z Example:: 2024-12-18T00:16:45.1034351Z 2024-12-18T00:16:45.1034481Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1034730Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T00:16:45.1034927Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T00:16:45.1035133Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T00:16:45.1035345Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T00:16:45.1035445Z >>> # Save a checkpoint 2024-12-18T00:16:45.1035560Z >>> model, optim = ... 2024-12-18T00:16:45.1035668Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:45.1035761Z >>> model, 2024-12-18T00:16:45.1035894Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:45.1036027Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1036190Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1036278Z >>> ) 2024-12-18T00:16:45.1036407Z >>> state_dict = model.state_dict() 2024-12-18T00:16:45.1036577Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T00:16:45.1036726Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T00:16:45.1036837Z >>> # Load a checkpoint 2024-12-18T00:16:45.1036939Z >>> model, optim = ... 2024-12-18T00:16:45.1037104Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T00:16:45.1037215Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:45.1037306Z >>> model, 2024-12-18T00:16:45.1037438Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:45.1037570Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1040334Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1040431Z >>> ) 2024-12-18T00:16:45.1040552Z >>> model.load_state_dict(state_dict) 2024-12-18T00:16:45.1040718Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T00:16:45.1040833Z >>> model, optim, optim_state_dict 2024-12-18T00:16:45.1040933Z >>> ) 2024-12-18T00:16:45.1041059Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T00:16:45.1041157Z 2024-12-18T00:16:45.1041246Z Args: 2024-12-18T00:16:45.1041444Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T00:16:45.1041659Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T00:16:45.1041793Z were passed into the optimizer ``optim``. 2024-12-18T00:16:45.1041985Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T00:16:45.1042080Z parameters. 2024-12-18T00:16:45.1042302Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-12-18T00:16:45.1042548Z transform. If the value is None, optim.state_dict() will be used. ( 2024-12-18T00:16:45.1042648Z Default: ``None``) 2024-12-18T00:16:45.1042897Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T00:16:45.1043136Z are sharded or ``None`` if using the default process group. ( 2024-12-18T00:16:45.1059090Z Default: ``None``) 2024-12-18T00:16:45.1059240Z 2024-12-18T00:16:45.1059341Z Returns: 2024-12-18T00:16:45.1059552Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-12-18T00:16:45.1059731Z ``model``. The sharding of the optimizer state is based on 2024-12-18T00:16:45.1059830Z ``state_dict_type``. 2024-12-18T00:16:45.1059917Z 2024-12-18T00:16:45.1060171Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1060257Z 2024-12-18T00:16:45.1060355Z warnings.warn(msg) 2024-12-18T00:16:45.1060540Z 2024-12-18T00:16:45.1060797Z --- Parse Warning: 59 / 105 --- 2024-12-18T00:16:45.1062012Z /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=1916. 2024-12-18T00:16:45.1062343Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1062425Z 2024-12-18T00:16:45.1062782Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-12-18T00:16:45.1062861Z 2024-12-18T00:16:45.1063025Z Given a ``optim_state_dict`` that is transformed through 2024-12-18T00:16:45.1063241Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-12-18T00:16:45.1063451Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-12-18T00:16:45.1063644Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-12-18T00:16:45.1063726Z 2024-12-18T00:16:45.1063860Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1064091Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T00:16:45.1064245Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T00:16:45.1064426Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T00:16:45.1064620Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T00:16:45.1064720Z >>> # Save a checkpoint 2024-12-18T00:16:45.1064815Z >>> model, optim = ... 2024-12-18T00:16:45.1064926Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:45.1065011Z >>> model, 2024-12-18T00:16:45.1065127Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:45.1065329Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1065473Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1065560Z >>> ) 2024-12-18T00:16:45.1065672Z >>> state_dict = model.state_dict() 2024-12-18T00:16:45.1065784Z >>> original_osd = optim.state_dict() 2024-12-18T00:16:45.1065917Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-12-18T00:16:45.1066000Z >>> model, 2024-12-18T00:16:45.1066088Z >>> optim, 2024-12-18T00:16:45.1066200Z >>> optim_state_dict=original_osd 2024-12-18T00:16:45.1066281Z >>> ) 2024-12-18T00:16:45.1066426Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T00:16:45.1066520Z >>> # Load a checkpoint 2024-12-18T00:16:45.1066617Z >>> model, optim = ... 2024-12-18T00:16:45.1066766Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T00:16:45.1066876Z >>> FSDP.set_state_dict_type( 2024-12-18T00:16:45.1066960Z >>> model, 2024-12-18T00:16:45.1067075Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T00:16:45.1067205Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1067343Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T00:16:45.1067427Z >>> ) 2024-12-18T00:16:45.1067539Z >>> model.load_state_dict(state_dict) 2024-12-18T00:16:45.1067690Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T00:16:45.1067801Z >>> model, optim, optim_state_dict 2024-12-18T00:16:45.1067883Z >>> ) 2024-12-18T00:16:45.1068008Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T00:16:45.1068089Z 2024-12-18T00:16:45.1068174Z Args: 2024-12-18T00:16:45.1068365Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T00:16:45.1068557Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T00:16:45.1068691Z were passed into the optimizer ``optim``. 2024-12-18T00:16:45.1068867Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T00:16:45.1068992Z parameters. 2024-12-18T00:16:45.1069199Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-12-18T00:16:45.1069396Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-12-18T00:16:45.1069586Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-12-18T00:16:45.1069811Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-12-18T00:16:45.1070005Z load_directly (bool): If this is set to True, this API will also 2024-12-18T00:16:45.1070201Z call optim.load_state_dict(result) before returning the result. 2024-12-18T00:16:45.1070420Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-12-18T00:16:45.1070515Z (Default: ``False``) 2024-12-18T00:16:45.1070753Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T00:16:45.1070938Z are sharded or ``None`` if using the default process group. ( 2024-12-18T00:16:45.1071036Z Default: ``None``) 2024-12-18T00:16:45.1071119Z 2024-12-18T00:16:45.1071367Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1071451Z 2024-12-18T00:16:45.1071549Z warnings.warn(msg) 2024-12-18T00:16:45.1071628Z 2024-12-18T00:16:45.1071831Z --- Parse Warning: 60 / 105 --- 2024-12-18T00:16:45.1072834Z /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=137. 2024-12-18T00:16:45.1073098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1073178Z 2024-12-18T00:16:45.1073403Z RemoteModule instance can only be created after RPC initialization. 2024-12-18T00:16:45.1073530Z 2024-12-18T00:16:45.1073931Z It creates a user-specified module on a specified remote node. 2024-12-18T00:16:45.1074168Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T00:16:45.1074273Z executed on the remote node. 2024-12-18T00:16:45.1074510Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T00:16:45.1074666Z gradients back to the corresponding remote module. 2024-12-18T00:16:45.1075025Z It can be shared across processors using `RPC framework `__, 2024-12-18T00:16:45.1075218Z without incurring any overheads of copying the actual module, 2024-12-18T00:16:45.1075413Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-12-18T00:16:45.1075524Z pointing to the remote module. 2024-12-18T00:16:45.1075605Z 2024-12-18T00:16:45.1075806Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-12-18T00:16:45.1076010Z the ``forward`` method of the module returned by the ``module_cls``. 2024-12-18T00:16:45.1076099Z 2024-12-18T00:16:45.1076413Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-12-18T00:16:45.1076501Z 2024-12-18T00:16:45.1076766Z Particularly, to create a hybrid model, typically the local modules should be 2024-12-18T00:16:45.1077148Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-12-18T00:16:45.1077256Z Hybrid Example: 2024-12-18T00:16:45.1077377Z >>> class HybridModel(nn.Module): 2024-12-18T00:16:45.1077506Z >>> def __init__(self) -> None: 2024-12-18T00:16:45.1077622Z >>> nn.Module.__init__(self) 2024-12-18T00:16:45.1077767Z >>> self.remote_embedding = RemoteModule(...) 2024-12-18T00:16:45.1077908Z >>> self.local_linear = nn.Linear(...) 2024-12-18T00:16:45.1078694Z 2024-12-18T00:16:45.1078911Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T00:16:45.1079164Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T00:16:45.1079390Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-12-18T00:16:45.1079525Z ``def forward(input: Tensor) -> Tensor:`` and 2024-12-18T00:16:45.1079791Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-12-18T00:16:45.1079891Z 2024-12-18T00:16:45.1079994Z .. note:: 2024-12-18T00:16:45.1080152Z If the remote module is placed on a cuda device, 2024-12-18T00:16:45.1080389Z any input CPU tensors will be automatically moved to the same cuda device, 2024-12-18T00:16:45.1080790Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-12-18T00:16:45.1080891Z 2024-12-18T00:16:45.1080980Z Args: 2024-12-18T00:16:45.1081291Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T00:16:45.1081583Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T00:16:45.1081687Z formats: 2024-12-18T00:16:45.1081774Z 2024-12-18T00:16:45.1081922Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T00:16:45.1082091Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T00:16:45.1082177Z 2024-12-18T00:16:45.1082438Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T00:16:45.1082560Z module_cls (nn.Module): For example, 2024-12-18T00:16:45.1082685Z >>> class MyModule(nn.Module): 2024-12-18T00:16:45.1082789Z >>> def forward(input): 2024-12-18T00:16:45.1082893Z >>> return input + 1 2024-12-18T00:16:45.1083040Z >>> 2024-12-18T00:16:45.1083148Z >>> module_cls = MyModule 2024-12-18T00:16:45.1083364Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T00:16:45.1083558Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T00:16:45.1083839Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T00:16:45.1084089Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T00:16:45.1084312Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T00:16:45.1084558Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T00:16:45.1084645Z 2024-12-18T00:16:45.1084747Z Returns: 2024-12-18T00:16:45.1084993Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T00:16:45.1085223Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T00:16:45.1085512Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T00:16:45.1085659Z on the user-provided module on the remote side. 2024-12-18T00:16:45.1085761Z 2024-12-18T00:16:45.1085856Z Example:: 2024-12-18T00:16:45.1086026Z Run the following code in two different processes: 2024-12-18T00:16:45.1086113Z 2024-12-18T00:16:45.1086235Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1086346Z >>> # On worker 0: 2024-12-18T00:16:45.1086445Z >>> import torch 2024-12-18T00:16:45.1086587Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1086700Z >>> from torch import nn, Tensor 2024-12-18T00:16:45.1086920Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T00:16:45.1087021Z >>> 2024-12-18T00:16:45.1087165Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T00:16:45.1087334Z >>> remote_linear_module = RemoteModule( 2024-12-18T00:16:45.1087514Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T00:16:45.1087617Z >>> ) 2024-12-18T00:16:45.1087729Z >>> input = torch.randn(128, 20) 2024-12-18T00:16:45.1087885Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T00:16:45.1088001Z >>> ret = ret_fut.wait() 2024-12-18T00:16:45.1088101Z >>> rpc.shutdown() 2024-12-18T00:16:45.1088230Z 2024-12-18T00:16:45.1088351Z >>> # On worker 1: 2024-12-18T00:16:45.1088447Z >>> import torch 2024-12-18T00:16:45.1088591Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1088679Z >>> 2024-12-18T00:16:45.1088833Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T00:16:45.1088934Z >>> rpc.shutdown() 2024-12-18T00:16:45.1089019Z 2024-12-18T00:16:45.1089290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1089378Z 2024-12-18T00:16:45.1089494Z warnings.warn(msg) 2024-12-18T00:16:45.1089580Z 2024-12-18T00:16:45.1089826Z --- Parse Warning: 61 / 105 --- 2024-12-18T00:16:45.1090888Z /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=514. 2024-12-18T00:16:45.1091153Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1091257Z 2024-12-18T00:16:45.1091580Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-12-18T00:16:45.1091684Z 2024-12-18T00:16:45.1092007Z This alternate initialization method can be particularly useful if we want to create multiple 2024-12-18T00:16:45.1092335Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-12-18T00:16:45.1092456Z 2024-12-18T00:16:45.1092734Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-12-18T00:16:45.1092926Z which is not supported. The recommended way is as follows: 2024-12-18T00:16:45.1093013Z 2024-12-18T00:16:45.1093146Z 1. the sender creates a RemoteModule; 2024-12-18T00:16:45.1093295Z 2. the sender sends its ``module_rref`` over RPC; 2024-12-18T00:16:45.1093644Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-12-18T00:16:45.1093732Z 2024-12-18T00:16:45.1093827Z Example:: 2024-12-18T00:16:45.1093995Z Run the following code in two different processes: 2024-12-18T00:16:45.1094078Z 2024-12-18T00:16:45.1094209Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1094304Z >>> # On worker 0: 2024-12-18T00:16:45.1094395Z >>> import torch 2024-12-18T00:16:45.1094536Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1094649Z >>> from torch import nn, Tensor 2024-12-18T00:16:45.1094885Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T00:16:45.1094971Z >>> 2024-12-18T00:16:45.1095127Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T00:16:45.1095242Z >>> remote_module = RemoteModule( 2024-12-18T00:16:45.1095370Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T00:16:45.1095474Z >>> ) 2024-12-18T00:16:45.1095566Z >>> 2024-12-18T00:16:45.1095694Z >>> remote_module1 = rpc.rpc_sync( 2024-12-18T00:16:45.1095793Z >>> "worker1/cpu", 2024-12-18T00:16:45.1095923Z >>> RemoteModule.init_from_module_rref, 2024-12-18T00:16:45.1096100Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-12-18T00:16:45.1096189Z >>> ) 2024-12-18T00:16:45.1096303Z >>> rpc.shutdown() 2024-12-18T00:16:45.1096392Z 2024-12-18T00:16:45.1096488Z >>> # On worker 1: 2024-12-18T00:16:45.1096623Z >>> import torch 2024-12-18T00:16:45.1096754Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1096856Z >>> 2024-12-18T00:16:45.1096996Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T00:16:45.1097110Z >>> rpc.shutdown() 2024-12-18T00:16:45.1097193Z 2024-12-18T00:16:45.1097281Z Args: 2024-12-18T00:16:45.1097643Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T00:16:45.1097934Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T00:16:45.1098030Z formats: 2024-12-18T00:16:45.1098123Z 2024-12-18T00:16:45.1098281Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T00:16:45.1098434Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T00:16:45.1098537Z 2024-12-18T00:16:45.1098786Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T00:16:45.1099051Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-12-18T00:16:45.1099162Z the created remote module. 2024-12-18T00:16:45.1099441Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T00:16:45.1099690Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T00:16:45.1099910Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T00:16:45.1100157Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T00:16:45.1100243Z 2024-12-18T00:16:45.1100343Z Returns: 2024-12-18T00:16:45.1100585Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T00:16:45.1100824Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-12-18T00:16:45.1101142Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T00:16:45.1101291Z on the user-provided module on the remote side. 2024-12-18T00:16:45.1101388Z 2024-12-18T00:16:45.1101640Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1101739Z 2024-12-18T00:16:45.1101843Z warnings.warn(msg) 2024-12-18T00:16:45.1101930Z 2024-12-18T00:16:45.1102147Z --- Parse Warning: 62 / 105 --- 2024-12-18T00:16:45.1103118Z /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=606. 2024-12-18T00:16:45.1103394Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1103489Z 2024-12-18T00:16:45.1103737Z A RemoteModule instance can only be created after RPC initialization. 2024-12-18T00:16:45.1103824Z 2024-12-18T00:16:45.1104021Z It creates a user-specified module on a specified remote node. 2024-12-18T00:16:45.1104270Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T00:16:45.1104379Z executed on the remote node. 2024-12-18T00:16:45.1104632Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T00:16:45.1104793Z gradients back to the corresponding remote module. 2024-12-18T00:16:45.1104879Z 2024-12-18T00:16:45.1105111Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-12-18T00:16:45.1105324Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-12-18T00:16:45.1105586Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-12-18T00:16:45.1105788Z and ``forward`` are the same as the ``forward`` method of the module 2024-12-18T00:16:45.1105939Z returned by the ``module_cls``. 2024-12-18T00:16:45.1106025Z 2024-12-18T00:16:45.1106227Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T00:16:45.1106488Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T00:16:45.1106743Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-12-18T00:16:45.1106866Z 2024-12-18T00:16:45.1106995Z | ``def forward(input: Tensor) -> Tensor:`` 2024-12-18T00:16:45.1107177Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-12-18T00:16:45.1107262Z 2024-12-18T00:16:45.1107350Z Args: 2024-12-18T00:16:45.1107662Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T00:16:45.1108004Z The format should be "/", where the device field can be parsed as torch.device type. 2024-12-18T00:16:45.1108161Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-12-18T00:16:45.1108410Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T00:16:45.1108674Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-12-18T00:16:45.1108762Z 2024-12-18T00:16:45.1108875Z >>> class MyModule(nn.Module): 2024-12-18T00:16:45.1109003Z >>> def forward(input): 2024-12-18T00:16:45.1109106Z >>> return input + 1 2024-12-18T00:16:45.1109205Z >>> 2024-12-18T00:16:45.1109309Z >>> module_cls = MyModule 2024-12-18T00:16:45.1109393Z 2024-12-18T00:16:45.1109609Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T00:16:45.1109804Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T00:16:45.1109900Z 2024-12-18T00:16:45.1110023Z Returns: 2024-12-18T00:16:45.1110281Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T00:16:45.1110516Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T00:16:45.1110785Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T00:16:45.1110941Z on the user-provided module on the remote side. 2024-12-18T00:16:45.1111025Z 2024-12-18T00:16:45.1111134Z Example:: 2024-12-18T00:16:45.1111291Z Run the following code in two different processes: 2024-12-18T00:16:45.1111377Z 2024-12-18T00:16:45.1111508Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1111603Z >>> # On worker 0: 2024-12-18T00:16:45.1111710Z >>> import torch 2024-12-18T00:16:45.1111838Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1111963Z >>> from torch import nn, Tensor 2024-12-18T00:16:45.1112182Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T00:16:45.1112274Z >>> 2024-12-18T00:16:45.1112430Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T00:16:45.1112552Z >>> remote_linear_module = RemoteModule( 2024-12-18T00:16:45.1112691Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T00:16:45.1112779Z >>> ) 2024-12-18T00:16:45.1112888Z >>> input = torch.randn(128, 20) 2024-12-18T00:16:45.1113061Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T00:16:45.1113166Z >>> ret = ret_fut.wait() 2024-12-18T00:16:45.1113284Z >>> rpc.shutdown() 2024-12-18T00:16:45.1113373Z 2024-12-18T00:16:45.1113479Z >>> # On worker 1: 2024-12-18T00:16:45.1113574Z >>> import torch 2024-12-18T00:16:45.1113706Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1113806Z >>> 2024-12-18T00:16:45.1113979Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T00:16:45.1114092Z >>> rpc.shutdown() 2024-12-18T00:16:45.1114206Z 2024-12-18T00:16:45.1114400Z Furthermore, a more practical example that is combined with 2024-12-18T00:16:45.1114888Z `DistributedDataParallel `__ (DDP) 2024-12-18T00:16:45.1115221Z can be found in this `tutorial `__. 2024-12-18T00:16:45.1115360Z 2024-12-18T00:16:45.1115643Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1115739Z 2024-12-18T00:16:45.1115839Z warnings.warn(msg) 2024-12-18T00:16:45.1115923Z 2024-12-18T00:16:45.1116132Z --- Parse Warning: 63 / 105 --- 2024-12-18T00:16:45.1117125Z /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. 2024-12-18T00:16:45.1117403Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1117488Z 2024-12-18T00:16:45.1117743Z DistributedOptimizer takes remote references to parameters scattered 2024-12-18T00:16:45.1117985Z across workers and applies the given optimizer locally for each parameter. 2024-12-18T00:16:45.1118073Z 2024-12-18T00:16:45.1118328Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-12-18T00:16:45.1118578Z to retrieve the gradients for specific parameters. 2024-12-18T00:16:45.1118686Z 2024-12-18T00:16:45.1118788Z Concurrent calls to 2024-12-18T00:16:45.1118997Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-12-18T00:16:45.1119158Z either from the same or different clients, will 2024-12-18T00:16:45.1119385Z be serialized on each worker -- as each worker's optimizer can only work 2024-12-18T00:16:45.1119642Z on one set of gradients at a time. However, there is no guarantee that 2024-12-18T00:16:45.1119892Z the full forward-backward-optimizer sequence will execute for one client 2024-12-18T00:16:45.1120120Z at a time. This means that the gradients being applied may not correspond 2024-12-18T00:16:45.1120346Z to the latest forward pass executed on a given worker. Also, there is no 2024-12-18T00:16:45.1120474Z guaranteed ordering across workers. 2024-12-18T00:16:45.1120564Z 2024-12-18T00:16:45.1120830Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-12-18T00:16:45.1121074Z by default, so that optimizer updates are not blocked by the Python Global 2024-12-18T00:16:45.1121318Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-12-18T00:16:45.1121575Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-12-18T00:16:45.1121830Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-12-18T00:16:45.1121944Z for your own custom optimizers. 2024-12-18T00:16:45.1122044Z 2024-12-18T00:16:45.1122143Z Args: 2024-12-18T00:16:45.1122341Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-12-18T00:16:45.1122465Z instantiate on each worker. 2024-12-18T00:16:45.1122673Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-12-18T00:16:45.1122784Z to optimize. 2024-12-18T00:16:45.1122999Z args: arguments to pass to the optimizer constructor on each worker. 2024-12-18T00:16:45.1123220Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-12-18T00:16:45.1123318Z 2024-12-18T00:16:45.1123414Z Example:: 2024-12-18T00:16:45.1123546Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1123710Z >>> import torch.distributed.autograd as dist_autograd 2024-12-18T00:16:45.1123849Z >>> import torch.distributed.rpc as rpc 2024-12-18T00:16:45.1123955Z >>> from torch import optim 2024-12-18T00:16:45.1124173Z >>> from torch.distributed.optim import DistributedOptimizer 2024-12-18T00:16:45.1124273Z >>> 2024-12-18T00:16:45.1124407Z >>> with dist_autograd.context() as context_id: 2024-12-18T00:16:45.1124517Z >>> # Forward pass. 2024-12-18T00:16:45.1124715Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-12-18T00:16:45.1124959Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-12-18T00:16:45.1125104Z >>> loss = rref1.to_here() + rref2.to_here() 2024-12-18T00:16:45.1125190Z >>> 2024-12-18T00:16:45.1125305Z >>> # Backward pass. 2024-12-18T00:16:45.1125456Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-12-18T00:16:45.1125552Z >>> 2024-12-18T00:16:45.1125647Z >>> # Optimizer. 2024-12-18T00:16:45.1125773Z >>> dist_optim = DistributedOptimizer( 2024-12-18T00:16:45.1125884Z >>> optim.SGD, 2024-12-18T00:16:45.1125984Z >>> [rref1, rref2], 2024-12-18T00:16:45.1126089Z >>> lr=0.05, 2024-12-18T00:16:45.1126176Z >>> ) 2024-12-18T00:16:45.1126289Z >>> dist_optim.step(context_id) 2024-12-18T00:16:45.1126388Z 2024-12-18T00:16:45.1126546Z __ https://github.com/pytorch/tutorials/pull/1465 2024-12-18T00:16:45.1126642Z 2024-12-18T00:16:45.1126895Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1126990Z 2024-12-18T00:16:45.1127088Z warnings.warn(msg) 2024-12-18T00:16:45.1127170Z 2024-12-18T00:16:45.1127389Z --- Parse Warning: 64 / 105 --- 2024-12-18T00:16:45.1128447Z /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. 2024-12-18T00:16:45.1128756Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1128840Z 2024-12-18T00:16:45.1129241Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-12-18T00:16:45.1129392Z This optimizer runs local optimizer at every step. 2024-12-18T00:16:45.1129726Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-12-18T00:16:45.1129829Z 2024-12-18T00:16:45.1129913Z Args: 2024-12-18T00:16:45.1130032Z optim: The local optimizer. 2024-12-18T00:16:45.1130248Z averager: A model averager instance to run post-localSGD algorithm. 2024-12-18T00:16:45.1130331Z 2024-12-18T00:16:45.1130435Z Example:: 2024-12-18T00:16:45.1130517Z 2024-12-18T00:16:45.1130660Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1130756Z >>> import torch 2024-12-18T00:16:45.1130889Z >>> import torch.distributed as dist 2024-12-18T00:16:45.1131161Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T00:16:45.1131268Z >>> import torch.nn as nn 2024-12-18T00:16:45.1131473Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-12-18T00:16:45.1131744Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T00:16:45.1131862Z >>> PostLocalSGDState, 2024-12-18T00:16:45.1131961Z >>> post_localSGD_hook, 2024-12-18T00:16:45.1132046Z >>> ) 2024-12-18T00:16:45.1132143Z >>> 2024-12-18T00:16:45.1132296Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T00:16:45.1132450Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T00:16:45.1132535Z >>> ) 2024-12-18T00:16:45.1132631Z >>> 2024-12-18T00:16:45.1132779Z >>> # Register a post-localSGD communication hook. 2024-12-18T00:16:45.1133076Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T00:16:45.1133278Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T00:16:45.1133361Z >>> 2024-12-18T00:16:45.1133578Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-12-18T00:16:45.1133851Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-12-18T00:16:45.1134059Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T00:16:45.1134270Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-12-18T00:16:45.1134389Z >>> opt = PostLocalSGDOptimizer( 2024-12-18T00:16:45.1134504Z >>> optim=local_optim, 2024-12-18T00:16:45.1134747Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T00:16:45.1134845Z >>> ) 2024-12-18T00:16:45.1134930Z >>> 2024-12-18T00:16:45.1135154Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-12-18T00:16:45.1135470Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-12-18T00:16:45.1135841Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-12-18T00:16:45.1135962Z >>> for step in range(0, 200): 2024-12-18T00:16:45.1136061Z >>> opt.zero_grad() 2024-12-18T00:16:45.1136185Z >>> loss = loss_fn(output, labels) 2024-12-18T00:16:45.1136286Z >>> loss.backward() 2024-12-18T00:16:45.1136381Z >>> opt.step() 2024-12-18T00:16:45.1136475Z 2024-12-18T00:16:45.1136729Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1136827Z 2024-12-18T00:16:45.1136923Z warnings.warn(msg) 2024-12-18T00:16:45.1137007Z 2024-12-18T00:16:45.1137243Z --- Parse Warning: 65 / 105 --- 2024-12-18T00:16:45.1138332Z /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=282. 2024-12-18T00:16:45.1138607Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1138693Z 2024-12-18T00:16:45.1139113Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-12-18T00:16:45.1139198Z 2024-12-18T00:16:45.1139341Z The sharing is done as described by ZeRO_. 2024-12-18T00:16:45.1139425Z 2024-12-18T00:16:45.1139575Z The local optimizer instance in each rank is only 2024-12-18T00:16:45.1139834Z responsible for updating approximately ``1 / world_size`` parameters and 2024-12-18T00:16:45.1140039Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-12-18T00:16:45.1140301Z parameters are updated locally, each rank will broadcast its parameters to 2024-12-18T00:16:45.1140487Z all other peers to keep all model replicas in the same state. 2024-12-18T00:16:45.1140679Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-12-18T00:16:45.1140947Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-12-18T00:16:45.1141048Z memory consumption. 2024-12-18T00:16:45.1141145Z 2024-12-18T00:16:45.1141399Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-12-18T00:16:45.1141637Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-12-18T00:16:45.1141877Z not divided among ranks. The partition is arbitrary and might not match the 2024-12-18T00:16:45.1142005Z the parameter registration or usage order. 2024-12-18T00:16:45.1142099Z 2024-12-18T00:16:45.1142190Z Arguments: 2024-12-18T00:16:45.1142399Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-12-18T00:16:45.1142609Z or :class:`dict` s giving all parameters, which will be sharded 2024-12-18T00:16:45.1142715Z across ranks. 2024-12-18T00:16:45.1142801Z 2024-12-18T00:16:45.1142892Z Keyword Args: 2024-12-18T00:16:45.1143126Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-12-18T00:16:45.1143243Z optimizer. 2024-12-18T00:16:45.1143487Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-12-18T00:16:45.1143683Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-12-18T00:16:45.1143832Z :meth:`torch.distributed.init_process_group`). 2024-12-18T00:16:45.1144070Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-12-18T00:16:45.1144278Z packed into buckets to speed up communication, and ``param.data`` 2024-12-18T00:16:45.1144492Z fields point to bucket views at different offsets; if ``False``, 2024-12-18T00:16:45.1144695Z each individual parameter is communicated separately, and each 2024-12-18T00:16:45.1144861Z ``params.data`` stays intact (default: ``False``). 2024-12-18T00:16:45.1145055Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-12-18T00:16:45.1145265Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-12-18T00:16:45.1145480Z synchronization; this requires (1) either a functional optimizer 2024-12-18T00:16:45.1145658Z for the ``optimizer_class`` argument or one with a functional 2024-12-18T00:16:45.1145850Z equivalent and (2) registering a DDP communication hook 2024-12-18T00:16:45.1146050Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-12-18T00:16:45.1146226Z parameters are packed into buckets matching those in 2024-12-18T00:16:45.1146414Z :class:`DistributedDataParallel`, meaning that the 2024-12-18T00:16:45.1146575Z ``parameters_as_bucket_view`` argument is ignored. 2024-12-18T00:16:45.1146764Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-12-18T00:16:45.1146858Z (per normal). 2024-12-18T00:16:45.1146973Z (default: ``False``) 2024-12-18T00:16:45.1147183Z **defaults: any trailing arguments, which are forwarded to the local 2024-12-18T00:16:45.1147285Z optimizer. 2024-12-18T00:16:45.1147370Z 2024-12-18T00:16:45.1147461Z Example:: 2024-12-18T00:16:45.1147560Z 2024-12-18T00:16:45.1147658Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1147776Z >>> import torch.nn as nn 2024-12-18T00:16:45.1147979Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-12-18T00:16:45.1148189Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-12-18T00:16:45.1148419Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-12-18T00:16:45.1148541Z >>> ddp = DDP(model, device_ids=[rank]) 2024-12-18T00:16:45.1148676Z >>> opt = ZeroRedundancyOptimizer( 2024-12-18T00:16:45.1148779Z >>> ddp.parameters(), 2024-12-18T00:16:45.1148914Z >>> optimizer_class=torch.optim.Adam, 2024-12-18T00:16:45.1149007Z >>> lr=0.01 2024-12-18T00:16:45.1149091Z >>> ) 2024-12-18T00:16:45.1149217Z >>> ddp(inputs).sum().backward() 2024-12-18T00:16:45.1149310Z >>> opt.step() 2024-12-18T00:16:45.1149404Z 2024-12-18T00:16:45.1149494Z .. warning:: 2024-12-18T00:16:45.1149702Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-12-18T00:16:45.1149861Z passed-in parameters are the same dense type. 2024-12-18T00:16:45.1149943Z 2024-12-18T00:16:45.1150044Z .. warning:: 2024-12-18T00:16:45.1150258Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-12-18T00:16:45.1150470Z the way that overlapping :class:`DistributedDataParallel` with 2024-12-18T00:16:45.1150752Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-12-18T00:16:45.1150969Z two or three training iterations do not perform parameter updates in 2024-12-18T00:16:45.1151169Z the optimizer step, depending on if ``static_graph=False`` or 2024-12-18T00:16:45.1151376Z ``static_graph=True``, respectively. This is because it needs 2024-12-18T00:16:45.1151599Z information about the gradient bucketing strategy used by 2024-12-18T00:16:45.1151820Z :class:`DistributedDataParallel`, which is not finalized until the 2024-12-18T00:16:45.1152028Z second forward pass if ``static_graph=False`` or until the third 2024-12-18T00:16:45.1152238Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-12-18T00:16:45.1152345Z is to prepend dummy inputs. 2024-12-18T00:16:45.1152446Z 2024-12-18T00:16:45.1152702Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-12-18T00:16:45.1152798Z 2024-12-18T00:16:45.1152931Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-12-18T00:16:45.1153023Z 2024-12-18T00:16:45.1153108Z 2024-12-18T00:16:45.1153361Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1153455Z 2024-12-18T00:16:45.1153554Z warnings.warn(msg) 2024-12-18T00:16:45.1153651Z 2024-12-18T00:16:45.1153849Z --- Parse Warning: 66 / 105 --- 2024-12-18T00:16:45.1154833Z /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. 2024-12-18T00:16:45.1155111Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1155196Z 2024-12-18T00:16:45.1155466Z Custom reducer class that can be used to specify a custom operation that 2024-12-18T00:16:45.1155645Z reduces losses of multiple microbatches into one value. 2024-12-18T00:16:45.1155740Z 2024-12-18T00:16:45.1155832Z Example: 2024-12-18T00:16:45.1155933Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1156059Z >>> sum_reducer = _CustomReducer( 2024-12-18T00:16:45.1156160Z >>> torch.tensor(0.0), 2024-12-18T00:16:45.1156265Z >>> lambda a, b: a + b 2024-12-18T00:16:45.1156354Z >>> ) 2024-12-18T00:16:45.1156438Z 2024-12-18T00:16:45.1156704Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1156787Z 2024-12-18T00:16:45.1156903Z warnings.warn(msg) 2024-12-18T00:16:45.1156988Z 2024-12-18T00:16:45.1157187Z --- Parse Warning: 67 / 105 --- 2024-12-18T00:16:45.1158109Z /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. 2024-12-18T00:16:45.1158467Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1158569Z 2024-12-18T00:16:45.1158808Z A decorator for a function indicating that the return value of the function 2024-12-18T00:16:45.1159029Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-12-18T00:16:45.1159272Z function can run asynchronously on the RPC callee. More specifically, the 2024-12-18T00:16:45.1159518Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-12-18T00:16:45.1159752Z function and installs subsequent processing steps as a callback to that 2024-12-18T00:16:45.1159987Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-12-18T00:16:45.1160202Z from the :class:`~torch.futures.Future` when completed and send the 2024-12-18T00:16:45.1160387Z value back as the RPC response. That also means the returned 2024-12-18T00:16:45.1160659Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-12-18T00:16:45.1160883Z sent through RPC. This decorator is useful when the wrapped function's 2024-12-18T00:16:45.1161095Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-12-18T00:16:45.1161346Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-12-18T00:16:45.1161452Z 2024-12-18T00:16:45.1161682Z .. note:: To enable asynchronous execution, applications must pass the 2024-12-18T00:16:45.1161912Z function object returned by this decorator to RPC APIs. If RPC detected 2024-12-18T00:16:45.1162139Z attributes installed by this decorator, it knows that this function 2024-12-18T00:16:45.1162325Z returns a ``Future`` object and will handle that accordingly. 2024-12-18T00:16:45.1162548Z However, this does not mean this decorator has to be outmost one when 2024-12-18T00:16:45.1162778Z defining a function. For example, when combined with ``@staticmethod`` 2024-12-18T00:16:45.1163001Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-12-18T00:16:45.1163225Z inner decorator to allow the target function be recognized as a static 2024-12-18T00:16:45.1163456Z or class function. This target function can still execute asynchronously 2024-12-18T00:16:45.1163695Z because, when accessed, the static or class method preserves attributes 2024-12-18T00:16:45.1163852Z installed by ``@rpc.functions.async_execution``. 2024-12-18T00:16:45.1163946Z 2024-12-18T00:16:45.1164032Z 2024-12-18T00:16:45.1164125Z Example:: 2024-12-18T00:16:45.1164336Z The returned :class:`~torch.futures.Future` object can come from 2024-12-18T00:16:45.1164474Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-12-18T00:16:45.1164708Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-12-18T00:16:45.1164991Z constructor. The example below shows directly using the 2024-12-18T00:16:45.1165136Z :class:`~torch.futures.Future` returned by 2024-12-18T00:16:45.1165261Z :meth:`~torch.futures.Future.then`. 2024-12-18T00:16:45.1165350Z 2024-12-18T00:16:45.1165487Z >>> from torch.distributed import rpc 2024-12-18T00:16:45.1165578Z >>> 2024-12-18T00:16:45.1165707Z >>> # omitting setup and shutdown RPC 2024-12-18T00:16:45.1165800Z >>> 2024-12-18T00:16:45.1165901Z >>> # On all workers 2024-12-18T00:16:45.1166025Z >>> @rpc.functions.async_execution 2024-12-18T00:16:45.1166143Z >>> def async_add_chained(to, x, y, z): 2024-12-18T00:16:45.1166347Z >>> # This function runs on "worker1" and returns immediately when 2024-12-18T00:16:45.1166540Z >>> # the callback is installed through the `then(cb)` API. In the 2024-12-18T00:16:45.1166734Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-12-18T00:16:45.1166901Z >>> # When the return value of that `rpc_async` arrives at 2024-12-18T00:16:45.1167092Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-12-18T00:16:45.1167289Z >>> # and set the value for the previously returned `Future`, which 2024-12-18T00:16:45.1167476Z >>> # will then trigger RPC to send the result back to "worker0". 2024-12-18T00:16:45.1167654Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:45.1167772Z >>> lambda fut: fut.wait() + z 2024-12-18T00:16:45.1167868Z >>> ) 2024-12-18T00:16:45.1167955Z >>> 2024-12-18T00:16:45.1168048Z >>> # On worker0 2024-12-18T00:16:45.1168154Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1168256Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:45.1168359Z >>> "worker1", 2024-12-18T00:16:45.1168458Z >>> async_add_chained, 2024-12-18T00:16:45.1168584Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-12-18T00:16:45.1168705Z >>> ) 2024-12-18T00:16:45.1168827Z >>> print(ret) # prints tensor([3., 3.]) 2024-12-18T00:16:45.1168920Z 2024-12-18T00:16:45.1169147Z When combined with TorchScript decorators, this decorator must be the 2024-12-18T00:16:45.1169248Z outmost one. 2024-12-18T00:16:45.1169333Z 2024-12-18T00:16:45.1169463Z >>> from torch import Tensor 2024-12-18T00:16:45.1169614Z >>> from torch.futures import Future 2024-12-18T00:16:45.1169737Z >>> from torch.distributed import rpc 2024-12-18T00:16:45.1169830Z >>> 2024-12-18T00:16:45.1169947Z >>> # omitting setup and shutdown RPC 2024-12-18T00:16:45.1170033Z >>> 2024-12-18T00:16:45.1170139Z >>> # On all workers 2024-12-18T00:16:45.1170240Z >>> @torch.jit.script 2024-12-18T00:16:45.1170396Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-12-18T00:16:45.1170494Z >>> return x + y 2024-12-18T00:16:45.1170579Z >>> 2024-12-18T00:16:45.1170703Z >>> @rpc.functions.async_execution 2024-12-18T00:16:45.1170801Z >>> @torch.jit.script 2024-12-18T00:16:45.1170995Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-12-18T00:16:45.1171140Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-12-18T00:16:45.1171233Z >>> 2024-12-18T00:16:45.1171327Z >>> # On worker0 2024-12-18T00:16:45.1171429Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:45.1171530Z >>> "worker1", 2024-12-18T00:16:45.1171622Z >>> async_add, 2024-12-18T00:16:45.1171751Z >>> args=("worker2", torch.ones(2), 1) 2024-12-18T00:16:45.1171838Z >>> ) 2024-12-18T00:16:45.1171958Z >>> print(ret) # prints tensor([2., 2.]) 2024-12-18T00:16:45.1172050Z 2024-12-18T00:16:45.1172269Z When combined with static or class method, this decorator must be the 2024-12-18T00:16:45.1172397Z inner one. 2024-12-18T00:16:45.1172482Z 2024-12-18T00:16:45.1172601Z >>> from torch.distributed import rpc 2024-12-18T00:16:45.1172698Z >>> 2024-12-18T00:16:45.1172812Z >>> # omitting setup and shutdown RPC 2024-12-18T00:16:45.1172911Z >>> 2024-12-18T00:16:45.1173007Z >>> # On all workers 2024-12-18T00:16:45.1173130Z >>> class AsyncExecutionClass: 2024-12-18T00:16:45.1173220Z >>> 2024-12-18T00:16:45.1173315Z >>> @staticmethod 2024-12-18T00:16:45.1173450Z >>> @rpc.functions.async_execution 2024-12-18T00:16:45.1173789Z >>> def static_async_add(to, x, y, z): 2024-12-18T00:16:45.1173984Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:45.1174101Z >>> lambda fut: fut.wait() + z 2024-12-18T00:16:45.1174188Z >>> ) 2024-12-18T00:16:45.1174289Z >>> 2024-12-18T00:16:45.1174384Z >>> @classmethod 2024-12-18T00:16:45.1174520Z >>> @rpc.functions.async_execution 2024-12-18T00:16:45.1174647Z >>> def class_async_add(cls, to, x, y, z): 2024-12-18T00:16:45.1174784Z >>> ret_fut = torch.futures.Future() 2024-12-18T00:16:45.1174934Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:45.1175084Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-12-18T00:16:45.1175189Z >>> ) 2024-12-18T00:16:45.1175288Z >>> return ret_fut 2024-12-18T00:16:45.1175386Z >>> 2024-12-18T00:16:45.1175507Z >>> @rpc.functions.async_execution 2024-12-18T00:16:45.1175633Z >>> def bound_async_add(self, to, x, y, z): 2024-12-18T00:16:45.1175813Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T00:16:45.1175927Z >>> lambda fut: fut.wait() + z 2024-12-18T00:16:45.1176025Z >>> ) 2024-12-18T00:16:45.1176114Z >>> 2024-12-18T00:16:45.1176287Z >>> # On worker0 2024-12-18T00:16:45.1176386Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:45.1176477Z >>> "worker1", 2024-12-18T00:16:45.1176627Z >>> AsyncExecutionClass.static_async_add, 2024-12-18T00:16:45.1176747Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T00:16:45.1176846Z >>> ) 2024-12-18T00:16:45.1177002Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:45.1177086Z >>> 2024-12-18T00:16:45.1177232Z >>> ret = rpc.rpc_sync( 2024-12-18T00:16:45.1177323Z >>> "worker1", 2024-12-18T00:16:45.1177467Z >>> AsyncExecutionClass.class_async_add, 2024-12-18T00:16:45.1177583Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T00:16:45.1177677Z >>> ) 2024-12-18T00:16:45.1177798Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:45.1177881Z 2024-12-18T00:16:45.1178058Z This decorator also works with RRef helpers, i.e., . 2024-12-18T00:16:45.1178204Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-12-18T00:16:45.1178368Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-12-18T00:16:45.1178509Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-12-18T00:16:45.1178593Z 2024-12-18T00:16:45.1178729Z >>> from torch.distributed import rpc 2024-12-18T00:16:45.1178813Z >>> 2024-12-18T00:16:45.1178963Z >>> # reuse the AsyncExecutionClass class above 2024-12-18T00:16:45.1179116Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T00:16:45.1179337Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-12-18T00:16:45.1179457Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:45.1179542Z >>> 2024-12-18T00:16:45.1179705Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T00:16:45.1179935Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-12-18T00:16:45.1180129Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:45.1180222Z >>> 2024-12-18T00:16:45.1180376Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T00:16:45.1180628Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-12-18T00:16:45.1180749Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T00:16:45.1180850Z 2024-12-18T00:16:45.1181111Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1181211Z 2024-12-18T00:16:45.1181313Z warnings.warn(msg) 2024-12-18T00:16:45.1181398Z 2024-12-18T00:16:45.1181645Z --- Parse Warning: 68 / 105 --- 2024-12-18T00:16:45.1182713Z /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. 2024-12-18T00:16:45.1182993Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1183081Z 2024-12-18T00:16:45.1183299Z Set device mapping between each RPC caller and callee pair. This 2024-12-18T00:16:45.1183487Z function can be called multiple times to incrementally add 2024-12-18T00:16:45.1183601Z device placement configurations. 2024-12-18T00:16:45.1183702Z 2024-12-18T00:16:45.1183788Z Args: 2024-12-18T00:16:45.1183907Z to (str): Callee name. 2024-12-18T00:16:45.1184110Z device_map (Dict of int, str, or torch.device): Device placement 2024-12-18T00:16:45.1184294Z mappings from this worker to the callee. This map must be 2024-12-18T00:16:45.1184404Z invertible. 2024-12-18T00:16:45.1184489Z 2024-12-18T00:16:45.1184594Z Example: 2024-12-18T00:16:45.1184715Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1184824Z >>> # both workers 2024-12-18T00:16:45.1184919Z >>> def add(x, y): 2024-12-18T00:16:45.1185084Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-12-18T00:16:45.1185207Z >>> return x + y, (x + y).to(2) 2024-12-18T00:16:45.1185299Z >>> 2024-12-18T00:16:45.1185406Z >>> # on worker 0 2024-12-18T00:16:45.1185545Z >>> options = TensorPipeRpcBackendOptions( 2024-12-18T00:16:45.1185677Z >>> num_worker_threads=8, 2024-12-18T00:16:45.1185830Z >>> device_maps={"worker1": {0: 1}} 2024-12-18T00:16:45.1185967Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-12-18T00:16:45.1186068Z >>> ) 2024-12-18T00:16:45.1186200Z >>> options.set_device_map("worker1", {1: 2}) 2024-12-18T00:16:45.1186343Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-12-18T00:16:45.1186431Z >>> 2024-12-18T00:16:45.1186527Z >>> rpc.init_rpc( 2024-12-18T00:16:45.1186634Z >>> "worker0", 2024-12-18T00:16:45.1186724Z >>> rank=0, 2024-12-18T00:16:45.1186838Z >>> world_size=2, 2024-12-18T00:16:45.1186969Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-12-18T00:16:45.1187081Z >>> rpc_backend_options=options 2024-12-18T00:16:45.1187179Z >>> ) 2024-12-18T00:16:45.1187266Z >>> 2024-12-18T00:16:45.1187376Z >>> x = torch.ones(2) 2024-12-18T00:16:45.1187541Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-12-18T00:16:45.1187729Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-12-18T00:16:45.1187925Z >>> # sending the return value back, it will follow the invert of 2024-12-18T00:16:45.1188097Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-12-18T00:16:45.1188207Z >>> # cuda:1 on worker0 2024-12-18T00:16:45.1188352Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-12-18T00:16:45.1188510Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-12-18T00:16:45.1188625Z 2024-12-18T00:16:45.1188879Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1188978Z 2024-12-18T00:16:45.1189076Z warnings.warn(msg) 2024-12-18T00:16:45.1189172Z 2024-12-18T00:16:45.1189364Z --- Parse Warning: 69 / 105 --- 2024-12-18T00:16:45.1190372Z /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=32. 2024-12-18T00:16:45.1190636Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1190720Z 2024-12-18T00:16:45.1190998Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2024-12-18T00:16:45.1191277Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2024-12-18T00:16:45.1191557Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2024-12-18T00:16:45.1191721Z :class:`DTensor` according to the ``out_placements``. 2024-12-18T00:16:45.1191824Z 2024-12-18T00:16:45.1191915Z Args: 2024-12-18T00:16:45.1192125Z func (Callable): the function to be applied on each local shard of 2024-12-18T00:16:45.1192238Z :class:`DTensor` s. 2024-12-18T00:16:45.1192468Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-12-18T00:16:45.1192739Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2024-12-18T00:16:45.1192982Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-12-18T00:16:45.1193237Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-12-18T00:16:45.1193486Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-12-18T00:16:45.1193610Z mapping to the flattened ``output``. 2024-12-18T00:16:45.1193855Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-12-18T00:16:45.1194126Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2024-12-18T00:16:45.1194236Z should be `None`. 2024-12-18T00:16:45.1194500Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-12-18T00:16:45.1194761Z in. In this case, even if `out_placements` is not `None`, the result function 2024-12-18T00:16:45.1195019Z should ignore the desired placements because the function is not running with 2024-12-18T00:16:45.1195120Z :class:`DTensor` s. 2024-12-18T00:16:45.1195301Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-12-18T00:16:45.1195578Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2024-12-18T00:16:45.1195827Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2024-12-18T00:16:45.1196059Z placements of each :class:`DTensor` argument is the same as the required 2024-12-18T00:16:45.1196254Z placements or not. If the placements are not the same and 2024-12-18T00:16:45.1196502Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-12-18T00:16:45.1196761Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2024-12-18T00:16:45.1197021Z the required sharding placements before passing its local tensor to ``func``. 2024-12-18T00:16:45.1197247Z The only exception is when required placements are not ``None`` and the 2024-12-18T00:16:45.1197503Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-12-18T00:16:45.1197724Z will be skipped and the argument will be directly passed to ``func``. 2024-12-18T00:16:45.1197989Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-12-18T00:16:45.1198089Z Default: None 2024-12-18T00:16:45.1198240Z device_mesh (:class:`DeviceMesh`, optional): 2024-12-18T00:16:45.1198538Z the device mesh that all the :class:`DTensor` s are placed on. If not 2024-12-18T00:16:45.1198779Z specified, this will be inferred from the input :class:`DTensor` s' device 2024-12-18T00:16:45.1199031Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2024-12-18T00:16:45.1199142Z device mesh. Default: None. 2024-12-18T00:16:45.1199278Z redistribute_inputs (bool, optional): 2024-12-18T00:16:45.1199534Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2024-12-18T00:16:45.1199793Z their placements are different from the required input placements. If this 2024-12-18T00:16:45.1200028Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-12-18T00:16:45.1200169Z an exception will be raised. Default: False. 2024-12-18T00:16:45.1200268Z 2024-12-18T00:16:45.1200357Z Returns: 2024-12-18T00:16:45.1200625Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-12-18T00:16:45.1200866Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-12-18T00:16:45.1200960Z 2024-12-18T00:16:45.1201052Z Raises: 2024-12-18T00:16:45.1201303Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2024-12-18T00:16:45.1201557Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-12-18T00:16:45.1201658Z argument passed in. 2024-12-18T00:16:45.1201755Z 2024-12-18T00:16:45.1201997Z AssertionError: For any non-DTensor output, we require its corresponding 2024-12-18T00:16:45.1202258Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-12-18T00:16:45.1202402Z if this is not the case. 2024-12-18T00:16:45.1202487Z 2024-12-18T00:16:45.1202761Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-12-18T00:16:45.1202914Z a redistribution according to ``in_placements``. 2024-12-18T00:16:45.1203013Z 2024-12-18T00:16:45.1203129Z Example: 2024-12-18T00:16:45.1203273Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1203425Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-12-18T00:16:45.1203549Z >>> partial_sum_tensor = torch.mm(W, X) 2024-12-18T00:16:45.1203805Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-12-18T00:16:45.1203913Z >>> return reduced_tensor 2024-12-18T00:16:45.1204004Z >>> 2024-12-18T00:16:45.1204141Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-12-18T00:16:45.1204273Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-12-18T00:16:45.1204392Z >>> Y = torch.mm(W, X) 2024-12-18T00:16:45.1204583Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-12-18T00:16:45.1204786Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-12-18T00:16:45.1204872Z >>> 2024-12-18T00:16:45.1205146Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-12-18T00:16:45.1205284Z >>> local_mm_allreduce_forward = local_map( 2024-12-18T00:16:45.1205393Z >>> mm_allreduce_forward, 2024-12-18T00:16:45.1205525Z >>> out_placements=[Replicate()], 2024-12-18T00:16:45.1205647Z >>> in_placements=[col_wise, row_wise], 2024-12-18T00:16:45.1205767Z >>> device_mesh=device_mesh, 2024-12-18T00:16:45.1205853Z >>> ) 2024-12-18T00:16:45.1205938Z >>> 2024-12-18T00:16:45.1206246Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-12-18T00:16:45.1206502Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-12-18T00:16:45.1206845Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-12-18T00:16:45.1206930Z 2024-12-18T00:16:45.1207150Z .. note:: This API is currently experimental and subject to change 2024-12-18T00:16:45.1207239Z 2024-12-18T00:16:45.1207495Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1207590Z 2024-12-18T00:16:45.1207688Z warnings.warn(msg) 2024-12-18T00:16:45.1207786Z 2024-12-18T00:16:45.1207996Z --- Parse Warning: 70 / 105 --- 2024-12-18T00:16:45.1209072Z /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=25. 2024-12-18T00:16:45.1209354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1209437Z 2024-12-18T00:16:45.1209724Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2024-12-18T00:16:45.1209966Z strategies for an operator when the tensor inputs and outputs are DTensor. 2024-12-18T00:16:45.1210238Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-12-18T00:16:45.1210483Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-12-18T00:16:45.1210770Z when users would like to overwrite default sharding strategies of existing operators. 2024-12-18T00:16:45.1210853Z 2024-12-18T00:16:45.1210938Z Args: 2024-12-18T00:16:45.1211082Z op (Union[OpOverload, List[OpOverload]]): 2024-12-18T00:16:45.1211280Z An op or a list of ops to register the customized sharding function. 2024-12-18T00:16:45.1211406Z 2024-12-18T00:16:45.1211495Z Returns: 2024-12-18T00:16:45.1211765Z A function decorator which can be used to wrap a function that defines the sharding 2024-12-18T00:16:45.1212048Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-12-18T00:16:45.1212391Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-12-18T00:16:45.1212706Z already implemented the operator. The customized sharding function takes the same inputs 2024-12-18T00:16:45.1212946Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-12-18T00:16:45.1213227Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-12-18T00:16:45.1213501Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-12-18T00:16:45.1213640Z corresponding intput placements. 2024-12-18T00:16:45.1213727Z 2024-12-18T00:16:45.1213821Z Example: 2024-12-18T00:16:45.1213956Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1214095Z >>> @register_sharding(aten._softmax.default) 2024-12-18T00:16:45.1214269Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-12-18T00:16:45.1214417Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-12-18T00:16:45.1214535Z >>> acceptable_shardings = [] 2024-12-18T00:16:45.1214638Z >>> 2024-12-18T00:16:45.1214819Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-12-18T00:16:45.1214978Z >>> acceptable_shardings.append(all_replicate) 2024-12-18T00:16:45.1215066Z >>> 2024-12-18T00:16:45.1215203Z >>> for sharding_dim in range(x.ndim): 2024-12-18T00:16:45.1215321Z >>> if sharding_dim != softmax_dim: 2024-12-18T00:16:45.1215455Z >>> all_sharded = ( 2024-12-18T00:16:45.1215584Z >>> [Shard(sharding_dim)], 2024-12-18T00:16:45.1215710Z >>> [Shard(sharding_dim), None, None], 2024-12-18T00:16:45.1215819Z >>> ) 2024-12-18T00:16:45.1215960Z >>> acceptable_shardings.append(all_sharded) 2024-12-18T00:16:45.1216052Z >>> 2024-12-18T00:16:45.1216179Z >>> return acceptable_shardings 2024-12-18T00:16:45.1216266Z 2024-12-18T00:16:45.1216485Z .. note:: This API is currently experimental and subject to change 2024-12-18T00:16:45.1216571Z 2024-12-18T00:16:45.1216844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1216931Z 2024-12-18T00:16:45.1217032Z warnings.warn(msg) 2024-12-18T00:16:45.1217131Z 2024-12-18T00:16:45.1217323Z --- Parse Warning: 71 / 105 --- 2024-12-18T00:16:45.1218352Z /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=378. 2024-12-18T00:16:45.1218622Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1218720Z 2024-12-18T00:16:45.1219103Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-12-18T00:16:45.1219428Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-12-18T00:16:45.1219524Z 2024-12-18T00:16:45.1219617Z Keyword Args: 2024-12-18T00:16:45.1219834Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T00:16:45.1220161Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-12-18T00:16:45.1220537Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-12-18T00:16:45.1220685Z as a placeholder. default: None. 2024-12-18T00:16:45.1220917Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T00:16:45.1221312Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T00:16:45.1221751Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-12-18T00:16:45.1221900Z input_kwarg_layouts (Dict[str, Placement]): 2024-12-18T00:16:45.1222276Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-12-18T00:16:45.1222383Z default: None 2024-12-18T00:16:45.1222548Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-12-18T00:16:45.1222922Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T00:16:45.1223084Z have the desired DTensor layouts. default: None. 2024-12-18T00:16:45.1223202Z use_local_output (bool, optional): 2024-12-18T00:16:45.1223576Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-12-18T00:16:45.1223663Z Returns: 2024-12-18T00:16:45.1223990Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-12-18T00:16:45.1224076Z 2024-12-18T00:16:45.1224171Z Example:: 2024-12-18T00:16:45.1224290Z >>> # xdoctest: +SKIP(failing) 2024-12-18T00:16:45.1224599Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-12-18T00:16:45.1224803Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T00:16:45.1224895Z >>> ... 2024-12-18T00:16:45.1225237Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T00:16:45.1225370Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T00:16:45.1225454Z >>> 2024-12-18T00:16:45.1225801Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-12-18T00:16:45.1225951Z >>> # and then redistributed to Replicated DTensor. 2024-12-18T00:16:45.1226066Z >>> parallelize_module( 2024-12-18T00:16:45.1226204Z >>> block, # this can be a submodule or module 2024-12-18T00:16:45.1226309Z >>> tp_mesh, 2024-12-18T00:16:45.1226416Z >>> parallelize_plan={ 2024-12-18T00:16:45.1226541Z >>> "attn": PrepareModuleInput( 2024-12-18T00:16:45.1226692Z >>> input_layouts=(Shard(0), None, None, ...), 2024-12-18T00:16:45.1226862Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-12-18T00:16:45.1226964Z >>> ), 2024-12-18T00:16:45.1227055Z >>> } 2024-12-18T00:16:45.1227142Z >>> ) 2024-12-18T00:16:45.1227239Z 2024-12-18T00:16:45.1227491Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1227588Z 2024-12-18T00:16:45.1227688Z warnings.warn(msg) 2024-12-18T00:16:45.1227783Z 2024-12-18T00:16:45.1227973Z --- Parse Warning: 72 / 105 --- 2024-12-18T00:16:45.1229006Z /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=533. 2024-12-18T00:16:45.1229279Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1229364Z 2024-12-18T00:16:45.1229767Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-12-18T00:16:45.1230102Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-12-18T00:16:45.1230225Z 2024-12-18T00:16:45.1230322Z Keyword Args: 2024-12-18T00:16:45.1230487Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T00:16:45.1230837Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-12-18T00:16:45.1231267Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-12-18T00:16:45.1231424Z ``None`` need to be specified as a placeholder. 2024-12-18T00:16:45.1231623Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T00:16:45.1232021Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-12-18T00:16:45.1232141Z have the desired DTensor layouts. 2024-12-18T00:16:45.1232261Z use_local_output (bool, optional): 2024-12-18T00:16:45.1232634Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-12-18T00:16:45.1232725Z Returns: 2024-12-18T00:16:45.1233033Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-12-18T00:16:45.1233119Z 2024-12-18T00:16:45.1233226Z Example:: 2024-12-18T00:16:45.1233338Z >>> # xdoctest: +SKIP(failing) 2024-12-18T00:16:45.1233653Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-12-18T00:16:45.1233856Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T00:16:45.1233946Z >>> ... 2024-12-18T00:16:45.1234263Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T00:16:45.1234393Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T00:16:45.1234516Z >>> 2024-12-18T00:16:45.1234917Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-12-18T00:16:45.1235058Z >>> # and then redistributed to Sharded DTensor. 2024-12-18T00:16:45.1235170Z >>> parallelize_module( 2024-12-18T00:16:45.1235307Z >>> block, # this can be a submodule or module 2024-12-18T00:16:45.1235411Z >>> tp_mesh, 2024-12-18T00:16:45.1235553Z >>> parallelize_plan = PrepareModuleOutput( 2024-12-18T00:16:45.1235683Z >>> output_layouts=Replicate(), 2024-12-18T00:16:45.1235805Z >>> desired_output_layouts=Shard(0) 2024-12-18T00:16:45.1235893Z >>> ) 2024-12-18T00:16:45.1235992Z >>> ) 2024-12-18T00:16:45.1236077Z 2024-12-18T00:16:45.1236340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1236427Z 2024-12-18T00:16:45.1236527Z warnings.warn(msg) 2024-12-18T00:16:45.1236623Z 2024-12-18T00:16:45.1236811Z --- Parse Warning: 73 / 105 --- 2024-12-18T00:16:45.1237816Z /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. 2024-12-18T00:16:45.1238082Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1238180Z 2024-12-18T00:16:45.1238502Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-12-18T00:16:45.1238756Z distribution where all component are from different parameterizations of 2024-12-18T00:16:45.1238979Z the same distribution type. It is parameterized by a `Categorical` 2024-12-18T00:16:45.1239179Z "selecting distribution" (over `k` component) and a component 2024-12-18T00:16:45.1239401Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-12-18T00:16:45.1239603Z (equal to `[k]`) which indexes each (batch of) component. 2024-12-18T00:16:45.1239699Z 2024-12-18T00:16:45.1239798Z Examples:: 2024-12-18T00:16:45.1239882Z 2024-12-18T00:16:45.1240014Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.1240220Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-12-18T00:16:45.1240376Z >>> # weighted normal distributions 2024-12-18T00:16:45.1240524Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T00:16:45.1240674Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-12-18T00:16:45.1240806Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T00:16:45.1240891Z 2024-12-18T00:16:45.1241101Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-12-18T00:16:45.1241235Z >>> # weighted bivariate normal distributions 2024-12-18T00:16:45.1241366Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T00:16:45.1241482Z >>> comp = D.Independent(D.Normal( 2024-12-18T00:16:45.1241613Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-12-18T00:16:45.1241743Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T00:16:45.1241830Z 2024-12-18T00:16:45.1242024Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-12-18T00:16:45.1242229Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-12-18T00:16:45.1242355Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-12-18T00:16:45.1242475Z >>> comp = D.Independent(D.Normal( 2024-12-18T00:16:45.1242615Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-12-18T00:16:45.1242744Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T00:16:45.1242832Z 2024-12-18T00:16:45.1242931Z Args: 2024-12-18T00:16:45.1243136Z mixture_distribution: `torch.distributions.Categorical`-like 2024-12-18T00:16:45.1243358Z instance. Manages the probability of selecting component. 2024-12-18T00:16:45.1243544Z The number of categories must match the rightmost batch 2024-12-18T00:16:45.1243735Z dimension of the `component_distribution`. Must have either 2024-12-18T00:16:45.1243888Z scalar `batch_shape` or `batch_shape` matching 2024-12-18T00:16:45.1244028Z `component_distribution.batch_shape[:-1]` 2024-12-18T00:16:45.1244260Z component_distribution: `torch.distributions.Distribution`-like 2024-12-18T00:16:45.1244438Z instance. Right-most batch dimension indexes component. 2024-12-18T00:16:45.1244524Z 2024-12-18T00:16:45.1244787Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1244872Z 2024-12-18T00:16:45.1244981Z warnings.warn(msg) 2024-12-18T00:16:45.1245064Z 2024-12-18T00:16:45.1245259Z --- Parse Warning: 74 / 105 --- 2024-12-18T00:16:45.1246258Z /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. 2024-12-18T00:16:45.1246523Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1246615Z 2024-12-18T00:16:45.1246801Z Creates a RelaxedBernoulli distribution, parametrized by 2024-12-18T00:16:45.1247006Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-12-18T00:16:45.1247224Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-12-18T00:16:45.1247421Z so the values are in (0, 1), and has reparametrizable samples. 2024-12-18T00:16:45.1247506Z 2024-12-18T00:16:45.1247600Z Example:: 2024-12-18T00:16:45.1247704Z 2024-12-18T00:16:45.1247850Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:45.1247999Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-12-18T00:16:45.1248156Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-12-18T00:16:45.1248251Z >>> m.sample() 2024-12-18T00:16:45.1248384Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-12-18T00:16:45.1248470Z 2024-12-18T00:16:45.1248575Z Args: 2024-12-18T00:16:45.1248722Z temperature (Tensor): relaxation temperature 2024-12-18T00:16:45.1248939Z probs (Number, Tensor): the probability of sampling `1` 2024-12-18T00:16:45.1249139Z logits (Number, Tensor): the log-odds of sampling `1` 2024-12-18T00:16:45.1249228Z 2024-12-18T00:16:45.1249496Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1249585Z 2024-12-18T00:16:45.1249700Z warnings.warn(msg) 2024-12-18T00:16:45.1249789Z 2024-12-18T00:16:45.1249977Z --- Parse Warning: 75 / 105 --- 2024-12-18T00:16:45.1251039Z /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=99. 2024-12-18T00:16:45.1251316Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1251419Z 2024-12-18T00:16:45.1251642Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-12-18T00:16:45.1251860Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-12-18T00:16:45.1252102Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-12-18T00:16:45.1252273Z its samples are on simplex, and are reparametrizable. 2024-12-18T00:16:45.1252376Z 2024-12-18T00:16:45.1252473Z Example:: 2024-12-18T00:16:45.1252572Z 2024-12-18T00:16:45.1252715Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:45.1252879Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-12-18T00:16:45.1253060Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-12-18T00:16:45.1253159Z >>> m.sample() 2024-12-18T00:16:45.1253298Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-12-18T00:16:45.1253386Z 2024-12-18T00:16:45.1253490Z Args: 2024-12-18T00:16:45.1253635Z temperature (Tensor): relaxation temperature 2024-12-18T00:16:45.1253762Z probs (Tensor): event probabilities 2024-12-18T00:16:45.1253966Z logits (Tensor): unnormalized log probability for each event 2024-12-18T00:16:45.1254055Z 2024-12-18T00:16:45.1254322Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1254413Z 2024-12-18T00:16:45.1254516Z warnings.warn(msg) 2024-12-18T00:16:45.1254615Z 2024-12-18T00:16:45.1254803Z --- Parse Warning: 76 / 105 --- 2024-12-18T00:16:45.1255827Z /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. 2024-12-18T00:16:45.1256095Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1256302Z Return a new dict with new, potentially nested, key value pair 2024-12-18T00:16:45.1256393Z 2024-12-18T00:16:45.1256494Z >>> purchase = { 2024-12-18T00:16:45.1256607Z ... "name": "Alice", 2024-12-18T00:16:45.1256788Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T00:16:45.1256921Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T00:16:45.1257007Z ... } 2024-12-18T00:16:45.1257229Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2024-12-18T00:16:45.1257340Z {'credit card': '5555-1234-1234-1234', 2024-12-18T00:16:45.1257438Z 'name': 'Alice', 2024-12-18T00:16:45.1257617Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-12-18T00:16:45.1257734Z 2024-12-18T00:16:45.1257999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1258084Z 2024-12-18T00:16:45.1258186Z warnings.warn(msg) 2024-12-18T00:16:45.1258283Z 2024-12-18T00:16:45.1258467Z --- Parse Warning: 77 / 105 --- 2024-12-18T00:16:45.1259552Z /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. 2024-12-18T00:16:45.1259819Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1259985Z Update value in a (potentially) nested dictionary 2024-12-18T00:16:45.1260073Z 2024-12-18T00:16:45.1260176Z inputs: 2024-12-18T00:16:45.1260299Z d - dictionary on which to operate 2024-12-18T00:16:45.1260524Z keys - list or tuple giving the location of the value to be changed in d 2024-12-18T00:16:45.1260665Z func - function to operate on that value 2024-12-18T00:16:45.1260751Z 2024-12-18T00:16:45.1260958Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-12-18T00:16:45.1261196Z original dictionary with v replaced by func(v), but does not mutate the 2024-12-18T00:16:45.1261301Z original dictionary. 2024-12-18T00:16:45.1261401Z 2024-12-18T00:16:45.1261612Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-12-18T00:16:45.1261839Z specified by the keys, with the innermost value set to func(default). 2024-12-18T00:16:45.1261924Z 2024-12-18T00:16:45.1262039Z >>> inc = lambda x: x + 1 2024-12-18T00:16:45.1262149Z >>> update_in({"a": 0}, ["a"], inc) 2024-12-18T00:16:45.1262241Z {'a': 1} 2024-12-18T00:16:45.1262365Z 2024-12-18T00:16:45.1262466Z >>> transaction = { 2024-12-18T00:16:45.1262579Z ... "name": "Alice", 2024-12-18T00:16:45.1262773Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T00:16:45.1262894Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T00:16:45.1262995Z ... } 2024-12-18T00:16:45.1263212Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2024-12-18T00:16:45.1263337Z {'credit card': '5555-1234-1234-1234', 2024-12-18T00:16:45.1263433Z 'name': 'Alice', 2024-12-18T00:16:45.1263614Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-12-18T00:16:45.1263703Z 2024-12-18T00:16:45.1263828Z >>> # updating a value when k0 is not in d 2024-12-18T00:16:45.1263973Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-12-18T00:16:45.1264068Z {1: {2: {3: 'bar'}}} 2024-12-18T00:16:45.1264201Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2024-12-18T00:16:45.1264301Z {1: 'foo', 2: {3: {4: 1}}} 2024-12-18T00:16:45.1264391Z 2024-12-18T00:16:45.1264656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1264742Z 2024-12-18T00:16:45.1264857Z warnings.warn(msg) 2024-12-18T00:16:45.1264941Z 2024-12-18T00:16:45.1265130Z --- Parse Warning: 78 / 105 --- 2024-12-18T00:16:45.1266146Z /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. 2024-12-18T00:16:45.1266408Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1266587Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-12-18T00:16:45.1266673Z 2024-12-18T00:16:45.1266866Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-12-18T00:16:45.1267101Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-12-18T00:16:45.1267199Z 2024-12-18T00:16:45.1267405Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-12-18T00:16:45.1267538Z structures such as dictionaries and lists. 2024-12-18T00:16:45.1267637Z 2024-12-18T00:16:45.1267738Z >>> transaction = { 2024-12-18T00:16:45.1267901Z ... "name": "Alice", 2024-12-18T00:16:45.1268094Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T00:16:45.1268213Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T00:16:45.1268311Z ... } 2024-12-18T00:16:45.1268450Z >>> get_in(["purchase", "items", 0], transaction) 2024-12-18T00:16:45.1268557Z 'Apple' 2024-12-18T00:16:45.1268668Z >>> get_in(["name"], transaction) 2024-12-18T00:16:45.1268770Z 'Alice' 2024-12-18T00:16:45.1268907Z >>> get_in(["purchase", "total"], transaction) 2024-12-18T00:16:45.1269057Z >>> get_in(["purchase", "items", "apple"], transaction) 2024-12-18T00:16:45.1269205Z >>> get_in(["purchase", "items", 10], transaction) 2024-12-18T00:16:45.1269345Z >>> get_in(["purchase", "total"], transaction, 0) 2024-12-18T00:16:45.1269448Z 0 2024-12-18T00:16:45.1269560Z >>> get_in(["y"], {}, no_default=True) 2024-12-18T00:16:45.1269681Z Traceback (most recent call last): 2024-12-18T00:16:45.1269782Z ... 2024-12-18T00:16:45.1269878Z KeyError: 'y' 2024-12-18T00:16:45.1269976Z 2024-12-18T00:16:45.1270066Z See Also: 2024-12-18T00:16:45.1270162Z itertoolz.get 2024-12-18T00:16:45.1270275Z operator.getitem 2024-12-18T00:16:45.1270361Z 2024-12-18T00:16:45.1270629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1270715Z 2024-12-18T00:16:45.1270856Z warnings.warn(msg) 2024-12-18T00:16:45.1270942Z 2024-12-18T00:16:45.1271127Z --- Parse Warning: 79 / 105 --- 2024-12-18T00:16:45.1272157Z /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. 2024-12-18T00:16:45.1272424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1272558Z Group a collection by a key function 2024-12-18T00:16:45.1272644Z 2024-12-18T00:16:45.1272826Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2024-12-18T00:16:45.1272949Z >>> groupby(len, names) # doctest: +SKIP 2024-12-18T00:16:45.1273112Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-12-18T00:16:45.1273207Z 2024-12-18T00:16:45.1273318Z >>> iseven = lambda x: x % 2 == 0 2024-12-18T00:16:45.1273505Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-12-18T00:16:45.1273784Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-12-18T00:16:45.1273871Z 2024-12-18T00:16:45.1274032Z Non-callable keys imply grouping on a member. 2024-12-18T00:16:45.1274117Z 2024-12-18T00:16:45.1274222Z >>> groupby( 2024-12-18T00:16:45.1274313Z ... "gender", 2024-12-18T00:16:45.1274419Z ... [ 2024-12-18T00:16:45.1274549Z ... {"name": "Alice", "gender": "F"}, 2024-12-18T00:16:45.1274669Z ... {"name": "Bob", "gender": "M"}, 2024-12-18T00:16:45.1274809Z ... {"name": "Charlie", "gender": "M"}, 2024-12-18T00:16:45.1274898Z ... ], 2024-12-18T00:16:45.1275013Z ... ) # doctest:+SKIP 2024-12-18T00:16:45.1275130Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-12-18T00:16:45.1275244Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-12-18T00:16:45.1275375Z {'gender': 'M', 'name': 'Charlie'}]} 2024-12-18T00:16:45.1275518Z 2024-12-18T00:16:45.1275673Z Not to be confused with ``itertools.groupby`` 2024-12-18T00:16:45.1275758Z 2024-12-18T00:16:45.1275849Z See Also: 2024-12-18T00:16:45.1275953Z countby 2024-12-18T00:16:45.1276040Z 2024-12-18T00:16:45.1276304Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1276423Z 2024-12-18T00:16:45.1276572Z warnings.warn(msg) 2024-12-18T00:16:45.1276660Z 2024-12-18T00:16:45.1276851Z --- Parse Warning: 80 / 105 --- 2024-12-18T00:16:45.1277782Z /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. 2024-12-18T00:16:45.1278045Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1278239Z Applies Batch Normalization over a N-Dimensional input. 2024-12-18T00:16:45.1278328Z 2024-12-18T00:16:45.1278781Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-12-18T00:16:45.1279014Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-12-18T00:16:45.1279234Z Internal Covariate Shift `__ . 2024-12-18T00:16:45.1279335Z 2024-12-18T00:16:45.1279434Z .. math:: 2024-12-18T00:16:45.1279534Z 2024-12-18T00:16:45.1279756Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-12-18T00:16:45.1279843Z 2024-12-18T00:16:45.1280085Z The mean and standard-deviation are calculated per-dimension over all 2024-12-18T00:16:45.1280319Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-12-18T00:16:45.1280579Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-12-18T00:16:45.1280811Z By default, the elements of :math:`\gamma` are sampled from 2024-12-18T00:16:45.1281026Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-12-18T00:16:45.1281285Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-12-18T00:16:45.1281421Z `torch.var(input, unbiased=False)`. 2024-12-18T00:16:45.1281511Z 2024-12-18T00:16:45.1281750Z Also by default, during training this layer keeps running estimates of its 2024-12-18T00:16:45.1281999Z computed mean and variance, which are then used for normalization during 2024-12-18T00:16:45.1282246Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-12-18T00:16:45.1282351Z of 0.1. 2024-12-18T00:16:45.1282439Z 2024-12-18T00:16:45.1282668Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-12-18T00:16:45.1282908Z keep running estimates, and batch statistics are instead used during 2024-12-18T00:16:45.1283019Z evaluation time as well. 2024-12-18T00:16:45.1283123Z 2024-12-18T00:16:45.1283219Z .. note:: 2024-12-18T00:16:45.1283457Z This :attr:`momentum` argument is different from one used in optimizer 2024-12-18T00:16:45.1283681Z classes and the conventional notion of momentum. Mathematically, the 2024-12-18T00:16:45.1283825Z update rule for running statistics here is 2024-12-18T00:16:45.1284107Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-12-18T00:16:45.1284314Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-12-18T00:16:45.1284432Z new observed value. 2024-12-18T00:16:45.1284518Z 2024-12-18T00:16:45.1284833Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-12-18T00:16:45.1285089Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-12-18T00:16:45.1285315Z Normalization or Spatio-temporal Batch Normalization. 2024-12-18T00:16:45.1285413Z 2024-12-18T00:16:45.1285562Z Currently :class:`SyncBatchNorm` only supports 2024-12-18T00:16:45.1285859Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-12-18T00:16:45.1286125Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-12-18T00:16:45.1286347Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-12-18T00:16:45.1286447Z Network with DDP. 2024-12-18T00:16:45.1286535Z 2024-12-18T00:16:45.1286637Z Args: 2024-12-18T00:16:45.1286804Z num_features: :math:`C` from an expected input of size 2024-12-18T00:16:45.1286917Z :math:`(N, C, +)` 2024-12-18T00:16:45.1287106Z eps: a value added to the denominator for numerical stability. 2024-12-18T00:16:45.1287210Z Default: ``1e-5`` 2024-12-18T00:16:45.1287418Z momentum: the value used for the running_mean and running_var 2024-12-18T00:16:45.1287628Z computation. Can be set to ``None`` for cumulative moving average 2024-12-18T00:16:45.1287764Z (i.e. simple average). Default: 0.1 2024-12-18T00:16:45.1287968Z affine: a boolean value that when set to ``True``, this module has 2024-12-18T00:16:45.1288136Z learnable affine parameters. Default: ``True`` 2024-12-18T00:16:45.1288351Z track_running_stats: a boolean value that when set to ``True``, this 2024-12-18T00:16:45.1288582Z module tracks the running mean and variance, and when set to ``False``, 2024-12-18T00:16:45.1288818Z this module does not track such statistics, and initializes statistics 2024-12-18T00:16:45.1289023Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-12-18T00:16:45.1289298Z When these buffers are ``None``, this module always uses batch statistics. 2024-12-18T00:16:45.1289454Z in both training and eval modes. Default: ``True`` 2024-12-18T00:16:45.1289716Z process_group: synchronization of stats happen within each process group 2024-12-18T00:16:45.1289946Z individually. Default behavior is synchronization across the whole 2024-12-18T00:16:45.1290052Z world 2024-12-18T00:16:45.1290141Z 2024-12-18T00:16:45.1290233Z Shape: 2024-12-18T00:16:45.1290360Z - Input: :math:`(N, C, +)` 2024-12-18T00:16:45.1290510Z - Output: :math:`(N, C, +)` (same shape as input) 2024-12-18T00:16:45.1290609Z 2024-12-18T00:16:45.1290701Z .. note:: 2024-12-18T00:16:45.1290949Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-12-18T00:16:45.1291166Z synchronization is disabled when ``model.eval()`` is set or if 2024-12-18T00:16:45.1291301Z ``self.training`` is otherwise ``False``. 2024-12-18T00:16:45.1291400Z 2024-12-18T00:16:45.1291497Z Examples:: 2024-12-18T00:16:45.1291583Z 2024-12-18T00:16:45.1291697Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1291813Z >>> # With Learnable Parameters 2024-12-18T00:16:45.1291936Z >>> m = nn.SyncBatchNorm(100) 2024-12-18T00:16:45.1292065Z >>> # creating process group (optional) 2024-12-18T00:16:45.1292223Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T00:16:45.1292329Z >>> ranks = list(range(8)) 2024-12-18T00:16:45.1292441Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T00:16:45.1292603Z >>> # Note: every rank calls into new_group for every 2024-12-18T00:16:45.1292756Z >>> # process group created, even if that rank is not 2024-12-18T00:16:45.1292873Z >>> # part of the group. 2024-12-18T00:16:45.1293125Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T00:16:45.1293369Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T00:16:45.1293489Z >>> # Without Learnable Parameters 2024-12-18T00:16:45.1293695Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-12-18T00:16:45.1293863Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-12-18T00:16:45.1293994Z >>> output = m(input) 2024-12-18T00:16:45.1294095Z 2024-12-18T00:16:45.1294214Z >>> # network is nn.BatchNorm layer 2024-12-18T00:16:45.1294493Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-12-18T00:16:45.1294670Z >>> # only single gpu per process is currently supported 2024-12-18T00:16:45.1294889Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-12-18T00:16:45.1295021Z >>> sync_bn_network, 2024-12-18T00:16:45.1295154Z >>> device_ids=[args.local_rank], 2024-12-18T00:16:45.1295299Z >>> output_device=args.local_rank) 2024-12-18T00:16:45.1295387Z 2024-12-18T00:16:45.1295642Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1295744Z 2024-12-18T00:16:45.1295850Z warnings.warn(msg) 2024-12-18T00:16:45.1295950Z 2024-12-18T00:16:45.1296155Z --- Parse Warning: 81 / 105 --- 2024-12-18T00:16:45.1297181Z /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=824. 2024-12-18T00:16:45.1297443Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1297783Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-12-18T00:16:45.1297885Z 2024-12-18T00:16:45.1297976Z Args: 2024-12-18T00:16:45.1298232Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-12-18T00:16:45.1298452Z process_group (optional): process group to scope synchronization, 2024-12-18T00:16:45.1298582Z default is the whole world 2024-12-18T00:16:45.1298669Z 2024-12-18T00:16:45.1298762Z Returns: 2024-12-18T00:16:45.1299026Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-12-18T00:16:45.1299243Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-12-18T00:16:45.1299464Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-12-18T00:16:45.1299558Z instead. 2024-12-18T00:16:45.1299658Z 2024-12-18T00:16:45.1299753Z Example:: 2024-12-18T00:16:45.1299841Z 2024-12-18T00:16:45.1299979Z >>> # Network with nn.BatchNorm layer 2024-12-18T00:16:45.1300121Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:45.1300259Z >>> module = torch.nn.Sequential( 2024-12-18T00:16:45.1300381Z >>> torch.nn.Linear(20, 100), 2024-12-18T00:16:45.1300508Z >>> torch.nn.BatchNorm1d(100), 2024-12-18T00:16:45.1300619Z >>> ).cuda() 2024-12-18T00:16:45.1300750Z >>> # creating process group (optional) 2024-12-18T00:16:45.1300908Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T00:16:45.1301018Z >>> ranks = list(range(8)) 2024-12-18T00:16:45.1301143Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T00:16:45.1301294Z >>> # Note: every rank calls into new_group for every 2024-12-18T00:16:45.1301450Z >>> # process group created, even if that rank is not 2024-12-18T00:16:45.1301597Z >>> # part of the group. 2024-12-18T00:16:45.1301722Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T00:16:45.1301984Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T00:16:45.1302189Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T00:16:45.1302611Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-12-18T00:16:45.1302699Z 2024-12-18T00:16:45.1302786Z 2024-12-18T00:16:45.1303056Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1303140Z 2024-12-18T00:16:45.1303254Z warnings.warn(msg) 2024-12-18T00:16:45.1303340Z 2024-12-18T00:16:45.1303529Z --- Parse Warning: 82 / 105 --- 2024-12-18T00:16:45.1304423Z /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. 2024-12-18T00:16:45.1304688Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1304786Z 2024-12-18T00:16:45.1305100Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-12-18T00:16:45.1305197Z 2024-12-18T00:16:45.1305468Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-12-18T00:16:45.1305711Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-12-18T00:16:45.1305797Z 2024-12-18T00:16:45.1306110Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-12-18T00:16:45.1306373Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-12-18T00:16:45.1306573Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-12-18T00:16:45.1306673Z 2024-12-18T00:16:45.1306764Z Shape: 2024-12-18T00:16:45.1306985Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-12-18T00:16:45.1307246Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-12-18T00:16:45.1307470Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-12-18T00:16:45.1307613Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-12-18T00:16:45.1307700Z 2024-12-18T00:16:45.1307800Z Args: 2024-12-18T00:16:45.1307956Z dim (Union[int, str]): Dimension to be unflattened 2024-12-18T00:16:45.1308300Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-12-18T00:16:45.1308401Z 2024-12-18T00:16:45.1308495Z Examples: 2024-12-18T00:16:45.1308619Z >>> input = torch.randn(2, 50) 2024-12-18T00:16:45.1308724Z >>> # With tuple of ints 2024-12-18T00:16:45.1308838Z >>> m = nn.Sequential( 2024-12-18T00:16:45.1308940Z >>> nn.Linear(50, 50), 2024-12-18T00:16:45.1309051Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-12-18T00:16:45.1309151Z >>> ) 2024-12-18T00:16:45.1309252Z >>> output = m(input) 2024-12-18T00:16:45.1309365Z >>> output.size() 2024-12-18T00:16:45.1309469Z torch.Size([2, 2, 5, 5]) 2024-12-18T00:16:45.1309568Z >>> # With torch.Size 2024-12-18T00:16:45.1309686Z >>> m = nn.Sequential( 2024-12-18T00:16:45.1309787Z >>> nn.Linear(50, 50), 2024-12-18T00:16:45.1309927Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-12-18T00:16:45.1310014Z >>> ) 2024-12-18T00:16:45.1310113Z >>> output = m(input) 2024-12-18T00:16:45.1310223Z >>> output.size() 2024-12-18T00:16:45.1310322Z torch.Size([2, 2, 5, 5]) 2024-12-18T00:16:45.1310462Z >>> # With namedshape (tuple of tuples) 2024-12-18T00:16:45.1310639Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-12-18T00:16:45.1310858Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-12-18T00:16:45.1310968Z >>> output = unflatten(input) 2024-12-18T00:16:45.1311067Z >>> output.size() 2024-12-18T00:16:45.1311181Z torch.Size([2, 2, 5, 5]) 2024-12-18T00:16:45.1311291Z 2024-12-18T00:16:45.1311587Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1311673Z 2024-12-18T00:16:45.1311772Z warnings.warn(msg) 2024-12-18T00:16:45.1311871Z 2024-12-18T00:16:45.1312062Z --- Parse Warning: 83 / 105 --- 2024-12-18T00:16:45.1313056Z /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=1698. 2024-12-18T00:16:45.1313324Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1313536Z Creates a criterion that measures the triplet loss given input 2024-12-18T00:16:45.1313733Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-12-18T00:16:45.1313951Z positive, and negative examples, respectively), and a nonnegative, 2024-12-18T00:16:45.1314219Z real-valued function ("distance function") used to compute the relationship 2024-12-18T00:16:45.1314441Z between the anchor and positive example ("positive distance") and the 2024-12-18T00:16:45.1314608Z anchor and negative example ("negative distance"). 2024-12-18T00:16:45.1314694Z 2024-12-18T00:16:45.1314922Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-12-18T00:16:45.1315026Z can be described as: 2024-12-18T00:16:45.1315156Z 2024-12-18T00:16:45.1315262Z .. math:: 2024-12-18T00:16:45.1315406Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-12-18T00:16:45.1315576Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-12-18T00:16:45.1315662Z 2024-12-18T00:16:45.1315923Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-12-18T00:16:45.1316214Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-12-18T00:16:45.1316458Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-12-18T00:16:45.1316715Z between the positive and negative distances that is required for the loss to 2024-12-18T00:16:45.1316946Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-12-18T00:16:45.1317080Z that the distance function can handle. 2024-12-18T00:16:45.1317166Z 2024-12-18T00:16:45.1317298Z If :attr:`reduction` is not ``'none'`` 2024-12-18T00:16:45.1317400Z (default ``'mean'``), then: 2024-12-18T00:16:45.1317489Z 2024-12-18T00:16:45.1317589Z .. math:: 2024-12-18T00:16:45.1317682Z \ell(x, y) = 2024-12-18T00:16:45.1317788Z \begin{cases} 2024-12-18T00:16:45.1317992Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-12-18T00:16:45.1318186Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-12-18T00:16:45.1318289Z \end{cases} 2024-12-18T00:16:45.1318464Z 2024-12-18T00:16:45.1318717Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-12-18T00:16:45.1318966Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-12-18T00:16:45.1319064Z 2024-12-18T00:16:45.1319154Z Args: 2024-12-18T00:16:45.1319426Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-12-18T00:16:45.1319632Z quantifies the closeness of two tensors. If not specified, 2024-12-18T00:16:45.1319834Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-12-18T00:16:45.1320113Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-12-18T00:16:45.1320384Z between the positive and negative distances required for the loss to be 0. Larger 2024-12-18T00:16:45.1320729Z margins penalize cases where the negative examples are not distant enough from the 2024-12-18T00:16:45.1320909Z anchors, relative to the positives. Default: :math:`1`. 2024-12-18T00:16:45.1321163Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-12-18T00:16:45.1321438Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-12-18T00:16:45.1321675Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-12-18T00:16:45.1321956Z negative example than the anchor is, swaps the positive example and the anchor in 2024-12-18T00:16:45.1322090Z the loss computation. Default: ``False``. 2024-12-18T00:16:45.1322381Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-12-18T00:16:45.1322569Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-12-18T00:16:45.1322772Z ``'mean'``: the sum of the output will be divided by the number of 2024-12-18T00:16:45.1323009Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-12-18T00:16:45.1323095Z 2024-12-18T00:16:45.1323189Z 2024-12-18T00:16:45.1323280Z Shape: 2024-12-18T00:16:45.1323534Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-12-18T00:16:45.1323664Z as supported by the distance function. 2024-12-18T00:16:45.1323944Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-12-18T00:16:45.1324053Z otherwise. 2024-12-18T00:16:45.1324140Z 2024-12-18T00:16:45.1324246Z Examples:: 2024-12-18T00:16:45.1324329Z 2024-12-18T00:16:45.1324446Z >>> # Initialize embeddings 2024-12-18T00:16:45.1324569Z >>> embedding = nn.Embedding(1000, 128) 2024-12-18T00:16:45.1324702Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-12-18T00:16:45.1324848Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-12-18T00:16:45.1324980Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-12-18T00:16:45.1325104Z >>> anchor = embedding(anchor_ids) 2024-12-18T00:16:45.1325227Z >>> positive = embedding(positive_ids) 2024-12-18T00:16:45.1325348Z >>> negative = embedding(negative_ids) 2024-12-18T00:16:45.1325446Z >>> 2024-12-18T00:16:45.1325561Z >>> # Built-in Distance Function 2024-12-18T00:16:45.1325675Z >>> triplet_loss = \ 2024-12-18T00:16:45.1325957Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-12-18T00:16:45.1326123Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T00:16:45.1326227Z >>> output.backward() 2024-12-18T00:16:45.1326315Z >>> 2024-12-18T00:16:45.1326436Z >>> # Custom Distance Function 2024-12-18T00:16:45.1326542Z >>> def l_infinity(x1, x2): 2024-12-18T00:16:45.1326713Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-12-18T00:16:45.1326801Z >>> 2024-12-18T00:16:45.1326989Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-12-18T00:16:45.1327099Z >>> triplet_loss = ( 2024-12-18T00:16:45.1327379Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-12-18T00:16:45.1327546Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T00:16:45.1327651Z >>> output.backward() 2024-12-18T00:16:45.1327778Z >>> 2024-12-18T00:16:45.1327898Z >>> # Custom Distance Function (Lambda) 2024-12-18T00:16:45.1327998Z >>> triplet_loss = ( 2024-12-18T00:16:45.1328140Z >>> nn.TripletMarginWithDistanceLoss( 2024-12-18T00:16:45.1328354Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-12-18T00:16:45.1328544Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T00:16:45.1328671Z >>> output.backward() 2024-12-18T00:16:45.1328762Z 2024-12-18T00:16:45.1328866Z Reference: 2024-12-18T00:16:45.1329173Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-12-18T00:16:45.1329406Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2024-12-18T00:16:45.1329494Z 2024-12-18T00:16:45.1329758Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-12-18T00:16:45.1329848Z 2024-12-18T00:16:45.1329951Z warnings.warn(msg) 2024-12-18T00:16:45.1330048Z 2024-12-18T00:16:45.1330259Z --- Parse Warning: 84 / 105 --- 2024-12-18T00:16:45.1331169Z /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. 2024-12-18T00:16:45.1331434Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1331601Z Computes a partial inverse of :class:`MaxPool2d`. 2024-12-18T00:16:45.1331687Z 2024-12-18T00:16:45.1331949Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-12-18T00:16:45.1332043Z 2024-12-18T00:16:45.1332270Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-12-18T00:16:45.1332554Z including the indices of the maximal values and computes a partial inverse 2024-12-18T00:16:45.1332705Z in which all non-maximal values are set to zero. 2024-12-18T00:16:45.1332802Z 2024-12-18T00:16:45.1332890Z Note: 2024-12-18T00:16:45.1333206Z This operation may behave nondeterministically when the input indices has repeat values. 2024-12-18T00:16:45.1333595Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-12-18T00:16:45.1333683Z 2024-12-18T00:16:45.1333920Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-12-18T00:16:45.1334094Z sizes. Hence, the inversion process can get ambiguous. 2024-12-18T00:16:45.1334297Z To accommodate this, you can provide the needed output size 2024-12-18T00:16:45.1334507Z as an additional argument :attr:`output_size` in the forward call. 2024-12-18T00:16:45.1334632Z See the Inputs and Example below. 2024-12-18T00:16:45.1334731Z 2024-12-18T00:16:45.1334819Z Args: 2024-12-18T00:16:45.1335011Z kernel_size (int or tuple): Size of the max pooling window. 2024-12-18T00:16:45.1335183Z stride (int or tuple): Stride of the max pooling window. 2024-12-18T00:16:45.1335322Z It is set to :attr:`kernel_size` by default. 2024-12-18T00:16:45.1335521Z padding (int or tuple): Padding that was added to the input 2024-12-18T00:16:45.1335610Z 2024-12-18T00:16:45.1335711Z Inputs: 2024-12-18T00:16:45.1335834Z - `input`: the input Tensor to invert 2024-12-18T00:16:45.1336055Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-12-18T00:16:45.1336214Z - `output_size` (optional): the targeted output size 2024-12-18T00:16:45.1336301Z 2024-12-18T00:16:45.1336402Z Shape: 2024-12-18T00:16:45.1336591Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-12-18T00:16:45.1336843Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-12-18T00:16:45.1336930Z 2024-12-18T00:16:45.1337031Z .. math:: 2024-12-18T00:16:45.1337304Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-12-18T00:16:45.1337390Z 2024-12-18T00:16:45.1337493Z .. math:: 2024-12-18T00:16:45.1337803Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-12-18T00:16:45.1337901Z 2024-12-18T00:16:45.1338069Z or as given by :attr:`output_size` in the call operator 2024-12-18T00:16:45.1338153Z 2024-12-18T00:16:45.1338255Z Example:: 2024-12-18T00:16:45.1338338Z 2024-12-18T00:16:45.1346302Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-12-18T00:16:45.1346531Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-12-18T00:16:45.1346692Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-12-18T00:16:45.1346828Z [ 5., 6., 7., 8.], 2024-12-18T00:16:45.1346939Z [ 9., 10., 11., 12.], 2024-12-18T00:16:45.1347072Z [13., 14., 15., 16.]]]]) 2024-12-18T00:16:45.1347195Z >>> output, indices = pool(input) 2024-12-18T00:16:45.1347325Z >>> unpool(output, indices) 2024-12-18T00:16:45.1347440Z tensor([[[[ 0., 0., 0., 0.], 2024-12-18T00:16:45.1347540Z [ 0., 6., 0., 8.], 2024-12-18T00:16:45.1347653Z [ 0., 0., 0., 0.], 2024-12-18T00:16:45.1347760Z [ 0., 14., 0., 16.]]]]) 2024-12-18T00:16:45.1347990Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-12-18T00:16:45.1348138Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-12-18T00:16:45.1348354Z [ 6., 7., 8., 9., 10.], 2024-12-18T00:16:45.1348469Z [11., 12., 13., 14., 15.], 2024-12-18T00:16:45.1348584Z [16., 17., 18., 19., 20.]]]]) 2024-12-18T00:16:45.1348718Z >>> output, indices = pool(input) 2024-12-18T00:16:45.1348904Z >>> # This call will not work without specifying output_size 2024-12-18T00:16:45.1349080Z >>> unpool(output, indices, output_size=input.size()) 2024-12-18T00:16:45.1349192Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-12-18T00:16:45.1349312Z [ 0., 7., 0., 9., 0.], 2024-12-18T00:16:45.1349416Z [ 0., 0., 0., 0., 0.], 2024-12-18T00:16:45.1349521Z [ 0., 17., 0., 19., 0.]]]]) 2024-12-18T00:16:45.1349628Z 2024-12-18T00:16:45.1349711Z 2024-12-18T00:16:45.1349809Z 2024-12-18T00:16:45.1350070Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1350160Z 2024-12-18T00:16:45.1350275Z warnings.warn(msg) 2024-12-18T00:16:45.1350363Z 2024-12-18T00:16:45.1350622Z --- Parse Warning: 85 / 105 --- 2024-12-18T00:16:45.1351526Z /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. 2024-12-18T00:16:45.1351801Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1352119Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-12-18T00:16:45.1352208Z 2024-12-18T00:16:45.1352544Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-12-18T00:16:45.1352657Z and with 2D inputs, this class 2024-12-18T00:16:45.1352755Z 2024-12-18T00:16:45.1353105Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-12-18T00:16:45.1353428Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-12-18T00:16:45.1353763Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-12-18T00:16:45.1353846Z 2024-12-18T00:16:45.1354265Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-12-18T00:16:45.1354360Z operations. 2024-12-18T00:16:45.1354453Z 2024-12-18T00:16:45.1354715Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-12-18T00:16:45.1354961Z pass. This scales the output of the Embedding before performing a weighted 2024-12-18T00:16:45.1355212Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-12-18T00:16:45.1355451Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-12-18T00:16:45.1355572Z :attr:`per_sample_weights`. 2024-12-18T00:16:45.1355658Z 2024-12-18T00:16:45.1355757Z Args: 2024-12-18T00:16:45.1355941Z num_embeddings (int): size of the dictionary of embeddings 2024-12-18T00:16:45.1356112Z embedding_dim (int): the size of each embedding vector 2024-12-18T00:16:45.1356440Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-12-18T00:16:45.1356595Z is renormalized to have norm :attr:`max_norm`. 2024-12-18T00:16:45.1356945Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-12-18T00:16:45.1357275Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-12-18T00:16:45.1357489Z the words in the mini-batch. Default ``False``. 2024-12-18T00:16:45.1357677Z Note: this option is not supported when ``mode="max"``. 2024-12-18T00:16:45.1357937Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-12-18T00:16:45.1358166Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-12-18T00:16:45.1358503Z into consideration. ``"mean"`` computes the average of the values 2024-12-18T00:16:45.1358684Z in the bag, ``"max"`` computes the max value over each bag. 2024-12-18T00:16:45.1358801Z Default: ``"mean"`` 2024-12-18T00:16:45.1359132Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-12-18T00:16:45.1359395Z Notes for more details regarding sparse gradients. Note: this option is not 2024-12-18T00:16:45.1359537Z supported when ``mode="max"``. 2024-12-18T00:16:45.1359912Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-12-18T00:16:45.1360155Z is equivalent to the size of `indices`. This matches the CSR format. 2024-12-18T00:16:45.1360487Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-12-18T00:16:45.1360773Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-12-18T00:16:45.1361019Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-12-18T00:16:45.1361292Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-12-18T00:16:45.1361580Z zeros, but can be updated to another value to be used as the padding vector. 2024-12-18T00:16:45.1361830Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-12-18T00:16:45.1361988Z reduction. 2024-12-18T00:16:45.1362099Z 2024-12-18T00:16:45.1362208Z Attributes: 2024-12-18T00:16:45.1362527Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-12-18T00:16:45.1362672Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-12-18T00:16:45.1362766Z 2024-12-18T00:16:45.1362870Z Examples:: 2024-12-18T00:16:45.1362967Z 2024-12-18T00:16:45.1363138Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-12-18T00:16:45.1363311Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-12-18T00:16:45.1363440Z >>> # a batch of 2 samples of 4 indices each 2024-12-18T00:16:45.1363629Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T00:16:45.1363790Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T00:16:45.1363936Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T00:16:45.1364065Z >>> embedding_sum(input, offsets) 2024-12-18T00:16:45.1364178Z tensor([[-0.8861, -5.4350, -0.0523], 2024-12-18T00:16:45.1364295Z [ 1.1306, -2.5798, -1.0044]]) 2024-12-18T00:16:45.1364379Z 2024-12-18T00:16:45.1364493Z >>> # Example with padding_idx 2024-12-18T00:16:45.1364717Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-12-18T00:16:45.1364902Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T00:16:45.1365089Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T00:16:45.1365205Z >>> embedding_sum(input, offsets) 2024-12-18T00:16:45.1365312Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-12-18T00:16:45.1365426Z [-0.7082, 3.2145, -2.6251]]) 2024-12-18T00:16:45.1365515Z 2024-12-18T00:16:45.1365704Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-12-18T00:16:45.1365855Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-12-18T00:16:45.1366024Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-12-18T00:16:45.1366132Z embedding.weight, 2024-12-18T00:16:45.1366264Z padding_idx=embedding.padding_idx, 2024-12-18T00:16:45.1366370Z mode='sum') 2024-12-18T00:16:45.1366459Z 2024-12-18T00:16:45.1366725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1366814Z 2024-12-18T00:16:45.1366916Z warnings.warn(msg) 2024-12-18T00:16:45.1367011Z 2024-12-18T00:16:45.1367227Z --- Parse Warning: 86 / 105 --- 2024-12-18T00:16:45.1368255Z /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=1748. 2024-12-18T00:16:45.1368522Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1368618Z 2024-12-18T00:16:45.1368854Z Context manager for training with uneven inputs across processes in DDP. 2024-12-18T00:16:45.1368940Z 2024-12-18T00:16:45.1369172Z This context manager will keep track of already-joined DDP processes, 2024-12-18T00:16:45.1369381Z and "shadow" the forward and backward passes by inserting collective 2024-12-18T00:16:45.1369628Z communication operations to match with the ones created by non-joined 2024-12-18T00:16:45.1369891Z DDP processes. This will ensure each collective call has a corresponding 2024-12-18T00:16:45.1370125Z call by already-joined DDP processes, preventing hangs or errors that 2024-12-18T00:16:45.1370331Z would otherwise happen when training with uneven inputs across 2024-12-18T00:16:45.1370595Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-12-18T00:16:45.1370841Z specified to be ``True``, all trainers will throw an error once one rank 2024-12-18T00:16:45.1371043Z runs out of inputs, allowing these errors to be caught and handled 2024-12-18T00:16:45.1371167Z according to application logic. 2024-12-18T00:16:45.1371257Z 2024-12-18T00:16:45.1371487Z Once all DDP processes have joined, the context manager will broadcast 2024-12-18T00:16:45.1371712Z the model corresponding to the last joined process to all processes to 2024-12-18T00:16:45.1371865Z ensure the model is the same across all processes 2024-12-18T00:16:45.1371988Z (which is guaranteed by DDP). 2024-12-18T00:16:45.1372074Z 2024-12-18T00:16:45.1372288Z To use this to enable training with uneven inputs across processes, 2024-12-18T00:16:45.1372512Z simply wrap this context manager around your training loop. No further 2024-12-18T00:16:45.1372697Z modifications to the model or data loading is required. 2024-12-18T00:16:45.1372785Z 2024-12-18T00:16:45.1372883Z .. warning:: 2024-12-18T00:16:45.1373105Z If the model or training loop this context manager is wrapped around 2024-12-18T00:16:45.1373291Z has additional distributed collective operations, such as 2024-12-18T00:16:45.1373489Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-12-18T00:16:45.1373846Z ``throw_on_early_termination`` must be enabled. This is because this 2024-12-18T00:16:45.1374071Z context manager is not aware of non-DDP collective communication. 2024-12-18T00:16:45.1374316Z This flag will cause all ranks to throw when any one rank 2024-12-18T00:16:45.1374532Z exhausts inputs, allowing these errors to be caught and recovered 2024-12-18T00:16:45.1374649Z from across all ranks. 2024-12-18T00:16:45.1374735Z 2024-12-18T00:16:45.1374837Z Args: 2024-12-18T00:16:45.1375022Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-12-18T00:16:45.1375232Z gradients by the initial ``world_size`` DDP training was launched 2024-12-18T00:16:45.1375410Z with. If ``False``, will compute the effective world size 2024-12-18T00:16:45.1375598Z (number of ranks that have not depleted their inputs yet) and 2024-12-18T00:16:45.1375758Z divide gradients by that during allreduce. Set 2024-12-18T00:16:45.1375938Z ``divide_by_initial_world_size=True`` to ensure every input 2024-12-18T00:16:45.1376165Z sample including the uneven inputs have equal weight in terms of 2024-12-18T00:16:45.1376344Z how much they contribute to the global gradient. This is 2024-12-18T00:16:45.1376519Z achieved by always dividing the gradient by the initial 2024-12-18T00:16:45.1376721Z ``world_size`` even when we encounter uneven inputs. If you set 2024-12-18T00:16:45.1376891Z this to ``False``, we divide the gradient by the remaining 2024-12-18T00:16:45.1377106Z number of nodes. This ensures parity with training on a smaller 2024-12-18T00:16:45.1377288Z ``world_size`` although it also means the uneven inputs would 2024-12-18T00:16:45.1377492Z contribute more towards the global gradient. Typically, you 2024-12-18T00:16:45.1377681Z would want to set this to ``True`` for cases where the last few 2024-12-18T00:16:45.1377879Z inputs of your training job are uneven. In extreme cases, where 2024-12-18T00:16:45.1378071Z there is a large discrepancy in the number of inputs, setting 2024-12-18T00:16:45.1378258Z this to ``False`` might provide better results. 2024-12-18T00:16:45.1378482Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-12-18T00:16:45.1378655Z in ``enable=False`` to disable in cases where you know that 2024-12-18T00:16:45.1378855Z inputs are even across participating processes. Default is 2024-12-18T00:16:45.1378943Z ``True``. 2024-12-18T00:16:45.1379198Z throw_on_early_termination (bool): Whether to throw an error 2024-12-18T00:16:45.1379391Z or continue training when at least one rank has exhausted 2024-12-18T00:16:45.1379578Z inputs. If ``True``, will throw upon the first rank reaching end 2024-12-18T00:16:45.1379760Z of data. If ``False``, will continue training with a smaller 2024-12-18T00:16:45.1379953Z effective world size until all ranks are joined. Note that if 2024-12-18T00:16:45.1380092Z this flag is specified, then the flag 2024-12-18T00:16:45.1380268Z ``divide_by_initial_world_size`` would be ignored. Default 2024-12-18T00:16:45.1380364Z is ``False``. 2024-12-18T00:16:45.1380461Z 2024-12-18T00:16:45.1380548Z 2024-12-18T00:16:45.1380659Z Example:: 2024-12-18T00:16:45.1380743Z 2024-12-18T00:16:45.1380873Z >>> # xdoctest: +SKIP("Distributed") 2024-12-18T00:16:45.1380967Z >>> import torch 2024-12-18T00:16:45.1381091Z >>> import torch.distributed as dist 2024-12-18T00:16:45.1381196Z >>> import os 2024-12-18T00:16:45.1381321Z >>> import torch.multiprocessing as mp 2024-12-18T00:16:45.1381439Z >>> import torch.nn as nn 2024-12-18T00:16:45.1381543Z >>> # On each spawned worker 2024-12-18T00:16:45.1381643Z >>> def worker(rank): 2024-12-18T00:16:45.1381826Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-12-18T00:16:45.1381941Z >>> torch.cuda.set_device(rank) 2024-12-18T00:16:45.1382118Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T00:16:45.1382297Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-12-18T00:16:45.1382451Z >>> model, device_ids=[rank], output_device=rank 2024-12-18T00:16:45.1382537Z >>> ) 2024-12-18T00:16:45.1382668Z >>> # Rank 1 gets one more input than rank 0. 2024-12-18T00:16:45.1382866Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-12-18T00:16:45.1382966Z >>> with model.join(): 2024-12-18T00:16:45.1383082Z >>> for _ in range(5): 2024-12-18T00:16:45.1383190Z >>> for inp in inputs: 2024-12-18T00:16:45.1383305Z >>> loss = model(inp).sum() 2024-12-18T00:16:45.1383425Z >>> loss.backward() 2024-12-18T00:16:45.1383618Z >>> # Without the join() API, the below synchronization will hang 2024-12-18T00:16:45.1383777Z >>> # blocking for rank 1's allreduce to complete. 2024-12-18T00:16:45.1383908Z >>> torch.cuda.synchronize(device=rank) 2024-12-18T00:16:45.1384006Z 2024-12-18T00:16:45.1384260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1384343Z 2024-12-18T00:16:45.1384456Z warnings.warn(msg) 2024-12-18T00:16:45.1384542Z 2024-12-18T00:16:45.1384770Z --- Parse Warning: 87 / 105 --- 2024-12-18T00:16:45.1385858Z /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=2039. 2024-12-18T00:16:45.1386132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1386217Z 2024-12-18T00:16:45.1386525Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-12-18T00:16:45.1386654Z 2024-12-18T00:16:45.1386862Z Registers an optimizer with DDP such that the optimization for a 2024-12-18T00:16:45.1387086Z parameter will run immediately when that parameter's gradient is 2024-12-18T00:16:45.1387286Z finished with reduction, instead of waiting for all parameters' 2024-12-18T00:16:45.1387516Z gradients to finish reduction. This can result in a training speedup 2024-12-18T00:16:45.1387800Z depending on your workload since the optimizer can run while gradient 2024-12-18T00:16:45.1388032Z reduction for other parameters are still ongoing. In addition, this has 2024-12-18T00:16:45.1388266Z the potential to reduce peak memory consumption during training, as it 2024-12-18T00:16:45.1388476Z only needs to load the per-parameter optimizer states of a single 2024-12-18T00:16:45.1388690Z parameter at a time, instead of loading all per-parameter optimizer 2024-12-18T00:16:45.1388794Z states at once. 2024-12-18T00:16:45.1388884Z 2024-12-18T00:16:45.1388972Z Args: 2024-12-18T00:16:45.1389184Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-12-18T00:16:45.1389285Z as a fused optimizer. 2024-12-18T00:16:45.1389466Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-12-18T00:16:45.1389678Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-12-18T00:16:45.1389903Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-12-18T00:16:45.1390120Z Optimizers. If this is omitted, all DDP model parameters will be 2024-12-18T00:16:45.1390210Z optimized. 2024-12-18T00:16:45.1390418Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-12-18T00:16:45.1390502Z 2024-12-18T00:16:45.1390606Z .. warning :: 2024-12-18T00:16:45.1390820Z _register_fused_optim should only be called once on a DDP instance, 2024-12-18T00:16:45.1391054Z and registering multiple fused optimizers for the same DDP model 2024-12-18T00:16:45.1391199Z is not currently supported. Please ping 2024-12-18T00:16:45.1391432Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T00:16:45.1391543Z for your use case. 2024-12-18T00:16:45.1391629Z 2024-12-18T00:16:45.1391721Z .. warning :: 2024-12-18T00:16:45.1391928Z _register_fused_optim and register_comm_hook currently do not 2024-12-18T00:16:45.1392145Z compose together, meaning that custom DDP communication hooks are 2024-12-18T00:16:45.1392331Z not supported with overlapped optimizers. Please ping 2024-12-18T00:16:45.1392557Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T00:16:45.1392671Z for your use case. 2024-12-18T00:16:45.1392754Z 2024-12-18T00:16:45.1392844Z .. warning :: 2024-12-18T00:16:45.1393077Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-12-18T00:16:45.1393207Z with overlapped optimizer. Please ping 2024-12-18T00:16:45.1393446Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T00:16:45.1393541Z for your use case. 2024-12-18T00:16:45.1393638Z 2024-12-18T00:16:45.1393727Z Example:: 2024-12-18T00:16:45.1393812Z 2024-12-18T00:16:45.1393956Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-12-18T00:16:45.1394257Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-12-18T00:16:45.1394466Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-12-18T00:16:45.1394557Z >>> lr = 1e-2 2024-12-18T00:16:45.1394653Z >>> betas = (0.9, 0.99) 2024-12-18T00:16:45.1394755Z >>> eps = 1e-6 2024-12-18T00:16:45.1394976Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-12-18T00:16:45.1395113Z >>> # Example with subset of parameters 2024-12-18T00:16:45.1395249Z >>> params_to_opt = [list(net.parameters())[0]] 2024-12-18T00:16:45.1395396Z >>> net._register_fused_optim( 2024-12-18T00:16:45.1395625Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-12-18T00:16:45.1395713Z ... ) 2024-12-18T00:16:45.1395812Z 2024-12-18T00:16:45.1396065Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1396159Z 2024-12-18T00:16:45.1396284Z warnings.warn(msg) 2024-12-18T00:16:45.1396392Z 2024-12-18T00:16:45.1396605Z --- Parse Warning: 88 / 105 --- 2024-12-18T00:16:45.1397605Z /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. 2024-12-18T00:16:45.1397878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1398100Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-12-18T00:16:45.1398200Z 2024-12-18T00:16:45.1398565Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T00:16:45.1398841Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T00:16:45.1399112Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T00:16:45.1399422Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T00:16:45.1399521Z 2024-12-18T00:16:45.1399611Z .. note:: 2024-12-18T00:16:45.1399862Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-12-18T00:16:45.1400082Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-12-18T00:16:45.1400302Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T00:16:45.1400560Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T00:16:45.1400783Z One place we are confident in is that NHWC(channels_last) conversion for 2024-12-18T00:16:45.1401014Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-12-18T00:16:45.1401212Z even in cases where we have to apply permutation to input tensors. 2024-12-18T00:16:45.1401313Z 2024-12-18T00:16:45.1401539Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T00:16:45.1401668Z channels_last. This ensures that; 2024-12-18T00:16:45.1401886Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T00:16:45.1402118Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T00:16:45.1402368Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T00:16:45.1402485Z from memory_format conversion. 2024-12-18T00:16:45.1402583Z 2024-12-18T00:16:45.1402808Z The optimal case is that, layers between convolution layers are channels 2024-12-18T00:16:45.1403056Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T00:16:45.1403284Z encounters the first convolution layer and stay in that memory format. 2024-12-18T00:16:45.1403527Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T00:16:45.1403624Z 2024-12-18T00:16:45.1403843Z In case where a channels last incompatible layer is between convolution 2024-12-18T00:16:45.1404067Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T00:16:45.1404291Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T00:16:45.1404530Z contiguous format and be permuted to channels last when it encounters 2024-12-18T00:16:45.1404768Z another convolution layer. There's no point in propagating that 2024-12-18T00:16:45.1404985Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T00:16:45.1405098Z ``memory_format``. 2024-12-18T00:16:45.1405185Z 2024-12-18T00:16:45.1405424Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T00:16:45.1405700Z there might have been a better spot to fuse the permutation other than 2024-12-18T00:16:45.1405828Z immediately before a convolution. 2024-12-18T00:16:45.1405912Z 2024-12-18T00:16:45.1406000Z Args: 2024-12-18T00:16:45.1406223Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-12-18T00:16:45.1406330Z ``nn.Module`` 2024-12-18T00:16:45.1406494Z memory_format: user specified ``memory_format``, 2024-12-18T00:16:45.1406680Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T00:16:45.1406767Z 2024-12-18T00:16:45.1406864Z Returns: 2024-12-18T00:16:45.1407011Z The original module with updated ``nn.Conv2d`` 2024-12-18T00:16:45.1407107Z 2024-12-18T00:16:45.1407198Z Example: 2024-12-18T00:16:45.1407337Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:45.1407508Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T00:16:45.1407741Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T00:16:45.1407860Z >>> model = nn.Sequential( 2024-12-18T00:16:45.1407978Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-12-18T00:16:45.1408094Z >>> # This is identical to: 2024-12-18T00:16:45.1408339Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T00:16:45.1408603Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T00:16:45.1408745Z >>> out = model(input) 2024-12-18T00:16:45.1408832Z 2024-12-18T00:16:45.1409100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1409185Z 2024-12-18T00:16:45.1409296Z warnings.warn(msg) 2024-12-18T00:16:45.1409381Z 2024-12-18T00:16:45.1409589Z --- Parse Warning: 89 / 105 --- 2024-12-18T00:16:45.1410601Z /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. 2024-12-18T00:16:45.1410861Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1411088Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-12-18T00:16:45.1411365Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T00:16:45.1411651Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T00:16:45.1411908Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T00:16:45.1412227Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T00:16:45.1412317Z 2024-12-18T00:16:45.1412412Z .. note:: 2024-12-18T00:16:45.1412665Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-12-18T00:16:45.1412904Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-12-18T00:16:45.1413114Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T00:16:45.1413342Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T00:16:45.1413580Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-12-18T00:16:45.1413842Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-12-18T00:16:45.1414044Z even in cases where we have to apply permutation to input tensors. 2024-12-18T00:16:45.1414129Z 2024-12-18T00:16:45.1414366Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T00:16:45.1414761Z channels_last_3d. This ensures that; 2024-12-18T00:16:45.1415015Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T00:16:45.1415249Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T00:16:45.1415492Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T00:16:45.1415607Z from memory_format conversion. 2024-12-18T00:16:45.1415691Z 2024-12-18T00:16:45.1415927Z The optimal case is that, layers between convolution layers are channels 2024-12-18T00:16:45.1416167Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T00:16:45.1416406Z encounters the first convolution layer and stay in that memory format. 2024-12-18T00:16:45.1416643Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T00:16:45.1416736Z 2024-12-18T00:16:45.1416960Z In case where a channels last incompatible layer is between convolution 2024-12-18T00:16:45.1417172Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T00:16:45.1417403Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T00:16:45.1417626Z contiguous format and be permuted to channels last when it encounters 2024-12-18T00:16:45.1417841Z another convolution layer. There's no point in propagating that 2024-12-18T00:16:45.1418085Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T00:16:45.1418193Z ``memory_format``. 2024-12-18T00:16:45.1418280Z 2024-12-18T00:16:45.1418509Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T00:16:45.1418741Z there might have been a better spot to fuse the permutation other than 2024-12-18T00:16:45.1418860Z immediately before a convolution. 2024-12-18T00:16:45.1418953Z 2024-12-18T00:16:45.1419043Z Args: 2024-12-18T00:16:45.1419268Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-12-18T00:16:45.1419376Z ``nn.Module`` 2024-12-18T00:16:45.1419527Z memory_format: user specified ``memory_format``, 2024-12-18T00:16:45.1419712Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T00:16:45.1419798Z 2024-12-18T00:16:45.1419894Z Returns: 2024-12-18T00:16:45.1420040Z The original module with updated ``nn.Conv3d`` 2024-12-18T00:16:45.1420128Z 2024-12-18T00:16:45.1420224Z Example: 2024-12-18T00:16:45.1420363Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T00:16:45.1420527Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T00:16:45.1420762Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T00:16:45.1420878Z >>> model = nn.Sequential( 2024-12-18T00:16:45.1420998Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-12-18T00:16:45.1421104Z >>> # This is identical to: 2024-12-18T00:16:45.1421365Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T00:16:45.1421637Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T00:16:45.1421748Z >>> out = model(input) 2024-12-18T00:16:45.1421835Z 2024-12-18T00:16:45.1422097Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1422219Z 2024-12-18T00:16:45.1422319Z warnings.warn(msg) 2024-12-18T00:16:45.1422408Z 2024-12-18T00:16:45.1422601Z --- Parse Warning: 90 / 105 --- 2024-12-18T00:16:45.1423551Z /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=936. 2024-12-18T00:16:45.1423814Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1424061Z Prune tensor by removing random channels along the specified dimension. 2024-12-18T00:16:45.1424141Z 2024-12-18T00:16:45.1424376Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T00:16:45.1424604Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T00:16:45.1424754Z along the specified ``dim`` selected at random. 2024-12-18T00:16:45.1424956Z Modifies module in place (and also return the modified module) 2024-12-18T00:16:45.1425042Z by: 2024-12-18T00:16:45.1425128Z 2024-12-18T00:16:45.1425345Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:45.1425565Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:45.1425784Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:45.1425990Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:45.1426095Z ``name+'_orig'``. 2024-12-18T00:16:45.1426178Z 2024-12-18T00:16:45.1426266Z Args: 2024-12-18T00:16:45.1426455Z module (nn.Module): module containing the tensor to prune 2024-12-18T00:16:45.1426638Z name (str): parameter name within ``module`` on which pruning 2024-12-18T00:16:45.1426765Z will act. 2024-12-18T00:16:45.1426938Z amount (int or float): quantity of parameters to prune. 2024-12-18T00:16:45.1427126Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T00:16:45.1427324Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T00:16:45.1427455Z absolute number of parameters to prune. 2024-12-18T00:16:45.1427661Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T00:16:45.1427744Z 2024-12-18T00:16:45.1427843Z Returns: 2024-12-18T00:16:45.1428061Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T00:16:45.1428152Z 2024-12-18T00:16:45.1428241Z Examples: 2024-12-18T00:16:45.1428340Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1428467Z >>> m = prune.random_structured( 2024-12-18T00:16:45.1428610Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-12-18T00:16:45.1428714Z ... ) 2024-12-18T00:16:45.1428897Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-12-18T00:16:45.1429005Z >>> print(columns_pruned) 2024-12-18T00:16:45.1429101Z 3 2024-12-18T00:16:45.1429188Z 2024-12-18T00:16:45.1429453Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1429541Z 2024-12-18T00:16:45.1429644Z warnings.warn(msg) 2024-12-18T00:16:45.1429739Z 2024-12-18T00:16:45.1429929Z --- Parse Warning: 91 / 105 --- 2024-12-18T00:16:45.1430811Z /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=977. 2024-12-18T00:16:45.1431074Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1431394Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-12-18T00:16:45.1431506Z 2024-12-18T00:16:45.1431745Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T00:16:45.1431972Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T00:16:45.1432182Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-12-18T00:16:45.1432415Z Modifies module in place (and also return the modified module) 2024-12-18T00:16:45.1432505Z by: 2024-12-18T00:16:45.1432605Z 2024-12-18T00:16:45.1432814Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:45.1433036Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:45.1433257Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:45.1433470Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:45.1433577Z ``name+'_orig'``. 2024-12-18T00:16:45.1433661Z 2024-12-18T00:16:45.1433759Z Args: 2024-12-18T00:16:45.1433937Z module (nn.Module): module containing the tensor to prune 2024-12-18T00:16:45.1434119Z name (str): parameter name within ``module`` on which pruning 2024-12-18T00:16:45.1434221Z will act. 2024-12-18T00:16:45.1434395Z amount (int or float): quantity of parameters to prune. 2024-12-18T00:16:45.1434582Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T00:16:45.1434780Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T00:16:45.1434924Z absolute number of parameters to prune. 2024-12-18T00:16:45.1435112Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-12-18T00:16:45.1435267Z entries for argument ``p`` in :func:`torch.norm`. 2024-12-18T00:16:45.1435503Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T00:16:45.1435737Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-12-18T00:16:45.1435940Z shape as module parameter) used to compute mask for pruning. 2024-12-18T00:16:45.1436163Z The values in this tensor indicate the importance of the corresponding 2024-12-18T00:16:45.1436309Z elements in the parameter being pruned. 2024-12-18T00:16:45.1436535Z If unspecified or None, the module parameter will be used in its place. 2024-12-18T00:16:45.1436618Z 2024-12-18T00:16:45.1436718Z Returns: 2024-12-18T00:16:45.1436937Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T00:16:45.1437033Z 2024-12-18T00:16:45.1437121Z Examples: 2024-12-18T00:16:45.1437244Z >>> from torch.nn.utils import prune 2024-12-18T00:16:45.1437368Z >>> m = prune.ln_structured( 2024-12-18T00:16:45.1437553Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-12-18T00:16:45.1437648Z ... ) 2024-12-18T00:16:45.1437734Z 2024-12-18T00:16:45.1437998Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1438078Z 2024-12-18T00:16:45.1438176Z warnings.warn(msg) 2024-12-18T00:16:45.1438270Z 2024-12-18T00:16:45.1438568Z --- Parse Warning: 92 / 105 --- 2024-12-18T00:16:45.1439488Z /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=1024. 2024-12-18T00:16:45.1439748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1439844Z 2024-12-18T00:16:45.1440275Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-12-18T00:16:45.1440393Z 2024-12-18T00:16:45.1440516Z Modifies modules in place by: 2024-12-18T00:16:45.1440601Z 2024-12-18T00:16:45.1440821Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:45.1441034Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:45.1441296Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:45.1441514Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:45.1441606Z ``name+'_orig'``. 2024-12-18T00:16:45.1441701Z 2024-12-18T00:16:45.1441785Z Args: 2024-12-18T00:16:45.1441990Z parameters (Iterable of (module, name) tuples): parameters of 2024-12-18T00:16:45.1442183Z the model to prune in a global fashion, i.e. by aggregating all 2024-12-18T00:16:45.1442389Z weights prior to deciding which ones to prune. module must be of 2024-12-18T00:16:45.1442551Z type :class:`nn.Module`, and name must be a string. 2024-12-18T00:16:45.1442769Z pruning_method (function): a valid pruning function from this module, 2024-12-18T00:16:45.1442954Z or a custom one implemented by the user that satisfies the 2024-12-18T00:16:45.1443180Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-12-18T00:16:45.1443417Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-12-18T00:16:45.1443638Z the corresponding parameter's importance scores tensor. The tensor 2024-12-18T00:16:45.1443844Z should be the same shape as the parameter, and is used for computing 2024-12-18T00:16:45.1443949Z mask for pruning. 2024-12-18T00:16:45.1444152Z If unspecified or None, the parameter will be used in place of its 2024-12-18T00:16:45.1444293Z importance scores. 2024-12-18T00:16:45.1444423Z kwargs: other keyword arguments such as: 2024-12-18T00:16:45.1444628Z amount (int or float): quantity of parameters to prune across the 2024-12-18T00:16:45.1444736Z specified parameters. 2024-12-18T00:16:45.1444910Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T00:16:45.1445119Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T00:16:45.1445249Z absolute number of parameters to prune. 2024-12-18T00:16:45.1445347Z 2024-12-18T00:16:45.1445437Z Raises: 2024-12-18T00:16:45.1445599Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-12-18T00:16:45.1445685Z 2024-12-18T00:16:45.1445772Z Note: 2024-12-18T00:16:45.1445997Z Since global structured pruning doesn't make much sense unless the 2024-12-18T00:16:45.1446194Z norm is normalized by the size of the parameter, we now limit the 2024-12-18T00:16:45.1446357Z scope of global pruning to unstructured methods. 2024-12-18T00:16:45.1446443Z 2024-12-18T00:16:45.1446530Z Examples: 2024-12-18T00:16:45.1446665Z >>> from torch.nn.utils import prune 2024-12-18T00:16:45.1446786Z >>> from collections import OrderedDict 2024-12-18T00:16:45.1446917Z >>> net = nn.Sequential(OrderedDict([ 2024-12-18T00:16:45.1447024Z ... ('first', nn.Linear(10, 4)), 2024-12-18T00:16:45.1447132Z ... ('second', nn.Linear(4, 1)), 2024-12-18T00:16:45.1447232Z ... ])) 2024-12-18T00:16:45.1447338Z >>> parameters_to_prune = ( 2024-12-18T00:16:45.1447454Z ... (net.first, 'weight'), 2024-12-18T00:16:45.1447557Z ... (net.second, 'weight'), 2024-12-18T00:16:45.1447656Z ... ) 2024-12-18T00:16:45.1447766Z >>> prune.global_unstructured( 2024-12-18T00:16:45.1447871Z ... parameters_to_prune, 2024-12-18T00:16:45.1448017Z ... pruning_method=prune.L1Unstructured, 2024-12-18T00:16:45.1448113Z ... amount=10, 2024-12-18T00:16:45.1448243Z ... ) 2024-12-18T00:16:45.1448463Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-12-18T00:16:45.1448553Z tensor(10) 2024-12-18T00:16:45.1448645Z 2024-12-18T00:16:45.1448729Z 2024-12-18T00:16:45.1448991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1449072Z 2024-12-18T00:16:45.1449266Z warnings.warn(msg) 2024-12-18T00:16:45.1449386Z 2024-12-18T00:16:45.1449585Z --- Parse Warning: 93 / 105 --- 2024-12-18T00:16:45.1450480Z /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=1143. 2024-12-18T00:16:45.1450741Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1451145Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-12-18T00:16:45.1451230Z 2024-12-18T00:16:45.1451459Z Modifies module in place (and also return the modified module) by: 2024-12-18T00:16:45.1451540Z 2024-12-18T00:16:45.1451748Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T00:16:45.1451983Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T00:16:45.1452196Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T00:16:45.1452419Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T00:16:45.1452513Z ``name+'_orig'``. 2024-12-18T00:16:45.1452597Z 2024-12-18T00:16:45.1452695Z Args: 2024-12-18T00:16:45.1452874Z module (nn.Module): module containing the tensor to prune 2024-12-18T00:16:45.1453070Z name (str): parameter name within ``module`` on which pruning 2024-12-18T00:16:45.1453202Z will act. 2024-12-18T00:16:45.1453391Z mask (Tensor): binary mask to be applied to the parameter. 2024-12-18T00:16:45.1453473Z 2024-12-18T00:16:45.1453560Z Returns: 2024-12-18T00:16:45.1453791Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T00:16:45.1453875Z 2024-12-18T00:16:45.1453978Z Examples: 2024-12-18T00:16:45.1454102Z >>> from torch.nn.utils import prune 2024-12-18T00:16:45.1454215Z >>> m = prune.custom_from_mask( 2024-12-18T00:16:45.1454398Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-12-18T00:16:45.1454484Z ... ) 2024-12-18T00:16:45.1454597Z >>> print(m.bias_mask) 2024-12-18T00:16:45.1454690Z tensor([0., 1., 0.]) 2024-12-18T00:16:45.1454787Z 2024-12-18T00:16:45.1454872Z 2024-12-18T00:16:45.1455126Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1455221Z 2024-12-18T00:16:45.1455321Z warnings.warn(msg) 2024-12-18T00:16:45.1455415Z 2024-12-18T00:16:45.1455603Z --- Parse Warning: 94 / 105 --- 2024-12-18T00:16:45.1456490Z /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=116. 2024-12-18T00:16:45.1456767Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1457131Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-12-18T00:16:45.1457227Z 2024-12-18T00:16:45.1457468Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-12-18T00:16:45.1457693Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-12-18T00:16:45.1457912Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-12-18T00:16:45.1458030Z (UAI 2018). 2024-12-18T00:16:45.1458121Z 2024-12-18T00:16:45.1458336Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-12-18T00:16:45.1458584Z but using exponential weights instead of equal weights across iterations. 2024-12-18T00:16:45.1458666Z 2024-12-18T00:16:45.1458939Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-12-18T00:16:45.1459186Z on the device :attr:`device` and allows to compute running averages of the 2024-12-18T00:16:45.1459301Z parameters of the :attr:`model`. 2024-12-18T00:16:45.1459392Z 2024-12-18T00:16:45.1459477Z Args: 2024-12-18T00:16:45.1459641Z model (torch.nn.Module): model to use with SWA/EMA 2024-12-18T00:16:45.1459876Z device (torch.device, optional): if provided, the averaged model will be 2024-12-18T00:16:45.1460005Z stored on the :attr:`device` 2024-12-18T00:16:45.1460213Z avg_fn (function, optional): the averaging function used to update 2024-12-18T00:16:45.1460416Z parameters; the function must take in the current value of the 2024-12-18T00:16:45.1460647Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-12-18T00:16:45.1460846Z parameter, and the number of models already averaged; if None, 2024-12-18T00:16:45.1461021Z an equally weighted average is used (default: None) 2024-12-18T00:16:45.1461249Z multi_avg_fn (function, optional): the averaging function used to update 2024-12-18T00:16:45.1461499Z parameters inplace; the function must take in the current values of the 2024-12-18T00:16:45.1461765Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-12-18T00:16:45.1461996Z parameters as a list, and the number of models already averaged; if None, 2024-12-18T00:16:45.1462193Z an equally weighted average is used (default: None) 2024-12-18T00:16:45.1462399Z use_buffers (bool): if ``True``, it will compute running averages for 2024-12-18T00:16:45.1462635Z both the parameters and the buffers of the model. (default: ``False``) 2024-12-18T00:16:45.1462716Z 2024-12-18T00:16:45.1462817Z Example: 2024-12-18T00:16:45.1462949Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1463081Z >>> loader, optimizer, model, loss_fn = ... 2024-12-18T00:16:45.1463259Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-12-18T00:16:45.1463480Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-12-18T00:16:45.1463610Z >>> T_max=300) 2024-12-18T00:16:45.1463707Z >>> swa_start = 160 2024-12-18T00:16:45.1463861Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-12-18T00:16:45.1463965Z >>> for i in range(300): 2024-12-18T00:16:45.1464082Z >>> for input, target in loader: 2024-12-18T00:16:45.1464204Z >>> optimizer.zero_grad() 2024-12-18T00:16:45.1464340Z >>> loss_fn(model(input), target).backward() 2024-12-18T00:16:45.1464461Z >>> optimizer.step() 2024-12-18T00:16:45.1464560Z >>> if i > swa_start: 2024-12-18T00:16:45.1464695Z >>> swa_model.update_parameters(model) 2024-12-18T00:16:45.1464814Z >>> swa_scheduler.step() 2024-12-18T00:16:45.1464903Z >>> else: 2024-12-18T00:16:45.1465019Z >>> scheduler.step() 2024-12-18T00:16:45.1465102Z >>> 2024-12-18T00:16:45.1465266Z >>> # Update bn statistics for the swa_model at the end 2024-12-18T00:16:45.1465429Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-12-18T00:16:45.1465514Z 2024-12-18T00:16:45.1465854Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-12-18T00:16:45.1466054Z If no averaging function is provided, the default is to compute 2024-12-18T00:16:45.1466210Z equally-weighted average of the weights (SWA). 2024-12-18T00:16:45.1466293Z 2024-12-18T00:16:45.1466393Z Example: 2024-12-18T00:16:45.1466544Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T00:16:45.1466784Z >>> # Compute exponential moving averages of the weights and buffers 2024-12-18T00:16:45.1466965Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-12-18T00:16:45.1467183Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-12-18T00:16:45.1467279Z 2024-12-18T00:16:45.1467373Z .. note:: 2024-12-18T00:16:45.1467596Z When using SWA/EMA with models containing Batch Normalization you may 2024-12-18T00:16:45.1467811Z need to update the activation statistics for Batch Normalization. 2024-12-18T00:16:45.1468047Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-12-18T00:16:45.1468276Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-12-18T00:16:45.1468514Z statistics in a post-training step by passing data through the model. The 2024-12-18T00:16:45.1468766Z second does it during the parameter update phase by averaging all buffers. 2024-12-18T00:16:45.1469008Z Empirical evidence has shown that updating the statistics in normalization 2024-12-18T00:16:45.1469238Z layers increases accuracy, but you may wish to empirically test which 2024-12-18T00:16:45.1469392Z approach yields the best results in your problem. 2024-12-18T00:16:45.1469474Z 2024-12-18T00:16:45.1469574Z .. note:: 2024-12-18T00:16:45.1469855Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-12-18T00:16:45.1469952Z 2024-12-18T00:16:45.1470039Z .. note:: 2024-12-18T00:16:45.1470238Z When :meth:`update_parameters` is called for the first time (i.e. 2024-12-18T00:16:45.1470435Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-12-18T00:16:45.1470638Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-12-18T00:16:45.1470837Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-12-18T00:16:45.1470945Z to update the parameters. 2024-12-18T00:16:45.1471040Z 2024-12-18T00:16:45.1471260Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T00:16:45.1471381Z https://arxiv.org/abs/1803.05407 2024-12-18T00:16:45.1471624Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-12-18T00:16:45.1471713Z Average: 2024-12-18T00:16:45.1471841Z https://arxiv.org/abs/1806.05594 2024-12-18T00:16:45.1472042Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-12-18T00:16:45.1472169Z https://arxiv.org/abs/1904.11943 2024-12-18T00:16:45.1472391Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-12-18T00:16:45.1472491Z Generalizes Well: 2024-12-18T00:16:45.1472616Z https://arxiv.org/abs/2001.02312 2024-12-18T00:16:45.1472715Z .. _Polyak averaging: 2024-12-18T00:16:45.1472896Z https://paperswithcode.com/method/polyak-averaging 2024-12-18T00:16:45.1472979Z 2024-12-18T00:16:45.1473229Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1473319Z 2024-12-18T00:16:45.1473419Z warnings.warn(msg) 2024-12-18T00:16:45.1473512Z 2024-12-18T00:16:45.1473851Z --- Parse Warning: 95 / 105 --- 2024-12-18T00:16:45.1474704Z /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=368. 2024-12-18T00:16:45.1475025Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1475239Z Anneals the learning rate in each parameter group to a fixed value. 2024-12-18T00:16:45.1475374Z 2024-12-18T00:16:45.1475642Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-12-18T00:16:45.1475873Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-12-18T00:16:45.1475959Z 2024-12-18T00:16:45.1476063Z Args: 2024-12-18T00:16:45.1476240Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-12-18T00:16:45.1476450Z swa_lrs (float or list): the learning rate value for all param groups 2024-12-18T00:16:45.1476597Z together or separately for each group. 2024-12-18T00:16:45.1476804Z annealing_epochs (int): number of epochs in the annealing phase 2024-12-18T00:16:45.1476914Z (default: 10) 2024-12-18T00:16:45.1477133Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-12-18T00:16:45.1477356Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-12-18T00:16:45.1477456Z (default: "cos") 2024-12-18T00:16:45.1477646Z last_epoch (int): the index of the last epoch (default: -1) 2024-12-18T00:16:45.1477742Z 2024-12-18T00:16:45.1477921Z The :class:`SWALR` scheduler can be used together with other 2024-12-18T00:16:45.1478150Z schedulers to switch to a constant learning rate late in the training 2024-12-18T00:16:45.1478254Z as in the example below. 2024-12-18T00:16:45.1478338Z 2024-12-18T00:16:45.1478504Z Example: 2024-12-18T00:16:45.1478638Z >>> # xdoctest: +SKIP("Undefined variables") 2024-12-18T00:16:45.1478813Z >>> loader, optimizer, model = ... 2024-12-18T00:16:45.1478930Z >>> lr_lambda = lambda epoch: 0.9 2024-12-18T00:16:45.1479170Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-12-18T00:16:45.1479281Z >>> lr_lambda=lr_lambda) 2024-12-18T00:16:45.1479455Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-12-18T00:16:45.1479653Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-12-18T00:16:45.1479749Z >>> swa_start = 160 2024-12-18T00:16:45.1479858Z >>> for i in range(300): 2024-12-18T00:16:45.1479977Z >>> for input, target in loader: 2024-12-18T00:16:45.1480108Z >>> optimizer.zero_grad() 2024-12-18T00:16:45.1480243Z >>> loss_fn(model(input), target).backward() 2024-12-18T00:16:45.1480355Z >>> optimizer.step() 2024-12-18T00:16:45.1480471Z >>> if i > swa_start: 2024-12-18T00:16:45.1480585Z >>> swa_scheduler.step() 2024-12-18T00:16:45.1480691Z >>> else: 2024-12-18T00:16:45.1480796Z >>> scheduler.step() 2024-12-18T00:16:45.1480883Z 2024-12-18T00:16:45.1481120Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T00:16:45.1481241Z https://arxiv.org/abs/1803.05407 2024-12-18T00:16:45.1481343Z 2024-12-18T00:16:45.1481600Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1481698Z 2024-12-18T00:16:45.1481794Z warnings.warn(msg) 2024-12-18T00:16:45.1481884Z 2024-12-18T00:16:45.1482091Z --- Parse Warning: 96 / 105 --- 2024-12-18T00:16:45.1482984Z /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=1274. 2024-12-18T00:16:45.1483288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1483440Z Asserts that ``actual`` and ``expected`` are close. 2024-12-18T00:16:45.1483536Z 2024-12-18T00:16:45.1483899Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-12-18T00:16:45.1484010Z 2024-12-18T00:16:45.1484116Z .. math:: 2024-12-18T00:16:45.1484241Z 2024-12-18T00:16:45.1484617Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-12-18T00:16:45.1484702Z 2024-12-18T00:16:45.1485051Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-12-18T00:16:45.1485269Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-12-18T00:16:45.1485357Z 2024-12-18T00:16:45.1485568Z In addition, they are only considered close if they have the same 2024-12-18T00:16:45.1485650Z 2024-12-18T00:16:45.1485855Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-12-18T00:16:45.1485984Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-12-18T00:16:45.1486129Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-12-18T00:16:45.1486265Z - stride (if ``check_stride`` is ``True``). 2024-12-18T00:16:45.1486351Z 2024-12-18T00:16:45.1486664Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-12-18T00:16:45.1486749Z 2024-12-18T00:16:45.1487120Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-12-18T00:16:45.1487490Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-12-18T00:16:45.1487745Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-12-18T00:16:45.1488143Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-12-18T00:16:45.1488227Z 2024-12-18T00:16:45.1488522Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-12-18T00:16:45.1488880Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-12-18T00:16:45.1488993Z definition above. 2024-12-18T00:16:45.1489073Z 2024-12-18T00:16:45.1489380Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-12-18T00:16:45.1489763Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-12-18T00:16:45.1490125Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-12-18T00:16:45.1490518Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-12-18T00:16:45.1490748Z their elements are considered close according to the above definition. 2024-12-18T00:16:45.1490843Z 2024-12-18T00:16:45.1490933Z .. note:: 2024-12-18T00:16:45.1491017Z 2024-12-18T00:16:45.1491363Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-12-18T00:16:45.1491688Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-12-18T00:16:45.1491976Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-12-18T00:16:45.1492059Z 2024-12-18T00:16:45.1492158Z Args: 2024-12-18T00:16:45.1492267Z actual (Any): Actual input. 2024-12-18T00:16:45.1492389Z expected (Any): Expected input. 2024-12-18T00:16:45.1492785Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-12-18T00:16:45.1492948Z are allowed. Otherwise type equality is required. 2024-12-18T00:16:45.1493323Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-12-18T00:16:45.1493637Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T00:16:45.1494013Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-12-18T00:16:45.1494278Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T00:16:45.1494537Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-12-18T00:16:45.1494823Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-12-18T00:16:45.1495075Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-12-18T00:16:45.1495318Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-12-18T00:16:45.1495669Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-12-18T00:16:45.1496026Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-12-18T00:16:45.1496186Z :func:`torch.promote_types`) before being compared. 2024-12-18T00:16:45.1496554Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-12-18T00:16:45.1496884Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-12-18T00:16:45.1497025Z compared. 2024-12-18T00:16:45.1497391Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-12-18T00:16:45.1497743Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-12-18T00:16:45.1498113Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-12-18T00:16:45.1498231Z should return the new message. 2024-12-18T00:16:45.1498329Z 2024-12-18T00:16:45.1498416Z Raises: 2024-12-18T00:16:45.1498660Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-12-18T00:16:45.1498829Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-12-18T00:16:45.1499152Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-12-18T00:16:45.1499524Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-12-18T00:16:45.1499628Z different types. 2024-12-18T00:16:45.1500002Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-12-18T00:16:45.1500365Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-12-18T00:16:45.1500694Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-12-18T00:16:45.1500991Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-12-18T00:16:45.1501119Z :attr:`~torch.Tensor.layout`. 2024-12-18T00:16:45.1501340Z AssertionError: If only one of corresponding tensors is quantized. 2024-12-18T00:16:45.1501723Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-12-18T00:16:45.1502052Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-12-18T00:16:45.1502172Z :attr:`~torch.Tensor.device`. 2024-12-18T00:16:45.1502512Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-12-18T00:16:45.1502923Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-12-18T00:16:45.1503299Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-12-18T00:16:45.1503385Z 2024-12-18T00:16:45.1503752Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-12-18T00:16:45.1503906Z ``dtype``'s, the maximum of both tolerances is used. 2024-12-18T00:16:45.1503994Z 2024-12-18T00:16:45.1504139Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1504271Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-12-18T00:16:45.1504392Z +===========================+============+==========+ 2024-12-18T00:16:45.1504532Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-12-18T00:16:45.1504668Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1504810Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-12-18T00:16:45.1504939Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1505085Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1505209Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1505357Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-12-18T00:16:45.1505483Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1505647Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-12-18T00:16:45.1505786Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1505926Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1506060Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1506198Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-12-18T00:16:45.1506339Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1506477Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1506599Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1506742Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1506868Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1507010Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1507137Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1507284Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1507411Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1507545Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T00:16:45.1507676Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1507804Z | other | ``0.0`` | ``0.0`` | 2024-12-18T00:16:45.1507938Z +---------------------------+------------+----------+ 2024-12-18T00:16:45.1508024Z 2024-12-18T00:16:45.1508125Z .. note:: 2024-12-18T00:16:45.1508209Z 2024-12-18T00:16:45.1508593Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-12-18T00:16:45.1508959Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-12-18T00:16:45.1509228Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-12-18T00:16:45.1509351Z 2024-12-18T00:16:45.1509453Z >>> import functools 2024-12-18T00:16:45.1509719Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-12-18T00:16:45.1509830Z >>> assert_equal(1e-9, 1e-10) 2024-12-18T00:16:45.1509995Z Traceback (most recent call last): 2024-12-18T00:16:45.1510119Z ... 2024-12-18T00:16:45.1510251Z AssertionError: Scalars are not equal! 2024-12-18T00:16:45.1510352Z 2024-12-18T00:16:45.1510467Z Expected 1e-10 but got 1e-09. 2024-12-18T00:16:45.1510596Z Absolute difference: 9.000000000000001e-10 2024-12-18T00:16:45.1510716Z Relative difference: 9.0 2024-12-18T00:16:45.1510801Z 2024-12-18T00:16:45.1510903Z Examples: 2024-12-18T00:16:45.1511023Z >>> # tensor to tensor comparison 2024-12-18T00:16:45.1511172Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-12-18T00:16:45.1511308Z >>> actual = torch.acos(torch.cos(expected)) 2024-12-18T00:16:45.1511458Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:45.1511554Z 2024-12-18T00:16:45.1511670Z >>> # scalar to scalar comparison 2024-12-18T00:16:45.1511780Z >>> import math 2024-12-18T00:16:45.1511895Z >>> expected = math.sqrt(2.0) 2024-12-18T00:16:45.1512007Z >>> actual = 2.0 / math.sqrt(2.0) 2024-12-18T00:16:45.1512166Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:45.1512252Z 2024-12-18T00:16:45.1512390Z >>> # numpy array to numpy array comparison 2024-12-18T00:16:45.1512496Z >>> import numpy as np 2024-12-18T00:16:45.1512632Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-12-18T00:16:45.1512765Z >>> actual = np.arccos(np.cos(expected)) 2024-12-18T00:16:45.1512938Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:45.1513037Z 2024-12-18T00:16:45.1513163Z >>> # sequence to sequence comparison 2024-12-18T00:16:45.1513285Z >>> import numpy as np 2024-12-18T00:16:45.1513544Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-12-18T00:16:45.1513680Z >>> # length and their elements have to match. 2024-12-18T00:16:45.1513857Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-12-18T00:16:45.1513973Z >>> actual = tuple(expected) 2024-12-18T00:16:45.1514133Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:45.1514219Z 2024-12-18T00:16:45.1514355Z >>> # mapping to mapping comparison 2024-12-18T00:16:45.1514486Z >>> from collections import OrderedDict 2024-12-18T00:16:45.1514590Z >>> import numpy as np 2024-12-18T00:16:45.1514709Z >>> foo = torch.tensor(1.0) 2024-12-18T00:16:45.1514810Z >>> bar = 2.0 2024-12-18T00:16:45.1514923Z >>> baz = np.array(3.0) 2024-12-18T00:16:45.1515185Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-12-18T00:16:45.1515401Z >>> # have to have the same set of keys and their elements have to match. 2024-12-18T00:16:45.1515611Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-12-18T00:16:45.1515747Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-12-18T00:16:45.1515906Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:45.1515992Z 2024-12-18T00:16:45.1516136Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T00:16:45.1516251Z >>> actual = expected.clone() 2024-12-18T00:16:45.1516421Z >>> # By default, directly related instances can be compared 2024-12-18T00:16:45.1516656Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-12-18T00:16:45.1516875Z >>> # This check can be made more strict with allow_subclasses=False 2024-12-18T00:16:45.1517011Z >>> torch.testing.assert_close( 2024-12-18T00:16:45.1517213Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-12-18T00:16:45.1517316Z ... ) 2024-12-18T00:16:45.1517476Z Traceback (most recent call last): 2024-12-18T00:16:45.1517588Z ... 2024-12-18T00:16:45.1517806Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T00:16:45.1518023Z and . 2024-12-18T00:16:45.1518260Z >>> # If the inputs are not directly related, they are never considered close 2024-12-18T00:16:45.1518523Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-12-18T00:16:45.1518660Z Traceback (most recent call last): 2024-12-18T00:16:45.1518749Z ... 2024-12-18T00:16:45.1519041Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T00:16:45.1519163Z and . 2024-12-18T00:16:45.1519427Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-12-18T00:16:45.1519563Z >>> # their type if check_dtype=False. 2024-12-18T00:16:45.1519734Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-12-18T00:16:45.1519830Z 2024-12-18T00:16:45.1519937Z >>> # NaN != NaN by default. 2024-12-18T00:16:45.1520064Z >>> expected = torch.tensor(float("Nan")) 2024-12-18T00:16:45.1520191Z >>> actual = expected.clone() 2024-12-18T00:16:45.1520340Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T00:16:45.1520468Z Traceback (most recent call last): 2024-12-18T00:16:45.1520585Z ... 2024-12-18T00:16:45.1520713Z AssertionError: Scalars are not close! 2024-12-18T00:16:45.1520816Z 2024-12-18T00:16:45.1520923Z Expected nan but got nan. 2024-12-18T00:16:45.1521079Z Absolute difference: nan (up to 1e-05 allowed) 2024-12-18T00:16:45.1521228Z Relative difference: nan (up to 1.3e-06 allowed) 2024-12-18T00:16:45.1521442Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-12-18T00:16:45.1521531Z 2024-12-18T00:16:45.1521657Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T00:16:45.1521791Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-12-18T00:16:45.1521934Z >>> # The default error message can be overwritten. 2024-12-18T00:16:45.1522235Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-12-18T00:16:45.1522358Z Traceback (most recent call last): 2024-12-18T00:16:45.1522457Z ... 2024-12-18T00:16:45.1522613Z AssertionError: Argh, the tensors are not close! 2024-12-18T00:16:45.1522837Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-12-18T00:16:45.1522951Z >>> # extra information 2024-12-18T00:16:45.1523064Z >>> torch.testing.assert_close( 2024-12-18T00:16:45.1523277Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-12-18T00:16:45.1523368Z ... ) 2024-12-18T00:16:45.1523498Z Traceback (most recent call last): 2024-12-18T00:16:45.1523584Z ... 2024-12-18T00:16:45.1523690Z AssertionError: Header 2024-12-18T00:16:45.1523798Z 2024-12-18T00:16:45.1523906Z Tensor-likes are not close! 2024-12-18T00:16:45.1524008Z 2024-12-18T00:16:45.1524122Z Mismatched elements: 2 / 3 (66.7%) 2024-12-18T00:16:45.1524350Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-12-18T00:16:45.1524619Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-12-18T00:16:45.1524713Z 2024-12-18T00:16:45.1524814Z Footer 2024-12-18T00:16:45.1524901Z 2024-12-18T00:16:45.1525155Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1525275Z 2024-12-18T00:16:45.1525413Z warnings.warn(msg) 2024-12-18T00:16:45.1525511Z 2024-12-18T00:16:45.1525731Z --- Parse Warning: 97 / 105 --- 2024-12-18T00:16:45.1526663Z /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=110. 2024-12-18T00:16:45.1526927Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1527075Z Register a container-like type as pytree node. 2024-12-18T00:16:45.1527175Z 2024-12-18T00:16:45.1527264Z Args: 2024-12-18T00:16:45.1527469Z cls (type): A Python type to treat as an internal pytree node. 2024-12-18T00:16:45.1527738Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-12-18T00:16:45.1528006Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-12-18T00:16:45.1528296Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-12-18T00:16:45.1528415Z passed to the ``unflatten_fn``. 2024-12-18T00:16:45.1528704Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-12-18T00:16:45.1528965Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-12-18T00:16:45.1529163Z The function should return an instance of ``cls``. 2024-12-18T00:16:45.1529428Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-12-18T00:16:45.1529607Z qualified name used when serializing the tree spec. 2024-12-18T00:16:45.1529915Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-12-18T00:16:45.1530207Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-12-18T00:16:45.1530479Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-12-18T00:16:45.1530781Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-12-18T00:16:45.1531052Z how to convert the custom json dumpable representation of the context back to the 2024-12-18T00:16:45.1531308Z original context. This is used for json deserialization, which is being used in 2024-12-18T00:16:45.1531445Z :mod:`torch.export` right now. 2024-12-18T00:16:45.1531529Z 2024-12-18T00:16:45.1531642Z Example:: 2024-12-18T00:16:45.1531729Z 2024-12-18T00:16:45.1531828Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1531990Z >>> # Registry a Python type with lambda functions 2024-12-18T00:16:45.1532094Z >>> register_pytree_node( 2024-12-18T00:16:45.1532197Z ... set, 2024-12-18T00:16:45.1532324Z ... lambda s: (sorted(s), None, None), 2024-12-18T00:16:45.1532449Z ... lambda children, _: set(children), 2024-12-18T00:16:45.1532550Z ... ) 2024-12-18T00:16:45.1532632Z 2024-12-18T00:16:45.1532899Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1532981Z 2024-12-18T00:16:45.1533090Z warnings.warn(msg) 2024-12-18T00:16:45.1533174Z 2024-12-18T00:16:45.1533366Z --- Parse Warning: 98 / 105 --- 2024-12-18T00:16:45.1534378Z /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=1201. 2024-12-18T00:16:45.1534634Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1534731Z 2024-12-18T00:16:45.1534996Z Context passed to policy function during selective checkpointing. 2024-12-18T00:16:45.1535096Z 2024-12-18T00:16:45.1535324Z This class is used to pass relevant metadata to the policy function during 2024-12-18T00:16:45.1535587Z selective checkpointing. The metadata includes whether the current invocation 2024-12-18T00:16:45.1535763Z of the policy function is during recomputation or not. 2024-12-18T00:16:45.1535848Z 2024-12-18T00:16:45.1535956Z Example: 2024-12-18T00:16:45.1536063Z >>> # xdoctest: +SKIP(stub) 2024-12-18T00:16:45.1536153Z >>> 2024-12-18T00:16:45.1536298Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T00:16:45.1536404Z >>> print(ctx.is_recompute) 2024-12-18T00:16:45.1536503Z >>> 2024-12-18T00:16:45.1536776Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T00:16:45.1536877Z >>> 2024-12-18T00:16:45.1537027Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T00:16:45.1537119Z >>> fn, x, y, 2024-12-18T00:16:45.1537236Z >>> use_reentrant=False, 2024-12-18T00:16:45.1537339Z >>> context_fn=context_fn, 2024-12-18T00:16:45.1537435Z >>> ) 2024-12-18T00:16:45.1537520Z 2024-12-18T00:16:45.1537774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1537869Z 2024-12-18T00:16:45.1537968Z warnings.warn(msg) 2024-12-18T00:16:45.1538061Z 2024-12-18T00:16:45.1538277Z --- Parse Warning: 99 / 105 --- 2024-12-18T00:16:45.1539276Z /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=1335. 2024-12-18T00:16:45.1539540Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1539621Z 2024-12-18T00:16:45.1539869Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-12-18T00:16:45.1539953Z 2024-12-18T00:16:45.1540179Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-12-18T00:16:45.1540340Z operations are recomputed during the backward pass. 2024-12-18T00:16:45.1540426Z 2024-12-18T00:16:45.1540530Z Args: 2024-12-18T00:16:45.1540655Z policy_fn_or_list (Callable or List): 2024-12-18T00:16:45.1540837Z - If a policy function is provided, it should accept a 2024-12-18T00:16:45.1541083Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-12-18T00:16:45.1541304Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-12-18T00:16:45.1541545Z indicating whether the execution of the op should be recomputed or not. 2024-12-18T00:16:45.1541750Z - If a list of operations is provided, it is equivalent to a policy 2024-12-18T00:16:45.1541953Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-12-18T00:16:45.1542174Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-12-18T00:16:45.1542282Z operations. 2024-12-18T00:16:45.1542498Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-12-18T00:16:45.1542724Z raised if any tensors cached by selective activation checkpoint are 2024-12-18T00:16:45.1542931Z mutated in order to ensure correctness. If set to `True`, this check 2024-12-18T00:16:45.1543030Z is disabled. 2024-12-18T00:16:45.1543160Z Returns: 2024-12-18T00:16:45.1543277Z A tuple of two context managers. 2024-12-18T00:16:45.1543375Z 2024-12-18T00:16:45.1543467Z Example: 2024-12-18T00:16:45.1543578Z >>> # xdoctest: +REQUIRES(LINUX) 2024-12-18T00:16:45.1543694Z >>> import functools 2024-12-18T00:16:45.1543784Z >>> 2024-12-18T00:16:45.1543954Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-12-18T00:16:45.1544113Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-12-18T00:16:45.1544212Z >>> 2024-12-18T00:16:45.1544311Z >>> ops_to_save = [ 2024-12-18T00:16:45.1544429Z >>> torch.ops.aten.mm.default, 2024-12-18T00:16:45.1544533Z >>> ] 2024-12-18T00:16:45.1544620Z >>> 2024-12-18T00:16:45.1544766Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T00:16:45.1544870Z >>> if op in ops_to_save: 2024-12-18T00:16:45.1545004Z >>> return CheckpointPolicy.MUST_SAVE 2024-12-18T00:16:45.1545107Z >>> else: 2024-12-18T00:16:45.1545254Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-12-18T00:16:45.1545351Z >>> 2024-12-18T00:16:45.1545625Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T00:16:45.1545722Z >>> 2024-12-18T00:16:45.1545825Z >>> # or equivalently 2024-12-18T00:16:45.1546106Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-12-18T00:16:45.1546208Z >>> 2024-12-18T00:16:45.1546302Z >>> def fn(x, y): 2024-12-18T00:16:45.1546515Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-12-18T00:16:45.1546607Z >>> 2024-12-18T00:16:45.1546751Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T00:16:45.1546851Z >>> fn, x, y, 2024-12-18T00:16:45.1546954Z >>> use_reentrant=False, 2024-12-18T00:16:45.1547099Z >>> context_fn=context_fn, 2024-12-18T00:16:45.1547189Z >>> ) 2024-12-18T00:16:45.1547276Z 2024-12-18T00:16:45.1547538Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1547621Z 2024-12-18T00:16:45.1547731Z warnings.warn(msg) 2024-12-18T00:16:45.1547815Z 2024-12-18T00:16:45.1548018Z --- Parse Warning: 100 / 105 --- 2024-12-18T00:16:45.1548922Z /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=957. 2024-12-18T00:16:45.1549184Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1549280Z 2024-12-18T00:16:45.1549425Z Create a :class:`setuptools.Extension` for C++. 2024-12-18T00:16:45.1549520Z 2024-12-18T00:16:45.1549766Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T00:16:45.1550007Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-12-18T00:16:45.1550095Z 2024-12-18T00:16:45.1550302Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T00:16:45.1550465Z constructor. Full list arguments can be found at 2024-12-18T00:16:45.1550793Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T00:16:45.1550890Z 2024-12-18T00:16:45.1550988Z .. note:: 2024-12-18T00:16:45.1551222Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T00:16:45.1551442Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T00:16:45.1551642Z the user's responsibility in their library to not use APIs from 2024-12-18T00:16:45.1551887Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T00:16:45.1552107Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T00:16:45.1552383Z example, to give access to custom ops from python, the library should 2024-12-18T00:16:45.1552514Z register the ops through the dispatcher. 2024-12-18T00:16:45.1552613Z 2024-12-18T00:16:45.1552703Z Example: 2024-12-18T00:16:45.1552806Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1552995Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:45.1553135Z >>> from setuptools import setup 2024-12-18T00:16:45.1553370Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-12-18T00:16:45.1553460Z >>> setup( 2024-12-18T00:16:45.1553563Z ... name='extension', 2024-12-18T00:16:45.1553670Z ... ext_modules=[ 2024-12-18T00:16:45.1553770Z ... CppExtension( 2024-12-18T00:16:45.1553887Z ... name='extension', 2024-12-18T00:16:45.1554012Z ... sources=['extension.cpp'], 2024-12-18T00:16:45.1554139Z ... extra_compile_args=['-g'], 2024-12-18T00:16:45.1554304Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2024-12-18T00:16:45.1554393Z ... ], 2024-12-18T00:16:45.1554499Z ... cmdclass={ 2024-12-18T00:16:45.1554620Z ... 'build_ext': BuildExtension 2024-12-18T00:16:45.1554723Z ... }) 2024-12-18T00:16:45.1554811Z 2024-12-18T00:16:45.1555075Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1555171Z 2024-12-18T00:16:45.1555272Z warnings.warn(msg) 2024-12-18T00:16:45.1555369Z 2024-12-18T00:16:45.1555558Z --- Parse Warning: 101 / 105 --- 2024-12-18T00:16:45.1556469Z /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=1019. 2024-12-18T00:16:45.1556771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1556859Z 2024-12-18T00:16:45.1557029Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-12-18T00:16:45.1557114Z 2024-12-18T00:16:45.1557365Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T00:16:45.1559897Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-12-18T00:16:45.1560156Z extension. This includes the CUDA include path, library path and runtime 2024-12-18T00:16:45.1560261Z library. 2024-12-18T00:16:45.1560347Z 2024-12-18T00:16:45.1560573Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T00:16:45.1560726Z constructor. Full list arguments can be found at 2024-12-18T00:16:45.1561053Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T00:16:45.1561150Z 2024-12-18T00:16:45.1561253Z .. note:: 2024-12-18T00:16:45.1561497Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T00:16:45.1561711Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T00:16:45.1561922Z the user's responsibility in their library to not use APIs from 2024-12-18T00:16:45.1562149Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T00:16:45.1562402Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T00:16:45.1562619Z example, to give access to custom ops from python, the library should 2024-12-18T00:16:45.1562763Z register the ops through the dispatcher. 2024-12-18T00:16:45.1562847Z 2024-12-18T00:16:45.1562949Z Example: 2024-12-18T00:16:45.1563051Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1563201Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:45.1563327Z >>> from setuptools import setup 2024-12-18T00:16:45.1563552Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-12-18T00:16:45.1563703Z >>> setup( 2024-12-18T00:16:45.1563809Z ... name='cuda_extension', 2024-12-18T00:16:45.1563906Z ... ext_modules=[ 2024-12-18T00:16:45.1564020Z ... CUDAExtension( 2024-12-18T00:16:45.1564140Z ... name='cuda_extension', 2024-12-18T00:16:45.1564351Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T00:16:45.1564488Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T00:16:45.1564621Z ... 'nvcc': ['-O2']}, 2024-12-18T00:16:45.1564778Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2024-12-18T00:16:45.1564867Z ... ], 2024-12-18T00:16:45.1564973Z ... cmdclass={ 2024-12-18T00:16:45.1565093Z ... 'build_ext': BuildExtension 2024-12-18T00:16:45.1565195Z ... }) 2024-12-18T00:16:45.1565281Z 2024-12-18T00:16:45.1565387Z Compute capabilities: 2024-12-18T00:16:45.1565485Z 2024-12-18T00:16:45.1565789Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-12-18T00:16:45.1566093Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-12-18T00:16:45.1566390Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-12-18T00:16:45.1566706Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2024-12-18T00:16:45.1566991Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-12-18T00:16:45.1567128Z support (see below for details on PTX). 2024-12-18T00:16:45.1567215Z 2024-12-18T00:16:45.1567530Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-12-18T00:16:45.1567691Z CCs you want the extension to support: 2024-12-18T00:16:45.1567779Z 2024-12-18T00:16:45.1567981Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-12-18T00:16:45.1568215Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-12-18T00:16:45.1568301Z 2024-12-18T00:16:45.1568698Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-12-18T00:16:45.1569017Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-12-18T00:16:45.1569331Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-12-18T00:16:45.1569627Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-12-18T00:16:45.1569960Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-12-18T00:16:45.1570239Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-12-18T00:16:45.1570574Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-12-18T00:16:45.1570889Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-12-18T00:16:45.1570987Z "8.0 8.6" would be better. 2024-12-18T00:16:45.1571086Z 2024-12-18T00:16:45.1571386Z Note that while it's possible to include all supported archs, the more archs get included the 2024-12-18T00:16:45.1571696Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-12-18T00:16:45.1571784Z 2024-12-18T00:16:45.1572127Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-12-18T00:16:45.1572343Z To workaround the issue, move python binding logic to pure C++ file. 2024-12-18T00:16:45.1572427Z 2024-12-18T00:16:45.1572534Z Example use: 2024-12-18T00:16:45.1572665Z #include 2024-12-18T00:16:45.1572834Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-12-18T00:16:45.1572921Z 2024-12-18T00:16:45.1573016Z Instead of: 2024-12-18T00:16:45.1573137Z #include 2024-12-18T00:16:45.1573296Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-12-18T00:16:45.1573395Z 2024-12-18T00:16:45.1573869Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-12-18T00:16:45.1574392Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-12-18T00:16:45.1574476Z 2024-12-18T00:16:45.1574589Z Relocatable device code linking: 2024-12-18T00:16:45.1574686Z 2024-12-18T00:16:45.1574966Z If you want to reference device symbols across compilation units (across object files), 2024-12-18T00:16:45.1575243Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-12-18T00:16:45.1575610Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-12-18T00:16:45.1575948Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-12-18T00:16:45.1576270Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-12-18T00:16:45.1576456Z helps reduce the protentional perf degradation of `-rdc`. 2024-12-18T00:16:45.1576640Z Note that it needs to be used at both steps to be useful. 2024-12-18T00:16:45.1576724Z 2024-12-18T00:16:45.1577110Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-12-18T00:16:45.1577285Z There is also a case where `-dlink` is used without `-rdc`: 2024-12-18T00:16:45.1577555Z when an extension is linked against a static lib containing rdc-compiled objects 2024-12-18T00:16:45.1577809Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-12-18T00:16:45.1577898Z 2024-12-18T00:16:45.1578116Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-12-18T00:16:45.1578201Z 2024-12-18T00:16:45.1578302Z Example: 2024-12-18T00:16:45.1578408Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1578611Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:45.1578726Z >>> CUDAExtension( 2024-12-18T00:16:45.1578839Z ... name='cuda_extension', 2024-12-18T00:16:45.1579014Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T00:16:45.1579111Z ... dlink=True, 2024-12-18T00:16:45.1579247Z ... dlink_libraries=["dlink_lib"], 2024-12-18T00:16:45.1579376Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T00:16:45.1579506Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-12-18T00:16:45.1579606Z 2024-12-18T00:16:45.1579863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1579963Z 2024-12-18T00:16:45.1580063Z warnings.warn(msg) 2024-12-18T00:16:45.1580146Z 2024-12-18T00:16:45.1580398Z --- Parse Warning: 102 / 105 --- 2024-12-18T00:16:45.1581268Z /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=1300. 2024-12-18T00:16:45.1581547Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1581633Z 2024-12-18T00:16:45.1581793Z Load a PyTorch C++ extension just-in-time (JIT). 2024-12-18T00:16:45.1581880Z 2024-12-18T00:16:45.1582090Z To load an extension, a Ninja build file is emitted, which is used to 2024-12-18T00:16:45.1582310Z compile the given sources into a dynamic library. This library is 2024-12-18T00:16:45.1582575Z subsequently loaded into the current Python process as a module and 2024-12-18T00:16:45.1582716Z returned from this function, ready for use. 2024-12-18T00:16:45.1582804Z 2024-12-18T00:16:45.1583025Z By default, the directory to which the build file is emitted and the 2024-12-18T00:16:45.1583259Z resulting library compiled to is ``/torch_extensions/``, where 2024-12-18T00:16:45.1583493Z ```` is the temporary folder on the current platform and ```` 2024-12-18T00:16:45.1583720Z the name of the extension. This location can be overridden in two ways. 2024-12-18T00:16:45.1583927Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-12-18T00:16:45.1584157Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-12-18T00:16:45.1584379Z into subfolders of this directory. Second, if the ``build_directory`` 2024-12-18T00:16:45.1584627Z argument to this function is supplied, it overrides the entire path, i.e. 2024-12-18T00:16:45.1584795Z the library will be compiled into that folder directly. 2024-12-18T00:16:45.1584880Z 2024-12-18T00:16:45.1585104Z To compile the sources, the default system compiler (``c++``) is used, 2024-12-18T00:16:45.1585343Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-12-18T00:16:45.1585582Z additional arguments to the compilation process, ``extra_cflags`` or 2024-12-18T00:16:45.1585804Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-12-18T00:16:45.1586027Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-12-18T00:16:45.1586186Z ``extra_cflags`` to pass further include directories. 2024-12-18T00:16:45.1586272Z 2024-12-18T00:16:45.1586521Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-12-18T00:16:45.1586709Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-12-18T00:16:45.1587006Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-12-18T00:16:45.1587218Z passing the CUDA lib64 directory as a library directory, and linking 2024-12-18T00:16:45.1587386Z ``cudart``. You can pass additional flags to nvcc via 2024-12-18T00:16:45.1587592Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-12-18T00:16:45.1587925Z heuristics for finding the CUDA install directory are used, which usually 2024-12-18T00:16:45.1588150Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-12-18T00:16:45.1588243Z safest option. 2024-12-18T00:16:45.1588341Z 2024-12-18T00:16:45.1588430Z Args: 2024-12-18T00:16:45.1588651Z name: The name of the extension to build. This MUST be the same as the 2024-12-18T00:16:45.1588764Z name of the pybind11 module! 2024-12-18T00:16:45.1588967Z sources: A list of relative or absolute paths to C++ source files. 2024-12-18T00:16:45.1589206Z extra_cflags: optional list of compiler flags to forward to the build. 2024-12-18T00:16:45.1589427Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-12-18T00:16:45.1589550Z when building CUDA sources. 2024-12-18T00:16:45.1589772Z extra_ldflags: optional list of linker flags to forward to the build. 2024-12-18T00:16:45.1590007Z extra_include_paths: optional list of include directories to forward 2024-12-18T00:16:45.1590104Z to the build. 2024-12-18T00:16:45.1590286Z build_directory: optional path to use as build workspace. 2024-12-18T00:16:45.1590480Z verbose: If ``True``, turns on verbose logging of load steps. 2024-12-18T00:16:45.1590702Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T00:16:45.1590875Z the build. If set to ``None`` (default), this value is 2024-12-18T00:16:45.1591082Z automatically determined based on the existence of ``.cu`` or 2024-12-18T00:16:45.1591289Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-12-18T00:16:45.1591403Z and libraries to be included. 2024-12-18T00:16:45.1591608Z is_python_module: If ``True`` (default), imports the produced shared 2024-12-18T00:16:45.1591811Z library as a Python module. If ``False``, behavior depends on 2024-12-18T00:16:45.1591939Z ``is_standalone``. 2024-12-18T00:16:45.1592160Z is_standalone: If ``False`` (default) loads the constructed extension 2024-12-18T00:16:45.1592359Z into the process as a plain dynamic library. If ``True``, build a 2024-12-18T00:16:45.1592479Z standalone executable. 2024-12-18T00:16:45.1592565Z 2024-12-18T00:16:45.1592655Z Returns: 2024-12-18T00:16:45.1592782Z If ``is_python_module`` is ``True``: 2024-12-18T00:16:45.1592961Z Returns the loaded PyTorch extension as a Python module. 2024-12-18T00:16:45.1593060Z 2024-12-18T00:16:45.1593265Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-12-18T00:16:45.1593481Z Returns nothing. (The shared library is loaded into the process as 2024-12-18T00:16:45.1593589Z a side effect.) 2024-12-18T00:16:45.1593674Z 2024-12-18T00:16:45.1593794Z If ``is_standalone`` is ``True``. 2024-12-18T00:16:45.1594001Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-12-18T00:16:45.1594188Z added to the PATH environment variable as a side effect.) 2024-12-18T00:16:45.1594274Z 2024-12-18T00:16:45.1594367Z Example: 2024-12-18T00:16:45.1594479Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1594620Z >>> from torch.utils.cpp_extension import load 2024-12-18T00:16:45.1594729Z >>> module = load( 2024-12-18T00:16:45.1594830Z ... name='extension', 2024-12-18T00:16:45.1594993Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T00:16:45.1595136Z ... extra_cflags=['-O2'], 2024-12-18T00:16:45.1595233Z ... verbose=True) 2024-12-18T00:16:45.1595329Z 2024-12-18T00:16:45.1595583Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1595669Z 2024-12-18T00:16:45.1595781Z warnings.warn(msg) 2024-12-18T00:16:45.1595865Z 2024-12-18T00:16:45.1596112Z --- Parse Warning: 103 / 105 --- 2024-12-18T00:16:45.1597005Z /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=1593. 2024-12-18T00:16:45.1597285Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1597372Z 2024-12-18T00:16:45.1597584Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-12-18T00:16:45.1597681Z 2024-12-18T00:16:45.1597918Z This function behaves exactly like :func:`load`, but takes its sources as 2024-12-18T00:16:45.1598160Z strings rather than filenames. These strings are stored to files in the 2024-12-18T00:16:45.1598453Z build directory, after which the behavior of :func:`load_inline` is 2024-12-18T00:16:45.1598579Z identical to :func:`load`. 2024-12-18T00:16:45.1598665Z 2024-12-18T00:16:45.1598754Z See `the 2024-12-18T00:16:45.1599101Z tests `_ 2024-12-18T00:16:45.1599229Z for good examples of using this function. 2024-12-18T00:16:45.1599331Z 2024-12-18T00:16:45.1599565Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-12-18T00:16:45.1599808Z the necessary header includes, as well as the (pybind11) binding code. More 2024-12-18T00:16:45.1600062Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-12-18T00:16:45.1600254Z single ``.cpp`` file. This file is then prepended with ``#include 2024-12-18T00:16:45.1600401Z ``. 2024-12-18T00:16:45.1600487Z 2024-12-18T00:16:45.1600724Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-12-18T00:16:45.1600960Z automatically generated for each function specified. ``functions`` can 2024-12-18T00:16:45.1601203Z either be a list of function names, or a dictionary mapping from function 2024-12-18T00:16:45.1601438Z names to docstrings. If a list is given, the name of each function is used 2024-12-18T00:16:45.1601537Z as its docstring. 2024-12-18T00:16:45.1601633Z 2024-12-18T00:16:45.1601848Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-12-18T00:16:45.1602036Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-12-18T00:16:45.1602247Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-12-18T00:16:45.1602470Z separately, but ultimately linked into a single library. Note that no 2024-12-18T00:16:45.1602713Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-12-18T00:16:45.1602932Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-12-18T00:16:45.1603158Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-12-18T00:16:45.1603272Z include its name in ``functions``). 2024-12-18T00:16:45.1603375Z 2024-12-18T00:16:45.1603565Z See :func:`load` for a description of arguments omitted below. 2024-12-18T00:16:45.1603652Z 2024-12-18T00:16:45.1603753Z Args: 2024-12-18T00:16:45.1603972Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-12-18T00:16:45.1604208Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-12-18T00:16:45.1604417Z functions: A list of function names for which to generate function 2024-12-18T00:16:45.1604671Z bindings. If a dictionary is given, it should map function names to 2024-12-18T00:16:45.1604863Z docstrings (which are otherwise just the function names). 2024-12-18T00:16:45.1605089Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T00:16:45.1605266Z the build. If set to ``None`` (default), this value is 2024-12-18T00:16:45.1605478Z automatically determined based on whether ``cuda_sources`` is 2024-12-18T00:16:45.1605680Z provided. Set it to ``True`` to force CUDA headers 2024-12-18T00:16:45.1605799Z and libraries to be included. 2024-12-18T00:16:45.1606024Z with_pytorch_error_handling: Determines whether pytorch error and 2024-12-18T00:16:45.1606228Z warning macros are handled by pytorch instead of pybind. To do 2024-12-18T00:16:45.1606449Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-12-18T00:16:45.1606665Z function. This redirection might cause issues in obscure cases 2024-12-18T00:16:45.1606861Z of cpp. This flag should be set to ``False`` when this redirect 2024-12-18T00:16:45.1606975Z causes issues. 2024-12-18T00:16:45.1607064Z 2024-12-18T00:16:45.1607158Z Example: 2024-12-18T00:16:45.1607328Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T00:16:45.1607503Z >>> from torch.utils.cpp_extension import load_inline 2024-12-18T00:16:45.1607605Z >>> source = """ 2024-12-18T00:16:45.1607766Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-12-18T00:16:45.1607874Z return x.sin() + y.sin(); 2024-12-18T00:16:45.1607963Z } 2024-12-18T00:16:45.1608065Z """ 2024-12-18T00:16:45.1608214Z >>> module = load_inline(name='inline_extension', 2024-12-18T00:16:45.1608346Z ... cpp_sources=[source], 2024-12-18T00:16:45.1608467Z ... functions=['sin_add']) 2024-12-18T00:16:45.1608553Z 2024-12-18T00:16:45.1608653Z .. note:: 2024-12-18T00:16:45.1608921Z Since load_inline will just-in-time compile the source code, please ensure 2024-12-18T00:16:45.1609164Z that you have the right toolchains installed in the runtime. For example, 2024-12-18T00:16:45.1609386Z when loading C++, make sure a C++ compiler is available. If you're loading 2024-12-18T00:16:45.1609669Z a CUDA extension, you will need to additionally install the corresponding CUDA 2024-12-18T00:16:45.1609917Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2024-12-18T00:16:45.1610151Z are not included when you install torch and must be additionally installed. 2024-12-18T00:16:45.1610249Z 2024-12-18T00:16:45.1610503Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2024-12-18T00:16:45.1610733Z the extension. This may use up too many resources on some systems. One 2024-12-18T00:16:45.1610956Z can control the number of workers by setting the `MAX_JOBS` environment 2024-12-18T00:16:45.1611085Z variable to a non-negative number. 2024-12-18T00:16:45.1611173Z 2024-12-18T00:16:45.1611426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1611524Z 2024-12-18T00:16:45.1611623Z warnings.warn(msg) 2024-12-18T00:16:45.1611718Z 2024-12-18T00:16:45.1611932Z --- Parse Warning: 104 / 105 --- 2024-12-18T00:16:45.1612915Z /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. 2024-12-18T00:16:45.1613179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1613261Z 2024-12-18T00:16:45.1613563Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-12-18T00:16:45.1613673Z 2024-12-18T00:16:45.1613979Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-12-18T00:16:45.1614233Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-12-18T00:16:45.1614473Z server like load. It can emulate multiple calling threads to a single module 2024-12-18T00:16:45.1614742Z provided. In the future we plan to enhance this component to support inter and 2024-12-18T00:16:45.1614996Z intra-op parallelism as well as multiple models running in a single process. 2024-12-18T00:16:45.1615086Z 2024-12-18T00:16:45.1615344Z Please note that even though nn.Module is supported, it might incur an overhead 2024-12-18T00:16:45.1615574Z from the need to hold GIL every time we execute Python code or pass around 2024-12-18T00:16:45.1615813Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-12-18T00:16:45.1616057Z model for inference deployment it is better to switch to using it in this 2024-12-18T00:16:45.1616153Z benchmark. 2024-12-18T00:16:45.1616236Z 2024-12-18T00:16:45.1616335Z Example:: 2024-12-18T00:16:45.1616421Z 2024-12-18T00:16:45.1616551Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T00:16:45.1616696Z >>> from torch.utils import ThroughputBenchmark 2024-12-18T00:16:45.1616832Z >>> bench = ThroughputBenchmark(my_module) 2024-12-18T00:16:45.1616994Z >>> # Pre-populate benchmark's data set with the inputs 2024-12-18T00:16:45.1617101Z >>> for input in inputs: 2024-12-18T00:16:45.1617327Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-12-18T00:16:45.1617458Z ... bench.add_input(input[0], x2=input[1]) 2024-12-18T00:16:45.1617663Z >>> # Inputs supplied above are randomly used during the execution 2024-12-18T00:16:45.1617771Z >>> stats = bench.benchmark( 2024-12-18T00:16:45.1617878Z ... num_calling_threads=4, 2024-12-18T00:16:45.1618017Z ... num_warmup_iters = 100, 2024-12-18T00:16:45.1618118Z ... num_iters = 1000, 2024-12-18T00:16:45.1618216Z ... ) 2024-12-18T00:16:45.1618398Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-12-18T00:16:45.1618578Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-12-18T00:16:45.1618666Z 2024-12-18T00:16:45.1618961Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1619056Z 2024-12-18T00:16:45.1619157Z warnings.warn(msg) 2024-12-18T00:16:45.1619252Z 2024-12-18T00:16:45.1619445Z --- Parse Warning: 105 / 105 --- 2024-12-18T00:16:45.1620406Z /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=17. 2024-12-18T00:16:45.1620671Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T00:16:45.1620872Z Sampler that restricts data loading to a subset of the dataset. 2024-12-18T00:16:45.1620966Z 2024-12-18T00:16:45.1621100Z It is especially useful in conjunction with 2024-12-18T00:16:45.1621357Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-12-18T00:16:45.1621619Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-12-18T00:16:45.1621852Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-12-18T00:16:45.1621983Z original dataset that is exclusive to it. 2024-12-18T00:16:45.1622068Z 2024-12-18T00:16:45.1622169Z .. note:: 2024-12-18T00:16:45.1622407Z Dataset is assumed to be of constant size and that any instance of it always 2024-12-18T00:16:45.1622559Z returns the same elements in the same order. 2024-12-18T00:16:45.1622673Z 2024-12-18T00:16:45.1622760Z Args: 2024-12-18T00:16:45.1622894Z dataset: Dataset used for sampling. 2024-12-18T00:16:45.1623112Z num_replicas (int, optional): Number of processes participating in 2024-12-18T00:16:45.1623368Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-12-18T00:16:45.1623487Z current distributed group. 2024-12-18T00:16:45.1623759Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-12-18T00:16:45.1623965Z By default, :attr:`rank` is retrieved from the current distributed 2024-12-18T00:16:45.1624056Z group. 2024-12-18T00:16:45.1624293Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-12-18T00:16:45.1624384Z indices. 2024-12-18T00:16:45.1624585Z seed (int, optional): random seed used to shuffle the sampler if 2024-12-18T00:16:45.1624784Z :attr:`shuffle=True`. This number should be identical across all 2024-12-18T00:16:45.1624959Z processes in the distributed group. Default: ``0``. 2024-12-18T00:16:45.1625176Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-12-18T00:16:45.1625373Z tail of the data to make it evenly divisible across the number of 2024-12-18T00:16:45.1625584Z replicas. If ``False``, the sampler will add extra indices to make 2024-12-18T00:16:45.1625795Z the data evenly divisible across the replicas. Default: ``False``. 2024-12-18T00:16:45.1625887Z 2024-12-18T00:16:45.1625978Z .. warning:: 2024-12-18T00:16:45.1626176Z In distributed mode, calling the :meth:`set_epoch` method at 2024-12-18T00:16:45.1626429Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-12-18T00:16:45.1626688Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-12-18T00:16:45.1626846Z the same ordering will be always used. 2024-12-18T00:16:45.1626930Z 2024-12-18T00:16:45.1627032Z Example:: 2024-12-18T00:16:45.1627118Z 2024-12-18T00:16:45.1627226Z >>> # xdoctest: +SKIP 2024-12-18T00:16:45.1627444Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-12-18T00:16:45.1627645Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-12-18T00:16:45.1627772Z ... sampler=sampler) 2024-12-18T00:16:45.1627909Z >>> for epoch in range(start_epoch, n_epochs): 2024-12-18T00:16:45.1628023Z ... if is_distributed: 2024-12-18T00:16:45.1628141Z ... sampler.set_epoch(epoch) 2024-12-18T00:16:45.1628238Z ... train(loader) 2024-12-18T00:16:45.1628333Z 2024-12-18T00:16:45.1628587Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T00:16:45.1628681Z 2024-12-18T00:16:45.1628782Z warnings.warn(msg) 2024-12-18T00:16:45.1628874Z 2024-12-18T00:16:45.1628991Z  2024-12-18T00:16:45.1629166Z === Found 9 run-time warnings === 2024-12-18T00:16:45.1629352Z --- Runtime Warning: 1 / 9 --- 2024-12-18T00:16:45.1629617Z example = 2024-12-18T00:16:45.1630977Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1354: 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 /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1938.) 2024-12-18T00:16:45.1631096Z return super().refine_names(names) 2024-12-18T00:16:45.1631189Z 2024-12-18T00:16:45.1631367Z --- Runtime Warning: 2 / 9 --- 2024-12-18T00:16:45.1631704Z example = 2024-12-18T00:16:45.1632338Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:272: UserWarning: Warning only once for all operators, other operators may also be overridden. 2024-12-18T00:16:45.1632649Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-12-18T00:16:45.1632887Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-12-18T00:16:45.1633186Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-12-18T00:16:45.1633295Z dispatch key: CPU 2024-12-18T00:16:45.1633728Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-12-18T00:16:45.1634286Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:161.) 2024-12-18T00:16:45.1634449Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-12-18T00:16:45.1634538Z 2024-12-18T00:16:45.1634725Z --- Runtime Warning: 3 / 9 --- 2024-12-18T00:16:45.1634967Z example = 2024-12-18T00:16:45.1636805Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py:109: 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 /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:182.) 2024-12-18T00:16:45.1637051Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-12-18T00:16:45.1637143Z 2024-12-18T00:16:45.1637324Z --- Runtime Warning: 4 / 9 --- 2024-12-18T00:16:45.1637585Z example = 2024-12-18T00:16:45.1639344Z :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 /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) 2024-12-18T00:16:45.1639447Z 2024-12-18T00:16:45.1639627Z --- Runtime Warning: 5 / 9 --- 2024-12-18T00:16:45.1639943Z example = 2024-12-18T00:16:45.1641432Z /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 2024-12-18T00:16:45.1641614Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-12-18T00:16:45.1641700Z 2024-12-18T00:16:45.1641875Z --- Runtime Warning: 6 / 9 --- 2024-12-18T00:16:45.1642179Z example = 2024-12-18T00:16:45.1643260Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:375: 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) 2024-12-18T00:16:45.1643372Z warnings.warn( 2024-12-18T00:16:45.1643454Z 2024-12-18T00:16:45.1643643Z --- Runtime Warning: 7 / 9 --- 2024-12-18T00:16:45.1643972Z example = 2024-12-18T00:16:45.1645095Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:375: 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) 2024-12-18T00:16:45.1645206Z warnings.warn( 2024-12-18T00:16:45.1645291Z 2024-12-18T00:16:45.1645514Z --- Runtime Warning: 8 / 9 --- 2024-12-18T00:16:45.1645789Z example = 2024-12-18T00:16:45.1646605Z /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`. 2024-12-18T00:16:45.1646727Z WeightNorm.apply(module, name, dim) 2024-12-18T00:16:45.1655383Z 2024-12-18T00:16:45.1655703Z --- Runtime Warning: 9 / 9 --- 2024-12-18T00:16:45.1656034Z example = 2024-12-18T00:16:45.1656874Z /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`. 2024-12-18T00:16:45.1656999Z WeightNorm.apply(module, name, dim) 2024-12-18T00:16:45.1657097Z 2024-12-18T00:16:45.1657404Z === 338 passed, 367 skipped, 114 warnings in 11.04 seconds === 2024-12-18T00:16:45.1657597Z Running test_autoload_enable 1/1 ... [2024-12-18 00:16:44.966984] 2024-12-18T00:16:47.4120868Z running install 2024-12-18T00:16:47.4122023Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:16:47.4122837Z !! 2024-12-18T00:16:47.4122951Z 2024-12-18T00:16:47.4123081Z ******************************************************************************** 2024-12-18T00:16:47.4123713Z Please avoid running ``setup.py`` directly. 2024-12-18T00:16:47.4124131Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:16:47.4124516Z standards-based tools. 2024-12-18T00:16:47.4124707Z 2024-12-18T00:16:47.4125037Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:16:47.4125652Z ******************************************************************************** 2024-12-18T00:16:47.4125918Z 2024-12-18T00:16:47.4126008Z !! 2024-12-18T00:16:47.4126241Z self.initialize_options() 2024-12-18T00:16:47.4254100Z running build 2024-12-18T00:16:47.4254348Z running build_py 2024-12-18T00:16:47.4329796Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:16:47.4331833Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:16:47.4334899Z running build_ext 2024-12-18T00:16:47.5132451Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:16:47.5132956Z creating build/temp.linux-x86_64-cpython-313 2024-12-18T00:16:47.5138621Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:16:48.5063260Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T00:16:48.5064294Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T00:16:48.5065384Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2024-12-18T00:16:48.5065948Z from extension.cpp:1: 2024-12-18T00:16:48.5067265Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T00:16:48.5068307Z extension.cpp:45:53: required from here 2024-12-18T00:16:48.5070422Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T00:16:48.5072569Z 1539 | class class_ : public detail::generic_type { 2024-12-18T00:16:48.5073079Z | ^~~~~~ 2024-12-18T00:16:48.5075552Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T00:16:48.5077132Z extension.cpp:45:53: required from here 2024-12-18T00:16:48.5080528Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2024-12-18T00:16:48.5083405Z 1599 | with_internals([&](internals &internals) { 2024-12-18T00:16:48.5083796Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:48.5084389Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T00:16:48.5084982Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:48.5085452Z 1601 | : internals.registered_types_cpp; 2024-12-18T00:16:48.5085868Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:48.5086302Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T00:16:48.5086728Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:48.5087135Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T00:16:48.5087539Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:48.5087872Z 1604 | }); 2024-12-18T00:16:48.5088113Z | ~ 2024-12-18T00:16:48.5091324Z 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 2024-12-18T00:16:48.9031866Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:16:48.9036651Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:16:49.8420424Z 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 2024-12-18T00:16:50.2089276Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:16:50.2093703Z 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 -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -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 2024-12-18T00:16:51.3553175Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:51.3554155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:51.3554959Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:51.3555888Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:51.3556571Z from rng_extension.cpp:6: 2024-12-18T00:16:51.3557396Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:51.3558195Z 1123 | # pragma unroll 2024-12-18T00:16:51.3558512Z | 2024-12-18T00:16:51.3559076Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T00:16:51.3560156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:51.3560982Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:51.3561851Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:51.3562837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:51.3563509Z from rng_extension.cpp:6: 2024-12-18T00:16:51.3564296Z /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] 2024-12-18T00:16:51.3565085Z 59 | #pragma unroll 2024-12-18T00:16:51.3565343Z | 2024-12-18T00:16:51.3566037Z /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] 2024-12-18T00:16:51.3566810Z 72 | #pragma unroll 2024-12-18T00:16:51.3567049Z | 2024-12-18T00:16:51.3567729Z /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] 2024-12-18T00:16:51.3568501Z 87 | #pragma unroll 2024-12-18T00:16:51.3568758Z | 2024-12-18T00:16:51.3569308Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T00:16:51.3570220Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:51.3571030Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:51.3571836Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:51.3572756Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:51.3573498Z from rng_extension.cpp:6: 2024-12-18T00:16:51.3574484Z /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] 2024-12-18T00:16:51.3575284Z 153 | #pragma unroll 2024-12-18T00:16:51.3575597Z | 2024-12-18T00:16:51.3578746Z 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 2024-12-18T00:16:51.7815948Z running install_lib 2024-12-18T00:16:51.7897741Z 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 2024-12-18T00:16:51.7999799Z 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 2024-12-18T00:16:51.8102807Z 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 2024-12-18T00:16:51.8209457Z running install_egg_info 2024-12-18T00:16:51.8380777Z running egg_info 2024-12-18T00:16:51.8449105Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:16:51.8452884Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:16:51.8454463Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:16:51.8457197Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:16:51.8530318Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:16:51.8539311Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:16:51.8540898Z 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) 2024-12-18T00:16:51.8543054Z 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 2024-12-18T00:16:51.8550347Z running install_scripts 2024-12-18T00:16:55.0007690Z 2024-12-18T00:16:55.0008132Z Running tests... 2024-12-18T00:16:55.0008496Z ---------------------------------------------------------------------- 2024-12-18T00:16:55.0120227Z . 2024-12-18T00:16:55.0120530Z ---------------------------------------------------------------------- 2024-12-18T00:16:55.0120914Z Ran 1 test in 0.011s 2024-12-18T00:16:55.0121076Z 2024-12-18T00:16:55.0121179Z OK 2024-12-18T00:16:55.0121305Z 2024-12-18T00:16:55.0121417Z Generating XML reports... 2024-12-18T00:16:55.5737019Z Running inductor/test_aot_inductor 1/1 ... [2024-12-18 00:16:55.573406] 2024-12-18T00:16:55.5737512Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:16:55.5740267Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_aot_inductor.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'] ... [2024-12-18 00:16:55.573779] 2024-12-18T00:17:02.2172228Z 2024-12-18T00:17:02.2173481Z inductor/test_aot_inductor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_1.1_bc1e41ad58349eef_.log 2024-12-18T00:17:02.2174386Z 2024-12-18T00:17:02.2174867Z Running inductor/test_aot_inductor_arrayref 1/3 ... [2024-12-18 00:17:02.217332] 2024-12-18T00:17:02.2175364Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:02.2178685Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_aot_inductor_arrayref.py', '-m', 'serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:17:02.217640] 2024-12-18T00:17:09.0405903Z 2024-12-18T00:17:09.0407021Z inductor/test_aot_inductor_arrayref 1/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_arrayref_1.3_2d27281e8ad1ad58_.log 2024-12-18T00:17:09.0407979Z Running 0 items in this shard: 2024-12-18T00:17:09.0408183Z 2024-12-18T00:17:09.0410455Z Running inductor/test_aot_inductor_custom_ops 1/1 ... [2024-12-18 00:17:09.040902] 2024-12-18T00:17:09.0410944Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:09.0414023Z 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'] ... [2024-12-18 00:17:09.041200] 2024-12-18T00:17:15.5897580Z 2024-12-18T00:17:15.5898704Z 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_63ae54191c5f01a9_.log 2024-12-18T00:17:15.5899524Z 2024-12-18T00:17:15.5900629Z Running inductor/test_kernel_benchmark 1/1 ... [2024-12-18 00:17:15.589902] 2024-12-18T00:17:15.5901107Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:15.5904056Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_kernel_benchmark.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'] ... [2024-12-18 00:17:15.590204] 2024-12-18T00:17:18.6188562Z 2024-12-18T00:17:18.6189834Z inductor/test_kernel_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_kernel_benchmark_1.1_46ed2e6bb9faeb2b_.log 2024-12-18T00:17:18.6190641Z 2024-12-18T00:17:18.6191535Z Running inductor/test_torchinductor_opinfo 3/47 ... [2024-12-18 00:17:18.618997] 2024-12-18T00:17:18.6192091Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:18.6195148Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=3', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:17:18.619297] 2024-12-18T00:17:26.9444938Z 2024-12-18T00:17:26.9445932Z inductor/test_torchinductor_opinfo 3/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_3.47_2bb1180856c5d76c_.log 2024-12-18T00:17:26.9446857Z Running 0 items in this shard: 2024-12-18T00:17:26.9447054Z 2024-12-18T00:17:26.9448501Z Running inductor/test_torchinductor_opinfo 5/47 ... [2024-12-18 00:17:26.944679] 2024-12-18T00:17:26.9449007Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:26.9451899Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=5', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:17:26.944999] 2024-12-18T00:17:35.1699024Z 2024-12-18T00:17:35.1700100Z inductor/test_torchinductor_opinfo 5/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_5.47_1183c09d4757b90a_.log 2024-12-18T00:17:35.1701310Z Running 0 items in this shard: 2024-12-18T00:17:35.1701504Z 2024-12-18T00:17:35.1702899Z Running inductor/test_torchinductor_opinfo 6/47 ... [2024-12-18 00:17:35.170127] 2024-12-18T00:17:35.1703402Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:35.1707523Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=6', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:17:35.170461] 2024-12-18T00:17:43.4955482Z 2024-12-18T00:17:43.4956569Z inductor/test_torchinductor_opinfo 6/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_6.47_508db42d29e6e574_.log 2024-12-18T00:17:43.4957533Z Running 0 items in this shard: 2024-12-18T00:17:43.4957733Z 2024-12-18T00:17:43.4958646Z Running inductor/test_torchinductor_opinfo 18/47 ... [2024-12-18 00:17:43.495711] 2024-12-18T00:17:43.4959149Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:43.4962382Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=18', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:17:43.496028] 2024-12-18T00:17:51.7209938Z 2024-12-18T00:17:51.7211022Z inductor/test_torchinductor_opinfo 18/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_18.47_e38da3c51ffe507e_.log 2024-12-18T00:17:51.7211968Z Running 0 items in this shard: 2024-12-18T00:17:51.7212229Z 2024-12-18T00:17:51.7213308Z Running inductor/test_torchinductor_opinfo 20/47 ... [2024-12-18 00:17:51.721169] 2024-12-18T00:17:51.7214060Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:51.7216998Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=20', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:17:51.721479] 2024-12-18T00:18:00.0968918Z 2024-12-18T00:18:00.0970286Z inductor/test_torchinductor_opinfo 20/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_20.47_c4a05851f4136bd5_.log 2024-12-18T00:18:00.0971261Z Running 0 items in this shard: 2024-12-18T00:18:00.0971518Z 2024-12-18T00:18:00.0971792Z Running inductor/test_torchinductor_opinfo 21/47 ... [2024-12-18 00:18:00.097048] 2024-12-18T00:18:00.0972357Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:00.0976729Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=21', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:18:00.097327] 2024-12-18T00:18:08.4224751Z 2024-12-18T00:18:08.4225790Z inductor/test_torchinductor_opinfo 21/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_21.47_fab3ae5f40b91754_.log 2024-12-18T00:18:08.4226795Z Running 0 items in this shard: 2024-12-18T00:18:08.4227034Z 2024-12-18T00:18:08.4227374Z Running inductor/test_torchinductor_opinfo 33/47 ... [2024-12-18 00:18:08.422597] 2024-12-18T00:18:08.4227879Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:08.4230869Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=33', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:18:08.422887] 2024-12-18T00:18:16.6476563Z 2024-12-18T00:18:16.6477528Z inductor/test_torchinductor_opinfo 33/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_33.47_5bd5f039d53d7fd8_.log 2024-12-18T00:18:16.6478523Z Running 0 items in this shard: 2024-12-18T00:18:16.6478721Z 2024-12-18T00:18:16.6479616Z Running inductor/test_torchinductor_opinfo 35/47 ... [2024-12-18 00:18:16.647806] 2024-12-18T00:18:16.6480316Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:16.6482966Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=35', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:18:16.648097] 2024-12-18T00:18:24.9730808Z 2024-12-18T00:18:24.9732505Z inductor/test_torchinductor_opinfo 35/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_35.47_e2681c3cb0b308d0_.log 2024-12-18T00:18:24.9734078Z Running 0 items in this shard: 2024-12-18T00:18:24.9734406Z 2024-12-18T00:18:24.9734834Z Running inductor/test_torchinductor_opinfo 36/47 ... [2024-12-18 00:18:24.973234] 2024-12-18T00:18:24.9735633Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:24.9737372Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=36', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:18:24.973515] 2024-12-18T00:18:33.1982545Z 2024-12-18T00:18:33.1983696Z inductor/test_torchinductor_opinfo 36/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_36.47_e293dd28adb0c379_.log 2024-12-18T00:18:33.1984772Z Running 0 items in this shard: 2024-12-18T00:18:33.1985244Z 2024-12-18T00:18:33.1985509Z Running inductor/test_compiled_optimizers 1/1 ... [2024-12-18 00:18:33.198385] 2024-12-18T00:18:33.1986071Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:33.1989456Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_compiled_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'] ... [2024-12-18 00:18:33.198668] 2024-12-18T00:18:41.0732172Z 2024-12-18T00:18:41.0733256Z inductor/test_compiled_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compiled_optimizers_1.1_296c773f0e1cc4da_.log 2024-12-18T00:18:41.0734182Z Running 0 items in this shard: 2024-12-18T00:18:41.0734395Z 2024-12-18T00:18:41.0735333Z Running dynamo/test_base_output 1/1 ... [2024-12-18 00:18:41.073385] 2024-12-18T00:18:41.0735787Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:41.0738972Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.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'] ... [2024-12-18 00:18:41.073697] 2024-12-18T00:18:44.2419986Z 2024-12-18T00:18:44.2420998Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_f20f4db26702c286_.log 2024-12-18T00:18:44.2421826Z Running 0 items in this shard: 2024-12-18T00:18:44.2422058Z 2024-12-18T00:18:44.2423121Z Running inductor/test_cooperative_reductions 1/1 ... [2024-12-18 00:18:44.242161] 2024-12-18T00:18:44.2423635Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:44.2426653Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cooperative_reductions.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'] ... [2024-12-18 00:18:44.242463] 2024-12-18T00:18:47.2917893Z 2024-12-18T00:18:47.2919048Z inductor/test_cooperative_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cooperative_reductions_1.1_c70d9a4c2a055167_.log 2024-12-18T00:18:47.2919874Z 2024-12-18T00:18:47.2921057Z Running test_import_stats 1/1 ... [2024-12-18 00:18:47.291961] 2024-12-18T00:18:47.2921799Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:47.2924576Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_import_stats.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'] ... [2024-12-18 00:18:47.292258] 2024-12-18T00:18:50.4605672Z 2024-12-18T00:18:50.4606479Z test_import_stats 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_import_stats_1.1_2e86a298a8cb8297_.log 2024-12-18T00:18:50.4607262Z Running 0 items in this shard: 2024-12-18T00:18:50.4607467Z 2024-12-18T00:18:50.4608993Z Running test_binary_ufuncs 1/1 ... [2024-12-18 00:18:50.460746] 2024-12-18T00:18:50.4609417Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:50.4612472Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_binary_ufuncs.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'] ... [2024-12-18 00:18:50.461045] 2024-12-18T00:18:56.4328850Z 2024-12-18T00:18:56.4329676Z test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_binary_ufuncs_1.1_91b2d4aa8b2296ed_.log 2024-12-18T00:18:56.4330485Z Running 0 items in this shard: 2024-12-18T00:18:56.4330683Z 2024-12-18T00:18:56.4332377Z Running export/test_functionalized_assertions 1/1 ... [2024-12-18 00:18:56.433073] 2024-12-18T00:18:56.4333118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:56.4335803Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_functionalized_assertions.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'] ... [2024-12-18 00:18:56.433372] 2024-12-18T00:18:59.6016723Z 2024-12-18T00:18:59.6018032Z export/test_functionalized_assertions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_functionalized_assertions_1.1_2511bf079e95e20c_.log 2024-12-18T00:18:59.6019063Z Running 0 items in this shard: 2024-12-18T00:18:59.6019275Z 2024-12-18T00:18:59.6019818Z Running export/test_tree_utils 1/1 ... [2024-12-18 00:18:59.601816] 2024-12-18T00:18:59.6020249Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:18:59.6023005Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_tree_utils.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'] ... [2024-12-18 00:18:59.602111] 2024-12-18T00:19:02.8204911Z 2024-12-18T00:19:02.8205846Z export/test_tree_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_tree_utils_1.1_69f01727f66541cb_.log 2024-12-18T00:19:02.8206691Z Running 0 items in this shard: 2024-12-18T00:19:02.8206888Z 2024-12-18T00:19:02.8208120Z Running inductor/test_inductor_annotations 1/1 ... [2024-12-18 00:19:02.820633] 2024-12-18T00:19:02.8208602Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:02.8211303Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_annotations.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'] ... [2024-12-18 00:19:02.820926] 2024-12-18T00:19:06.0393219Z 2024-12-18T00:19:06.0394290Z inductor/test_inductor_annotations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_annotations_1.1_a709c2c06b083482_.log 2024-12-18T00:19:06.0395441Z Running 0 items in this shard: 2024-12-18T00:19:06.0395657Z 2024-12-18T00:19:06.0396173Z Running dynamo/test_nops 1/1 ... [2024-12-18 00:19:06.039474] 2024-12-18T00:19:06.0396589Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:06.0399793Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.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'] ... [2024-12-18 00:19:06.039759] 2024-12-18T00:19:09.2081458Z 2024-12-18T00:19:09.2082352Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_ea95fe93fb1e3b3c_.log 2024-12-18T00:19:09.2083107Z Running 0 items in this shard: 2024-12-18T00:19:09.2083324Z 2024-12-18T00:19:09.2084508Z Running inductor/test_utils 1/1 ... [2024-12-18 00:19:09.208302] 2024-12-18T00:19:09.2084969Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:09.2087806Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_utils.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'] ... [2024-12-18 00:19:09.208591] 2024-12-18T00:19:12.3769884Z 2024-12-18T00:19:12.3770841Z inductor/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_utils_1.1_58e47b7a9d118966_.log 2024-12-18T00:19:12.3771668Z Running 0 items in this shard: 2024-12-18T00:19:12.3771884Z 2024-12-18T00:19:12.3772697Z Running inductor/test_indexing 1/1 ... [2024-12-18 00:19:12.377115] 2024-12-18T00:19:12.3773139Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:12.3776168Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_indexing.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'] ... [2024-12-18 00:19:12.377423] 2024-12-18T00:19:18.4495906Z 2024-12-18T00:19:18.4497343Z inductor/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_indexing_1.1_2ad634cc23ccee78_.log 2024-12-18T00:19:18.4498971Z Running 0 items in this shard: 2024-12-18T00:19:18.4499320Z 2024-12-18T00:19:18.4500080Z Running inductor/test_pad_mm 1/1 ... [2024-12-18 00:19:18.449852] 2024-12-18T00:19:18.4500753Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:18.4505731Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_pad_mm.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'] ... [2024-12-18 00:19:18.450261] 2024-12-18T00:19:22.0733539Z 2024-12-18T00:19:22.0734433Z inductor/test_pad_mm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_pad_mm_1.1_7fea007fad0fd131_.log 2024-12-18T00:19:22.0735098Z 2024-12-18T00:19:22.0736624Z Running dynamo/test_utils 1/1 ... [2024-12-18 00:19:22.073526] 2024-12-18T00:19:22.0737054Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:22.0740192Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_utils.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'] ... [2024-12-18 00:19:22.073815] 2024-12-18T00:19:25.2420005Z 2024-12-18T00:19:25.2420946Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_6b525724a9b9ade1_.log 2024-12-18T00:19:25.2421712Z Running 0 items in this shard: 2024-12-18T00:19:25.2421943Z 2024-12-18T00:19:25.2423071Z Running inductor/test_compile_worker 1/1 ... [2024-12-18 00:19:25.242160] 2024-12-18T00:19:25.2423806Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:25.2426740Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_compile_worker.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'] ... [2024-12-18 00:19:25.242452] 2024-12-18T00:19:31.2144316Z 2024-12-18T00:19:31.2145373Z inductor/test_compile_worker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compile_worker_1.1_b8f8ff3f1e9b72ca_.log 2024-12-18T00:19:31.2146245Z Running 0 items in this shard: 2024-12-18T00:19:31.2146439Z 2024-12-18T00:19:31.2147565Z Running functorch/test_aotdispatch 1/5 ... [2024-12-18 00:19:31.214609] 2024-12-18T00:19:31.2148075Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:31.2151232Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'serial', '--shard-id=1', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:19:31.214913] 2024-12-18T00:19:36.0354366Z 2024-12-18T00:19:36.0355384Z functorch/test_aotdispatch 1/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_1.5_42f438e458420ba3_.log 2024-12-18T00:19:36.0356261Z Running 0 items in this shard: 2024-12-18T00:19:36.0356452Z 2024-12-18T00:19:36.0357646Z Running functorch/test_aotdispatch 3/5 ... [2024-12-18 00:19:36.035605] 2024-12-18T00:19:36.0358169Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:36.0361254Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'serial', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:19:36.035913] 2024-12-18T00:19:40.9065924Z 2024-12-18T00:19:40.9067099Z functorch/test_aotdispatch 3/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_3.5_4197a4bfb4fd0fd2_.log 2024-12-18T00:19:40.9068085Z Running 0 items in this shard: 2024-12-18T00:19:40.9068386Z 2024-12-18T00:19:40.9068977Z Running functorch/test_aotdispatch 5/5 ... [2024-12-18 00:19:40.906741] 2024-12-18T00:19:40.9069660Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:40.9072533Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'serial', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:19:40.907042] 2024-12-18T00:19:45.6774690Z 2024-12-18T00:19:45.6775713Z functorch/test_aotdispatch 5/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_5.5_b93c41e3a0fecf7e_.log 2024-12-18T00:19:45.6776613Z Running 0 items in this shard: 2024-12-18T00:19:45.6776807Z 2024-12-18T00:19:45.6777980Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2024-12-18 00:19:45.677648] 2024-12-18T00:19:45.6778515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:45.6781722Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.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'] ... [2024-12-18 00:19:45.677974] 2024-12-18T00:19:48.6698904Z 2024-12-18T00:19:48.6700040Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_977b8b1d7b8afacc_.log 2024-12-18T00:19:48.6700930Z 2024-12-18T00:19:48.6701652Z Running functorch/test_ops 2/6 ... [2024-12-18 00:19:48.670020] 2024-12-18T00:19:48.6702392Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:48.6704816Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ops.py', '-m', 'serial', '--shard-id=2', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:19:48.670295] 2024-12-18T00:19:54.3918926Z 2024-12-18T00:19:54.3920039Z functorch/test_ops 2/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_2.6_62fa5912ad99610c_.log 2024-12-18T00:19:54.3920860Z Running 0 items in this shard: 2024-12-18T00:19:54.3921125Z 2024-12-18T00:19:54.3922165Z Running functorch/test_ops 5/6 ... [2024-12-18 00:19:54.392063] 2024-12-18T00:19:54.3922647Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:19:54.3925578Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ops.py', '-m', 'serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:19:54.392362] 2024-12-18T00:20:00.0638174Z 2024-12-18T00:20:00.0639141Z functorch/test_ops 5/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_5.6_2d248f9387e24882_.log 2024-12-18T00:20:00.0639931Z Running 0 items in this shard: 2024-12-18T00:20:00.0640141Z 2024-12-18T00:20:00.0641463Z Running inductor/test_snode_runtime 1/1 ... [2024-12-18 00:20:00.063994] 2024-12-18T00:20:00.0641923Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:00.0644860Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_snode_runtime.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'] ... [2024-12-18 00:20:00.064283] 2024-12-18T00:20:03.7279348Z 2024-12-18T00:20:03.7280546Z inductor/test_snode_runtime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_snode_runtime_1.1_ffb35b4d24f6e828_.log 2024-12-18T00:20:03.7281355Z 2024-12-18T00:20:03.7282390Z Running inductor/test_perf 1/1 ... [2024-12-18 00:20:03.728089] 2024-12-18T00:20:03.7282870Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:03.7285980Z 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'] ... [2024-12-18 00:20:03.728381] 2024-12-18T00:20:07.4071241Z 2024-12-18T00:20:07.4072132Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_6c32703a2559c129_.log 2024-12-18T00:20:07.4072782Z 2024-12-18T00:20:07.4074034Z Running inductor/test_fp8 1/1 ... [2024-12-18 00:20:07.407246] 2024-12-18T00:20:07.4074489Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:07.4077435Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fp8.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'] ... [2024-12-18 00:20:07.407549] 2024-12-18T00:20:10.3822251Z 2024-12-18T00:20:10.3823118Z inductor/test_fp8 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fp8_1.1_5a7eb4421d9e2acd_.log 2024-12-18T00:20:10.3823756Z 2024-12-18T00:20:10.3825088Z Running test_ops_fwd_gradients 1/1 ... [2024-12-18 00:20:10.382359] 2024-12-18T00:20:10.3825525Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:10.3828430Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_fwd_gradients.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'] ... [2024-12-18 00:20:10.382656] 2024-12-18T00:20:14.7523711Z 2024-12-18T00:20:14.7524649Z test_ops_fwd_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.1_c84868606c66b936_.log 2024-12-18T00:20:14.7525446Z Running 0 items in this shard: 2024-12-18T00:20:14.7525642Z 2024-12-18T00:20:14.7527389Z Running inductor/test_cpu_select_algorithm 8/17 ... [2024-12-18 00:20:14.752560] 2024-12-18T00:20:14.7528111Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:14.7530740Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=8', '--num-shards=17', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:20:14.752878] 2024-12-18T00:20:21.9762777Z 2024-12-18T00:20:21.9763914Z inductor/test_cpu_select_algorithm 8/17 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_8.17_6e9964c444f3afd2_.log 2024-12-18T00:20:21.9764962Z Running 0 items in this shard: 2024-12-18T00:20:21.9765202Z 2024-12-18T00:20:21.9765690Z Running inductor/test_cpu_select_algorithm 11/17 ... [2024-12-18 00:20:21.976425] 2024-12-18T00:20:21.9766249Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:21.9769639Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=11', '--num-shards=17', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:20:21.976724] 2024-12-18T00:20:29.0999760Z 2024-12-18T00:20:29.1000931Z inductor/test_cpu_select_algorithm 11/17 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_11.17_b3680e7f4d9175b1_.log 2024-12-18T00:20:29.1001938Z Running 0 items in this shard: 2024-12-18T00:20:29.1002366Z 2024-12-18T00:20:29.1002982Z Running inductor/test_cpu_select_algorithm 15/17 ... [2024-12-18 00:20:29.100132] 2024-12-18T00:20:29.1003487Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:29.1006718Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=15', '--num-shards=17', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:20:29.100445] 2024-12-18T00:20:36.2238617Z 2024-12-18T00:20:36.2239543Z inductor/test_cpu_select_algorithm 15/17 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_15.17_c29a73d105ceaffc_.log 2024-12-18T00:20:36.2240450Z Running 0 items in this shard: 2024-12-18T00:20:36.2240717Z 2024-12-18T00:20:36.2241656Z Running inductor/test_halide 1/1 ... [2024-12-18 00:20:36.223996] 2024-12-18T00:20:36.2242097Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:36.2245009Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_halide.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'] ... [2024-12-18 00:20:36.224310] 2024-12-18T00:20:42.8909345Z 2024-12-18T00:20:42.8910362Z inductor/test_halide 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_halide_1.1_854bcb5dc5663cd0_.log 2024-12-18T00:20:42.8911041Z 2024-12-18T00:20:42.8912118Z Running inductor/test_unbacked_symints 1/1 ... [2024-12-18 00:20:42.891056] 2024-12-18T00:20:42.8912600Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:42.8915266Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_unbacked_symints.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'] ... [2024-12-18 00:20:42.891338] 2024-12-18T00:20:45.9335478Z 2024-12-18T00:20:45.9336708Z inductor/test_unbacked_symints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_unbacked_symints_1.1_a53335de250b554e_.log 2024-12-18T00:20:45.9337598Z 2024-12-18T00:20:45.9338289Z Running test_ops 2/6 ... [2024-12-18 00:20:45.933692] 2024-12-18T00:20:45.9338665Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:45.9341594Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=2', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:20:45.933961] 2024-12-18T00:20:55.2602377Z 2024-12-18T00:20:55.2603273Z test_ops 2/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_2.6_d52b559678fdb3f7_.log 2024-12-18T00:20:55.2603980Z Running 0 items in this shard: 2024-12-18T00:20:55.2604248Z 2024-12-18T00:20:55.2605380Z Running test_ops 6/6 ... [2024-12-18 00:20:55.260386] 2024-12-18T00:20:55.2605823Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:20:55.2608936Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:20:55.260691] 2024-12-18T00:21:04.6371052Z 2024-12-18T00:21:04.6371959Z test_ops 6/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_6.6_80ab9c282be58762_.log 2024-12-18T00:21:04.6372715Z Running 0 items in this shard: 2024-12-18T00:21:04.6372948Z 2024-12-18T00:21:04.6374466Z Running test_matmul_cuda 1/1 ... [2024-12-18 00:21:04.637280] 2024-12-18T00:21:04.6374912Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:04.6378184Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.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'] ... [2024-12-18 00:21:04.637610] 2024-12-18T00:21:07.8461725Z 2024-12-18T00:21:07.8462573Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_063fa5ffc903fe43_.log 2024-12-18T00:21:07.8463323Z Running 0 items in this shard: 2024-12-18T00:21:07.8463519Z 2024-12-18T00:21:07.8465132Z Running test_hub 1/1 ... [2024-12-18 00:21:07.846352] 2024-12-18T00:21:07.8465519Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:07.8468532Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.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'] ... [2024-12-18 00:21:07.846654] 2024-12-18T00:21:10.8578323Z 2024-12-18T00:21:10.8579163Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_69bb40f8fc1bd14a_.log 2024-12-18T00:21:10.8579768Z 2024-12-18T00:21:10.8581780Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2024-12-18 00:21:10.857961] 2024-12-18T00:21:10.8582316Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:10.8584475Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.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'] ... [2024-12-18 00:21:10.858243] 2024-12-18T00:21:14.0264965Z 2024-12-18T00:21:14.0266313Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_6235641c68c7876e_.log 2024-12-18T00:21:14.0267242Z Running 0 items in this shard: 2024-12-18T00:21:14.0267435Z 2024-12-18T00:21:14.0268035Z Running test_out_dtype_op 1/1 ... [2024-12-18 00:21:14.026652] 2024-12-18T00:21:14.0268864Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:14.0271523Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_out_dtype_op.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'] ... [2024-12-18 00:21:14.026933] 2024-12-18T00:21:17.4955660Z 2024-12-18T00:21:17.4956953Z test_out_dtype_op 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_out_dtype_op_1.1_333fbcbf2486a320_.log 2024-12-18T00:21:17.4957732Z Running 0 items in this shard: 2024-12-18T00:21:17.4957939Z 2024-12-18T00:21:17.4959232Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2024-12-18 00:21:17.495747] 2024-12-18T00:21:17.4959888Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:17.4962633Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.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'] ... [2024-12-18 00:21:17.496033] 2024-12-18T00:21:20.7144735Z 2024-12-18T00:21:20.7146088Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_367c34f8de676b47_.log 2024-12-18T00:21:20.7147091Z Running 0 items in this shard: 2024-12-18T00:21:20.7147299Z 2024-12-18T00:21:20.7148157Z Running test_logging 1/1 ... [2024-12-18 00:21:20.714614] 2024-12-18T00:21:20.7148794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:20.7151226Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', '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'] ... [2024-12-18 00:21:20.714898] 2024-12-18T00:21:23.8832740Z 2024-12-18T00:21:23.8833824Z test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_logging_1.1_15d0d2c6189ad487_.log 2024-12-18T00:21:23.8834562Z Running 0 items in this shard: 2024-12-18T00:21:23.8834758Z 2024-12-18T00:21:23.8835871Z Running test_namedtensor 1/1 ... [2024-12-18 00:21:23.883421] 2024-12-18T00:21:23.8836515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:23.8839646Z 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'] ... [2024-12-18 00:21:23.883713] 2024-12-18T00:21:27.1020665Z 2024-12-18T00:21:27.1021750Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_ff5b766174609020_.log 2024-12-18T00:21:27.1022499Z Running 0 items in this shard: 2024-12-18T00:21:27.1022732Z 2024-12-18T00:21:27.1024276Z Running distributions/test_constraints 1/1 ... [2024-12-18 00:21:27.102253] 2024-12-18T00:21:27.1024940Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:27.1027834Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_constraints.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'] ... [2024-12-18 00:21:27.102548] 2024-12-18T00:21:30.3210379Z 2024-12-18T00:21:30.3211686Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_a0b7b85afbb9371e_.log 2024-12-18T00:21:30.3212597Z Running 0 items in this shard: 2024-12-18T00:21:30.3212805Z 2024-12-18T00:21:30.3213092Z Running test_decomp 1/10 ... [2024-12-18 00:21:30.321168] 2024-12-18T00:21:30.3213774Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:30.3216603Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=1', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:21:30.321444] 2024-12-18T00:21:35.8929842Z 2024-12-18T00:21:35.8930942Z test_decomp 1/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.10_072a1b6edac77820_.log 2024-12-18T00:21:35.8931887Z Running 0 items in this shard: 2024-12-18T00:21:35.8932103Z 2024-12-18T00:21:35.8933129Z Running test_decomp 6/10 ... [2024-12-18 00:21:35.893158] 2024-12-18T00:21:35.8933773Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:35.8936758Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=6', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:21:35.893452] 2024-12-18T00:21:41.4651063Z 2024-12-18T00:21:41.4652135Z test_decomp 6/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_6.10_91cf63464b5d6af8_.log 2024-12-18T00:21:41.4653445Z Running 0 items in this shard: 2024-12-18T00:21:41.4653642Z 2024-12-18T00:21:41.4654396Z Running test_decomp 10/10 ... [2024-12-18 00:21:41.465271] 2024-12-18T00:21:41.4654794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:41.4657762Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=10', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:21:41.465575] 2024-12-18T00:21:46.9368690Z 2024-12-18T00:21:46.9369560Z test_decomp 10/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_10.10_8dfb977c5fc321e5_.log 2024-12-18T00:21:46.9370274Z Running 0 items in this shard: 2024-12-18T00:21:46.9370713Z 2024-12-18T00:21:46.9372169Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2024-12-18 00:21:46.937064] 2024-12-18T00:21:46.9372691Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:46.9375953Z 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'] ... [2024-12-18 00:21:46.937386] 2024-12-18T00:21:50.0556762Z 2024-12-18T00:21:50.0557830Z 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_d20f19f24d68fd14_.log 2024-12-18T00:21:50.0558884Z Running 0 items in this shard: 2024-12-18T00:21:50.0559199Z 2024-12-18T00:21:50.0559924Z Running test_functional_optim 1/1 ... [2024-12-18 00:21:50.055848] 2024-12-18T00:21:50.0560364Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:50.0563440Z 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'] ... [2024-12-18 00:21:50.056150] 2024-12-18T00:21:53.3746042Z 2024-12-18T00:21:53.3746973Z test_functional_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_optim_1.1_9ca54001fb83aa9a_.log 2024-12-18T00:21:53.3747786Z Running 0 items in this shard: 2024-12-18T00:21:53.3747994Z 2024-12-18T00:21:53.3749091Z Running nn/test_load_state_dict 1/1 ... [2024-12-18 00:21:53.374776] 2024-12-18T00:21:53.3749532Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:53.3752498Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_load_state_dict.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'] ... [2024-12-18 00:21:53.375067] 2024-12-18T00:21:56.5433603Z 2024-12-18T00:21:56.5434452Z nn/test_load_state_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_load_state_dict_1.1_8d2d2b28ee2b3498_.log 2024-12-18T00:21:56.5435257Z Running 0 items in this shard: 2024-12-18T00:21:56.5435451Z 2024-12-18T00:21:56.5436966Z Running torch_np/test_function_base 1/1 ... [2024-12-18 00:21:56.543528] 2024-12-18T00:21:56.5437430Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:56.5440573Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_function_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'] ... [2024-12-18 00:21:56.543827] 2024-12-18T00:21:59.7121963Z 2024-12-18T00:21:59.7123105Z torch_np/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_function_base_1.1_1aecb97de3db7e6e_.log 2024-12-18T00:21:59.7124008Z Running 0 items in this shard: 2024-12-18T00:21:59.7124217Z 2024-12-18T00:21:59.7124796Z Running test_mkldnn 1/1 ... [2024-12-18 00:21:59.712366] 2024-12-18T00:21:59.7125185Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:21:59.7128415Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mkldnn.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'] ... [2024-12-18 00:21:59.712657] 2024-12-18T00:22:03.0812087Z 2024-12-18T00:22:03.0812939Z test_mkldnn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_1.1_3a234631ea6ca662_.log 2024-12-18T00:22:03.0813658Z Running 0 items in this shard: 2024-12-18T00:22:03.0813856Z 2024-12-18T00:22:03.0815341Z Running test_xnnpack_integration 1/1 ... [2024-12-18 00:22:03.081388] 2024-12-18T00:22:03.0816055Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:03.0819079Z 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'] ... [2024-12-18 00:22:03.081692] 2024-12-18T00:22:06.2500262Z 2024-12-18T00:22:06.2501478Z test_xnnpack_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_xnnpack_integration_1.1_11e0d00120d7106f_.log 2024-12-18T00:22:06.2502315Z Running 0 items in this shard: 2024-12-18T00:22:06.2502510Z 2024-12-18T00:22:06.2503176Z Running test_indexing 1/1 ... [2024-12-18 00:22:06.250186] 2024-12-18T00:22:06.2503579Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:06.2506862Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_indexing.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'] ... [2024-12-18 00:22:06.250493] 2024-12-18T00:22:09.4189912Z 2024-12-18T00:22:09.4190796Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_7c0d67efdcc07cb9_.log 2024-12-18T00:22:09.4191531Z Running 0 items in this shard: 2024-12-18T00:22:09.4191737Z 2024-12-18T00:22:09.4193341Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2024-12-18 00:22:09.419177] 2024-12-18T00:22:09.4193863Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:09.4196830Z 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'] ... [2024-12-18 00:22:09.419481] 2024-12-18T00:22:12.5878423Z 2024-12-18T00:22:12.5879567Z 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_4cb56468b7daa162_.log 2024-12-18T00:22:12.5880749Z Running 0 items in this shard: 2024-12-18T00:22:12.5881016Z 2024-12-18T00:22:12.5881429Z Running test_datapipe 1/1 ... [2024-12-18 00:22:12.588012] 2024-12-18T00:22:12.5881836Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:12.5885009Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_datapipe.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'] ... [2024-12-18 00:22:12.588302] 2024-12-18T00:22:16.3074021Z 2024-12-18T00:22:16.3075053Z test_datapipe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_datapipe_1.1_5160c46b9e315aa9_.log 2024-12-18T00:22:16.3075777Z Running 0 items in this shard: 2024-12-18T00:22:16.3076003Z 2024-12-18T00:22:16.3077449Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2024-12-18 00:22:16.307567] 2024-12-18T00:22:16.3078106Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:16.3081061Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.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'] ... [2024-12-18 00:22:16.307876] 2024-12-18T00:22:19.4763659Z 2024-12-18T00:22:19.4765059Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_1e4de11286de0ad8_.log 2024-12-18T00:22:19.4766028Z Running 0 items in this shard: 2024-12-18T00:22:19.4766236Z 2024-12-18T00:22:19.4766686Z Running torch_np/test_ndarray_methods 1/1 ... [2024-12-18 00:22:19.476522] 2024-12-18T00:22:19.4767343Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:19.4770584Z 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'] ... [2024-12-18 00:22:19.476829] 2024-12-18T00:22:22.7453353Z 2024-12-18T00:22:22.7454830Z 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_68a2aeb101458f81_.log 2024-12-18T00:22:22.7455735Z Running 0 items in this shard: 2024-12-18T00:22:22.7455943Z 2024-12-18T00:22:22.7456644Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2024-12-18 00:22:22.745519] 2024-12-18T00:22:22.7457297Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:22.7460527Z 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'] ... [2024-12-18 00:22:22.745826] 2024-12-18T00:22:25.9642930Z 2024-12-18T00:22:25.9644288Z 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_bf8bb211475f3bc1_.log 2024-12-18T00:22:25.9645265Z Running 0 items in this shard: 2024-12-18T00:22:25.9645486Z 2024-12-18T00:22:25.9646477Z Running torch_np/numpy_tests/core/test_dlpack 1/1 ... [2024-12-18 00:22:25.964480] 2024-12-18T00:22:25.9647113Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:25.9650524Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_dlpack.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'] ... [2024-12-18 00:22:25.964814] 2024-12-18T00:22:29.1332309Z 2024-12-18T00:22:29.1333696Z torch_np/numpy_tests/core/test_dlpack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dlpack_1.1_632ac4ec6261a684_.log 2024-12-18T00:22:29.1334904Z Running 0 items in this shard: 2024-12-18T00:22:29.1335099Z 2024-12-18T00:22:29.1336633Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2024-12-18 00:22:29.133470] 2024-12-18T00:22:29.1337396Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:29.1340844Z 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'] ... [2024-12-18 00:22:29.133841] 2024-12-18T00:22:32.3020996Z 2024-12-18T00:22:32.3022295Z 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_ba3ecc281b57eadb_.log 2024-12-18T00:22:32.3023387Z Running 0 items in this shard: 2024-12-18T00:22:32.3023584Z 2024-12-18T00:22:32.3024151Z Running nn/test_module_hooks 1/1 ... [2024-12-18 00:22:32.302268] 2024-12-18T00:22:32.3024786Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:32.3027548Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_module_hooks.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'] ... [2024-12-18 00:22:32.302536] 2024-12-18T00:22:35.5208850Z 2024-12-18T00:22:35.5210055Z nn/test_module_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_module_hooks_1.1_15e0f0ac6571f832_.log 2024-12-18T00:22:35.5210845Z Running 0 items in this shard: 2024-12-18T00:22:35.5211040Z 2024-12-18T00:22:35.5211917Z Running test_function_schema 1/1 ... [2024-12-18 00:22:35.521038] 2024-12-18T00:22:35.5212715Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:35.5215260Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_function_schema.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'] ... [2024-12-18 00:22:35.521306] 2024-12-18T00:22:38.6895967Z 2024-12-18T00:22:38.6897272Z test_function_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_function_schema_1.1_1904919ad2c6bd33_.log 2024-12-18T00:22:38.6898074Z Running 0 items in this shard: 2024-12-18T00:22:38.6898567Z 2024-12-18T00:22:38.6899063Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2024-12-18 00:22:38.689742] 2024-12-18T00:22:38.6899692Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:38.6902362Z 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'] ... [2024-12-18 00:22:38.690011] 2024-12-18T00:22:41.8583757Z 2024-12-18T00:22:41.8585014Z 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_569e3068124328b2_.log 2024-12-18T00:22:41.8585964Z Running 0 items in this shard: 2024-12-18T00:22:41.8586160Z 2024-12-18T00:22:41.8586926Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2024-12-18 00:22:41.858521] 2024-12-18T00:22:41.8587564Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:41.8590329Z 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'] ... [2024-12-18 00:22:41.858798] 2024-12-18T00:22:45.0271488Z 2024-12-18T00:22:45.0272912Z 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_55e8a2d77ca1eb95_.log 2024-12-18T00:22:45.0274036Z Running 0 items in this shard: 2024-12-18T00:22:45.0274284Z 2024-12-18T00:22:45.0274546Z Running test_schema_check 1/1 ... [2024-12-18 00:22:45.027296] 2024-12-18T00:22:45.0275282Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:45.0278381Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_schema_check.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'] ... [2024-12-18 00:22:45.027589] 2024-12-18T00:22:49.9480446Z 2024-12-18T00:22:49.9481307Z test_schema_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_schema_check_1.1_7c89a6ed61119a8c_.log 2024-12-18T00:22:49.9482095Z Running 0 items in this shard: 2024-12-18T00:22:49.9482289Z 2024-12-18T00:22:49.9483605Z Running test_quantization 2/2 ... [2024-12-18 00:22:49.948213] 2024-12-18T00:22:49.9484025Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:49.9486824Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.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'] ... [2024-12-18 00:22:49.948489] 2024-12-18T00:22:53.9176797Z 2024-12-18T00:22:53.9177985Z test_quantization 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_2.2_54eaa98594610f27_.log 2024-12-18T00:22:53.9178806Z Running 0 items in this shard: 2024-12-18T00:22:53.9179001Z 2024-12-18T00:22:53.9180115Z Running optim/test_lrscheduler 1/1 ... [2024-12-18 00:22:53.917840] 2024-12-18T00:22:53.9180915Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:53.9183747Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_lrscheduler.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'] ... [2024-12-18 00:22:53.918139] 2024-12-18T00:22:56.9018300Z 2024-12-18T00:22:56.9019692Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_f8f3ec7936323ef4_.log 2024-12-18T00:22:56.9020423Z 2024-12-18T00:22:56.9128941Z Running inductor/test_aot_inductor 1/1 ... [2024-12-18 00:22:56.912662] 2024-12-18T00:22:56.9129604Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:56.9133572Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_aot_inductor.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'] ... [2024-12-18 00:22:56.913101] 2024-12-18T00:22:56.9186402Z Running inductor/test_aot_inductor_arrayref 1/3 ... [2024-12-18 00:22:56.918405] 2024-12-18T00:22:56.9187122Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:56.9188930Z Running inductor/test_aot_inductor_custom_ops 1/1 ... [2024-12-18 00:22:56.918692] 2024-12-18T00:22:56.9189588Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:22:56.9191619Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_aot_inductor_arrayref.py', '-m', 'not serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:22:56.918930] 2024-12-18T00:22:56.9194036Z 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'] ... [2024-12-18 00:22:56.919077] 2024-12-18T00:23:03.8029827Z 2024-12-18T00:23:03.8030938Z 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_6e4da3d7cd8c0bdf_.log 2024-12-18T00:23:03.8031766Z 2024-12-18T00:23:04.6098171Z Uploading artifacts took 0.81 seconds 2024-12-18T00:23:06.9481474Z 2024-12-18T00:23:06.9482740Z inductor/test_aot_inductor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_aot_inductor_1.1_15241430fcfa4086_.log 2024-12-18T00:23:06.9483475Z 2024-12-18T00:23:07.1814962Z Running inductor/test_kernel_benchmark 1/1 ... [2024-12-18 00:23:07.181136] 2024-12-18T00:23:07.1815494Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:23:07.1817495Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_kernel_benchmark.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'] ... [2024-12-18 00:23:07.181508] 2024-12-18T00:23:10.3541735Z Running inductor/test_torchinductor_opinfo 3/47 ... [2024-12-18 00:23:10.353798] 2024-12-18T00:23:10.3542335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:23:10.3543888Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=3', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:23:10.354154] 2024-12-18T00:23:10.3691512Z 2024-12-18T00:23:10.3692665Z inductor/test_kernel_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_kernel_benchmark_1.1_e5a3988804746ce5_.log 2024-12-18T00:23:10.3693448Z 2024-12-18T00:23:13.7059491Z Running inductor/test_torchinductor_opinfo 5/47 ... [2024-12-18 00:23:13.705558] 2024-12-18T00:23:13.7060303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:23:13.7061814Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=5', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:23:13.705936] 2024-12-18T00:33:01.3512764Z 2024-12-18T00:33:01.3514467Z inductor/test_torchinductor_opinfo 5/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_5.47_da82bc1ed6bcdaf6_.log 2024-12-18T00:33:01.3550210Z Running 70 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amax_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_complex_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erf_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftshift_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftshift_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft2_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_igammac_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_isnan_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kron_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ldexp_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vander_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_not_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_var_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_pool2d_with_indices_backward_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_minimum_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mv_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_batch_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_strided_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cross_entropy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardshrink_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardtanh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hinge_embedding_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bicubic_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_l1_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_local_response_norm_cpu_float32, 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_multi_margin_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_circular_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_poisson_nll_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_soft_margin_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softsign_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_unfold_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nonzero_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_fro_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_nuc_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_prod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reciprocal_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sigmoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_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_polygamma_special_polygamma_n_0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trunc_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_float32 2024-12-18T00:33:01.3585073Z 2024-12-18T00:33:04.6734050Z Running inductor/test_torchinductor_opinfo 6/47 ... [2024-12-18 00:33:04.672979] 2024-12-18T00:33:04.6734716Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:33:04.6735936Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=6', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:33:04.673313] 2024-12-18T00:35:02.8217443Z 2024-12-18T00:35:02.8218710Z inductor/test_torchinductor_opinfo 3/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_3.47_79a5bcfa60483555_.log 2024-12-18T00:35:02.8261441Z Running 84 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_or_cpu_uint8, 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_chalf_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_count_nonzero_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_embed_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_like_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfc_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftshift_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfftn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fliplr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geometric_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_igamma_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isfinite_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isposinf_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lerp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_matrix_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_qr_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logit_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_amax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_sum_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_binary_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_multinomial_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanquantile_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_alpha_dropout_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool1d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_fractional_max_pool3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_area_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_linear_cpu_float32, 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_pixel_shuffle_cpu_float16, 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_poisson_nll_loss_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_upsample_bilinear_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_upsample_nearest_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_in_place_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ravel_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reciprocal_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sign_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_signal_windows_nuttall_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_scatter_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_legendre_polynomial_p_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtri_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtri_cpu_float64, 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_shifted_chebyshev_polynomial_u_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_sparse_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unflatten_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unflatten_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vdot_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_xlogy_cpu_float64 2024-12-18T00:35:02.8302986Z 2024-12-18T00:35:06.1333160Z Running inductor/test_torchinductor_opinfo 18/47 ... [2024-12-18 00:35:06.132977] 2024-12-18T00:35:06.1333707Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:35:06.1335439Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=18', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:35:06.133312] 2024-12-18T00:40:41.4024556Z 2024-12-18T00:40:41.4025844Z PRINTING LOG FILE of inductor/test_aot_inductor_arrayref 1/3 (test/test-reports/inductor.test_aot_inductor_arrayref_1.3_5e5c281f794ddef9_.log) 2024-12-18T00:40:41.4027588Z Test results will be stored in test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-ae45d13d3a9f9745.xml 2024-12-18T00:40:41.4028619Z ============================= test session starts ============================== 2024-12-18T00:40:41.4029278Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:41.4029849Z cachedir: .pytest_cache 2024-12-18T00:40:41.4030502Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:41.4031262Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:41.4031651Z configfile: pytest.ini 2024-12-18T00:40:41.4032359Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:41.4033391Z collecting ... collected 183 items 2024-12-18T00:40:41.4033825Z stepcurrent: Cannot find last run test, not skipping 2024-12-18T00:40:41.4085237Z Running 68 items in this shard: test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_amp_fallback_random_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printer_codegen_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printing_model_inputs_codegen_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_buffer_mutation_1_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_buffer_mutation_3_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_non_tensor_predicates_dynamic_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_share_predicte_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_simple_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_symint_input_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_use_buffers_from_outer_scope_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_with_outer_code_before_after_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_with_parameters_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_consecutive_compiles_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_convolution_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_cat_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_scalar_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fake_tensor_device_validation_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fallback_kernel_with_symexpr_output_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fp8_view_of_param_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fqn_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_freezing_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_fallback_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_with_none_index_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_inf_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_weight_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_masked_select_dynamic_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_nan_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_no_args_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_contiguous_output_alias_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_default_gpu_device_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_tensor_input_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_none_args_aot_codegen_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_output_misaligned_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_output_path_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_quanatized_int8_linear_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_return_constant_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_reuse_kernel_dynamic_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_run_with_grad_enabled_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_dtype_failed_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_shape_failed_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_scatter_fallback_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sdpa_2_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_seq_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_shifted_constraint_ranges_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_small_constant_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_torchvision_transforms_functional_tensor_resize_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_dynamic_shape_with_div_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_False_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_reinterpret_view_mem_leak_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_sympy_fn_like_arg_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_True_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_True_autotuning_False_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_with_none_input_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_unsupported_input_dtype_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_nested_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_simple_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_outer_code_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_no_triton_profiler_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_offset_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_profiler_cpu_with_stack_allocation, test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation 2024-12-18T00:40:41.4133291Z 2024-12-18T00:40:41.4134449Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_amp_fallback_random_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:23:15.571000 11652 site-packages/torch/_export/__init__.py:277] +============================+ 2024-12-18T00:40:41.4136027Z W1218 00:23:15.572000 11652 site-packages/torch/_export/__init__.py:278] | !!! WARNING !!! | 2024-12-18T00:40:41.4136786Z W1218 00:23:15.572000 11652 site-packages/torch/_export/__init__.py:279] +============================+ 2024-12-18T00:40:41.4138273Z W1218 00:23:15.572000 11652 site-packages/torch/_export/__init__.py:280] torch._export.aot_compile()/torch._export.aot_load() is being deprecated, please switch to directly calling torch._inductor.aoti_compile_and_package(torch.export.export())/torch._inductor.aoti_load_package() instead. 2024-12-18T00:40:41.4139550Z PASSED [10.8872s] [ 1%] 2024-12-18T00:40:41.4140607Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printer_codegen_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [20.1960s] [ 2%] 2024-12-18T00:40:41.4142709Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printing_model_inputs_codegen_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0033s] (requires CUDA) [ 4%] 2024-12-18T00:40:41.4144758Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_buffer_mutation_1_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 5%] 2024-12-18T00:40:41.4146659Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_buffer_mutation_3_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 7%] 2024-12-18T00:40:41.4148628Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_non_tensor_predicates_dynamic_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (Skipped!) [ 8%] 2024-12-18T00:40:41.4150596Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_share_predicte_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 10%] 2024-12-18T00:40:41.4152972Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_simple_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:23:35.902000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4155017Z W1218 00:23:35.902000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4155810Z PASSED [10.4308s] [ 11%] 2024-12-18T00:40:41.4156800Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_symint_input_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.4299s] [ 13%] 2024-12-18T00:40:41.4159332Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_use_buffers_from_outer_scope_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:23:56.739000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4161517Z W1218 00:23:56.740000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4162823Z W1218 00:23:56.740000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4163613Z PASSED [10.4027s] [ 14%] 2024-12-18T00:40:41.4165229Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_with_outer_code_before_after_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:24:07.122000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4167493Z W1218 00:24:07.123000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4168287Z PASSED [10.3426s] [ 16%] 2024-12-18T00:40:41.4169863Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_with_parameters_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:24:17.633000 11652 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4171468Z PASSED [11.1202s] [ 17%] 2024-12-18T00:40:41.4172495Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_consecutive_compiles_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.4524s] [ 19%] 2024-12-18T00:40:41.4174741Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_convolution_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.0611s] [ 20%] 2024-12-18T00:40:41.4176482Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_cat_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.0877s] [ 22%] 2024-12-18T00:40:41.4178990Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_scalar_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpvkkgpb4u/c7i5lqgiqlmv4idwxsjrqmsvd46uj5fkzsspijrpot2vur3xj2nr/cgwxkcjidpqp5uqsuzt6l47iiziydbfczqq4qrudhlbttlxq6ilj.cpp:369: 2024-12-18T00:40:41.4182526Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4184683Z /tmp/tmpvkkgpb4u/c7i5lqgiqlmv4idwxsjrqmsvd46uj5fkzsspijrpot2vur3xj2nr/cgwxkcjidpqp5uqsuzt6l47iiziydbfczqq4qrudhlbttlxq6ilj.cpp:664:44: required from here 2024-12-18T00:40:41.4186816Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4188091Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4188416Z PASSED [10.5227s] [ 23%] 2024-12-18T00:40:41.4189557Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fake_tensor_device_validation_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0033s] (requires GPU) [ 25%] 2024-12-18T00:40:41.4191589Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fallback_kernel_with_symexpr_output_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0030s] (requires GPU) [ 26%] 2024-12-18T00:40:41.4193674Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fp8_view_of_param_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (FP8 is only supported on H100+) [ 27%] 2024-12-18T00:40:41.4196261Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fqn_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpali60uh9/cc7nlmpi4k6tptfjzl2rrppxl5bjye65euahlyuq4n7e2twxq6am/c6jer3ecdpsjugqt6qevlnufswzsmjuhqnejttjc3iq7asamhg45.cpp:369: 2024-12-18T00:40:41.4199539Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4201694Z /tmp/tmpali60uh9/cc7nlmpi4k6tptfjzl2rrppxl5bjye65euahlyuq4n7e2twxq6am/c6jer3ecdpsjugqt6qevlnufswzsmjuhqnejttjc3iq7asamhg45.cpp:621:44: required from here 2024-12-18T00:40:41.4203817Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4205095Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4205423Z PASSED [10.0682s] [ 29%] 2024-12-18T00:40:41.4206387Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_freezing_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.3952s] [ 30%] 2024-12-18T00:40:41.4208146Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_fallback_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.0703s] [ 32%] 2024-12-18T00:40:41.4210017Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_with_none_index_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1419s] [ 33%] 2024-12-18T00:40:41.4212018Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_inf_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (Skip this test, only for local test. SIGABRT is produced.) [ 35%] 2024-12-18T00:40:41.4213950Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_weight_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [17.3695s] [ 36%] 2024-12-18T00:40:41.4215731Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_masked_select_dynamic_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1309s] [ 38%] 2024-12-18T00:40:41.4217696Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_nan_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0003s] (Skip this test, only for local test. SIGABRT is produced.) [ 39%] 2024-12-18T00:40:41.4220267Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_no_args_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmp6wou_duu/cy6pckxweidyslnbvcvrpsyyone6bqp7ip735pogeblawin3eyyg/cxuugnxtvwgdxzbox7vobkbrgwmqdzvjbbxxcfirqxzmz2imur7g.cpp:369: 2024-12-18T00:40:41.4223451Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4225611Z /tmp/tmp6wou_duu/cy6pckxweidyslnbvcvrpsyyone6bqp7ip735pogeblawin3eyyg/cxuugnxtvwgdxzbox7vobkbrgwmqdzvjbbxxcfirqxzmz2imur7g.cpp:591:44: required from here 2024-12-18T00:40:41.4227741Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4229012Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4229334Z PASSED [10.0510s] [ 41%] 2024-12-18T00:40:41.4230370Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_contiguous_output_alias_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.0230s] [ 42%] 2024-12-18T00:40:41.4232335Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_default_gpu_device_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (requires multiple cuda devices) [ 44%] 2024-12-18T00:40:41.4234621Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_tensor_input_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:26:35.937000 11652 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4236294Z W1218 00:26:35.960000 11652 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4237169Z W1218 00:26:45.462000 11652 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4238042Z W1218 00:26:45.482000 11652 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4238699Z PASSED [19.3822s] [ 45%] 2024-12-18T00:40:41.4239834Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_none_args_aot_codegen_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0032s] (requires GPU) [ 47%] 2024-12-18T00:40:41.4242423Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_output_misaligned_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpgawq0eok/ckdk5wu6gdihwg3is4c2ndwu3tfejgy6a7a5bdfvkifwfua6ibn2/cqwk7qjgclydbzcehl5qp6xkpppebaekmxrl46r3rggpgzeuqsac.cpp:369: 2024-12-18T00:40:41.4245699Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4247843Z /tmp/tmpgawq0eok/ckdk5wu6gdihwg3is4c2ndwu3tfejgy6a7a5bdfvkifwfua6ibn2/cqwk7qjgclydbzcehl5qp6xkpppebaekmxrl46r3rggpgzeuqsac.cpp:627:44: required from here 2024-12-18T00:40:41.4249957Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4251227Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4251560Z PASSED [10.0335s] [ 48%] 2024-12-18T00:40:41.4252550Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_output_path_2_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1149s] [ 50%] 2024-12-18T00:40:41.4256063Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_quanatized_int8_linear_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp: In member function ‘void torch::aot_inductor::AOTInductorModel::run_impl(AtenTensorOpaque**, AtenTensorOpaque**, torch::aot_inductor::DeviceStreamType, AOTIProxyExecutorHandle)’: 2024-12-18T00:40:41.4259724Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:728:10: warning: variable ‘L__self___input_scale’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4261057Z 728 | auto L__self___input_scale = constants_->at(0); 2024-12-18T00:40:41.4261437Z | ^~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4262858Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:729:10: warning: variable ‘L__self___input_zero_point’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4264169Z 729 | auto L__self___input_zero_point = constants_->at(1); 2024-12-18T00:40:41.4264566Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4265949Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:730:10: warning: variable ‘L__self___weight’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4267195Z 730 | auto L__self___weight = constants_->at(2); 2024-12-18T00:40:41.4267551Z | ^~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4268940Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:731:10: warning: variable ‘L__self___weight_scale’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4270229Z 731 | auto L__self___weight_scale = constants_->at(3); 2024-12-18T00:40:41.4270606Z | ^~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4272012Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:732:10: warning: variable ‘L__self___weight_zero_point’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4273375Z 732 | auto L__self___weight_zero_point = constants_->at(4); 2024-12-18T00:40:41.4273979Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4275372Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:733:10: warning: variable ‘L__self___bias’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4276685Z 733 | auto L__self___bias = constants_->at(5); 2024-12-18T00:40:41.4277032Z | ^~~~~~~~~~~~~~ 2024-12-18T00:40:41.4278472Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:734:10: warning: variable ‘L__self___output_scale’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4279734Z 734 | auto L__self___output_scale = constants_->at(6); 2024-12-18T00:40:41.4280103Z | ^~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4281536Z /tmp/tmplowx12c8/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/c3je33zyoifpl6ndv3bzmbmvpxy2t5jv24r4cvvrsn26ndpiludz.cpp:735:10: warning: variable ‘L__self___output_zero_point’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4282855Z 735 | auto L__self___output_zero_point = constants_->at(7); 2024-12-18T00:40:41.4283248Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4283567Z PASSED [9.3362s] [ 51%] 2024-12-18T00:40:41.4284555Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_return_constant_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.0931s] [ 52%] 2024-12-18T00:40:41.4286341Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_reuse_kernel_dynamic_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [11.7515s] [ 54%] 2024-12-18T00:40:41.4288142Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_run_with_grad_enabled_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.2074s] [ 55%] 2024-12-18T00:40:41.4290245Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_dtype_failed_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py Error: input_handles[0]: unmatched dtype, expected: 5(at::kHalf), but got: 6 2024-12-18T00:40:41.4291503Z 2024-12-18T00:40:41.4291611Z PASSED [9.8668s] [ 57%] 2024-12-18T00:40:41.4292865Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_shape_failed_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py Error: input_handles[0]: unmatched dim value at 1, expected: 4, but got: 8 2024-12-18T00:40:41.4294057Z 2024-12-18T00:40:41.4294296Z Error: input_handles[0]: unmatched stride value at 1, expected: 4, but got: 1 2024-12-18T00:40:41.4294665Z 2024-12-18T00:40:41.4294937Z Error: input_handles[0]: dim value is too large at 0, expected to be <= 1024, but got: 2048 2024-12-18T00:40:41.4295338Z 2024-12-18T00:40:41.4295649Z Error: input_handles[0]: dim value is too large at 0, expected to be <= 1024, but got: 2048 2024-12-18T00:40:41.4296041Z 2024-12-18T00:40:41.4296151Z PASSED [8.6521s] [ 58%] 2024-12-18T00:40:41.4297208Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_scatter_fallback_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 60%] 2024-12-18T00:40:41.4299107Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sdpa_2_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (bfloat16 only supported in sm80+) [ 61%] 2024-12-18T00:40:41.4301595Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_seq_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpciq3iifp/cpt2qkjbtlwf4o6h7sbqkwf5eqnfvfdcbvk6sqw3yvecy7cwtkrh/cbliqqrky6sq46fwwmw22vfk2wucndxr5lchnc2xbwr6i5lhnjx3.cpp:369: 2024-12-18T00:40:41.4304909Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4307071Z /tmp/tmpciq3iifp/cpt2qkjbtlwf4o6h7sbqkwf5eqnfvfdcbvk6sqw3yvecy7cwtkrh/cbliqqrky6sq46fwwmw22vfk2wucndxr5lchnc2xbwr6i5lhnjx3.cpp:682:44: required from here 2024-12-18T00:40:41.4309196Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4310485Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4310813Z PASSED [10.2639s] [ 63%] 2024-12-18T00:40:41.4311913Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_shifted_constraint_ranges_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 64%] 2024-12-18T00:40:41.4313763Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.0974s] [ 66%] 2024-12-18T00:40:41.4315498Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_small_constant_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.1311s] [ 67%] 2024-12-18T00:40:41.4317414Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_torchvision_transforms_functional_tensor_resize_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [11.9672s] [ 69%] 2024-12-18T00:40:41.4319588Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_dynamic_shape_with_div_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0049s] (requires GPU) [ 70%] 2024-12-18T00:40:41.4321778Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0045s] (requires GPU) [ 72%] 2024-12-18T00:40:41.4324003Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 73%] 2024-12-18T00:40:41.4326241Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_False_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 75%] 2024-12-18T00:40:41.4328473Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 76%] 2024-12-18T00:40:41.4330718Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_False_autotune_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0043s] (requires GPU) [ 77%] 2024-12-18T00:40:41.4332968Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 79%] 2024-12-18T00:40:41.4335252Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 80%] 2024-12-18T00:40:41.4337464Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_reinterpret_view_mem_leak_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0044s] (requires GPU) [ 82%] 2024-12-18T00:40:41.4339520Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_sympy_fn_like_arg_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0052s] (requires GPU) [ 83%] 2024-12-18T00:40:41.4341579Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0043s] (requires GPU) [ 85%] 2024-12-18T00:40:41.4343780Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_True_autotuning_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 86%] 2024-12-18T00:40:41.4345942Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_with_none_input_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0044s] (requires GPU) [ 88%] 2024-12-18T00:40:41.4347868Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_unsupported_input_dtype_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py PASSED [0.0732s] [ 89%] 2024-12-18T00:40:41.4349728Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_nested_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0003s] (Skipped!) [ 91%] 2024-12-18T00:40:41.4351649Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_simple_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 92%] 2024-12-18T00:40:41.4353584Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_outer_code_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 94%] 2024-12-18T00:40:41.4355471Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_no_triton_profiler_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.0054s] [ 95%] 2024-12-18T00:40:41.4357350Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_offset_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.2720s] [ 97%] 2024-12-18T00:40:41.4359166Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_profiler_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1070s] [ 98%] 2024-12-18T00:40:41.4361020Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [100%] 2024-12-18T00:40:41.4362077Z 2024-12-18T00:40:41.4362814Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-ae45d13d3a9f9745.xml - 2024-12-18T00:40:41.4363846Z ================== 36 passed, 32 skipped in 379.76s (0:06:19) ================== 2024-12-18T00:40:41.4364257Z Got exit code -11 (SIGSEGV) 2024-12-18T00:40:41.4364550Z Retrying single test... 2024-12-18T00:40:41.4365391Z Test results will be stored in test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-deb7cba2c0539f20.xml 2024-12-18T00:40:41.4366251Z ============================= test session starts ============================== 2024-12-18T00:40:41.4366828Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:41.4367366Z cachedir: .pytest_cache 2024-12-18T00:40:41.4368032Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:41.4368723Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:41.4369043Z configfile: pytest.ini 2024-12-18T00:40:41.4369685Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:41.4370378Z collecting ... collected 183 items 2024-12-18T00:40:41.4370754Z stepcurrent: Cannot find last run test, not skipping 2024-12-18T00:40:41.4371142Z Running 68 items in this shard 2024-12-18T00:40:41.4371335Z 2024-12-18T00:40:41.4372495Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_amp_fallback_random_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:31:58.901000 25539 site-packages/torch/_export/__init__.py:277] +============================+ 2024-12-18T00:40:41.4374269Z W1218 00:31:58.902000 25539 site-packages/torch/_export/__init__.py:278] | !!! WARNING !!! | 2024-12-18T00:40:41.4375033Z W1218 00:31:58.902000 25539 site-packages/torch/_export/__init__.py:279] +============================+ 2024-12-18T00:40:41.4376531Z W1218 00:31:58.902000 25539 site-packages/torch/_export/__init__.py:280] torch._export.aot_compile()/torch._export.aot_load() is being deprecated, please switch to directly calling torch._inductor.aoti_compile_and_package(torch.export.export())/torch._inductor.aoti_load_package() instead. 2024-12-18T00:40:41.4377822Z PASSED [10.5126s] [ 1%] 2024-12-18T00:40:41.4378948Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printer_codegen_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [20.0424s] [ 2%] 2024-12-18T00:40:41.4380983Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_aoti_debug_printing_model_inputs_codegen_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0054s] (requires CUDA) [ 4%] 2024-12-18T00:40:41.4382989Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_buffer_mutation_1_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0003s] (Skipped!) [ 5%] 2024-12-18T00:40:41.4384856Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_buffer_mutation_3_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0003s] (Skipped!) [ 7%] 2024-12-18T00:40:41.4386815Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_non_tensor_predicates_dynamic_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0003s] (Skipped!) [ 8%] 2024-12-18T00:40:41.4388792Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_share_predicte_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0003s] (Skipped!) [ 10%] 2024-12-18T00:40:41.4391162Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_simple_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:32:19.095000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4393220Z W1218 00:32:19.095000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4394085Z PASSED [10.4219s] [ 11%] 2024-12-18T00:40:41.4395091Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_symint_input_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.4203s] [ 13%] 2024-12-18T00:40:41.4397539Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_use_buffers_from_outer_scope_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:32:39.873000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4399730Z W1218 00:32:39.874000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4401002Z W1218 00:32:39.874000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4401783Z PASSED [10.3712s] [ 14%] 2024-12-18T00:40:41.4403401Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_with_outer_code_before_after_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:32:50.247000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4405520Z W1218 00:32:50.248000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4406313Z PASSED [10.4651s] [ 16%] 2024-12-18T00:40:41.4407877Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_cond_with_parameters_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:33:00.882000 25539 site-packages/torch/export/dynamic_shapes.py:673] Using None as a dynamic shape dimension is deprecated. Please use Dim.STATIC instead 2024-12-18T00:40:41.4409515Z PASSED [11.3592s] [ 17%] 2024-12-18T00:40:41.4410541Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_consecutive_compiles_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.5672s] [ 19%] 2024-12-18T00:40:41.4412347Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_convolution_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.0537s] [ 20%] 2024-12-18T00:40:41.4414086Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_cat_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1184s] [ 22%] 2024-12-18T00:40:41.4416503Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_dynamic_scalar_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpf9p6xj10/c7i5lqgiqlmv4idwxsjrqmsvd46uj5fkzsspijrpot2vur3xj2nr/c5cy5lk34krar5ue3ishm5i72q4prvklhrayhrnnk6ubuwgkigdu.cpp:369: 2024-12-18T00:40:41.4419874Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4422001Z /tmp/tmpf9p6xj10/c7i5lqgiqlmv4idwxsjrqmsvd46uj5fkzsspijrpot2vur3xj2nr/c5cy5lk34krar5ue3ishm5i72q4prvklhrayhrnnk6ubuwgkigdu.cpp:664:44: required from here 2024-12-18T00:40:41.4424115Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4425427Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4425754Z PASSED [10.4595s] [ 23%] 2024-12-18T00:40:41.4426877Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fake_tensor_device_validation_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0054s] (requires GPU) [ 25%] 2024-12-18T00:40:41.4428945Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fallback_kernel_with_symexpr_output_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0051s] (requires GPU) [ 26%] 2024-12-18T00:40:41.4430971Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fp8_view_of_param_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (FP8 is only supported on H100+) [ 27%] 2024-12-18T00:40:41.4433498Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_fqn_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpwmgpvpk9/cc7nlmpi4k6tptfjzl2rrppxl5bjye65euahlyuq4n7e2twxq6am/cfg2envpwgwmootvp2pfz6hdnmyuzizlmgvwidmpdgkpcdvczdzd.cpp:369: 2024-12-18T00:40:41.4436701Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4438932Z /tmp/tmpwmgpvpk9/cc7nlmpi4k6tptfjzl2rrppxl5bjye65euahlyuq4n7e2twxq6am/cfg2envpwgwmootvp2pfz6hdnmyuzizlmgvwidmpdgkpcdvczdzd.cpp:621:44: required from here 2024-12-18T00:40:41.4441058Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4442384Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4442698Z PASSED [10.0750s] [ 29%] 2024-12-18T00:40:41.4443678Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_freezing_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.2199s] [ 30%] 2024-12-18T00:40:41.4445476Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_fallback_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.0871s] [ 32%] 2024-12-18T00:40:41.4447298Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_index_put_with_none_index_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1826s] [ 33%] 2024-12-18T00:40:41.4449266Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_inf_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (Skip this test, only for local test. SIGABRT is produced.) [ 35%] 2024-12-18T00:40:41.4451191Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_large_weight_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [17.0155s] [ 36%] 2024-12-18T00:40:41.4452983Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_masked_select_dynamic_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.1127s] [ 38%] 2024-12-18T00:40:41.4454963Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_nan_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (Skip this test, only for local test. SIGABRT is produced.) [ 39%] 2024-12-18T00:40:41.4457640Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_no_args_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmp3qr6z7zw/cy6pckxweidyslnbvcvrpsyyone6bqp7ip735pogeblawin3eyyg/ctcwejz6nyrkyna46y547vdenyrfe4bpjixa53qnd73fe4p3hfds.cpp:369: 2024-12-18T00:40:41.4460957Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4463089Z /tmp/tmp3qr6z7zw/cy6pckxweidyslnbvcvrpsyyone6bqp7ip735pogeblawin3eyyg/ctcwejz6nyrkyna46y547vdenyrfe4bpjixa53qnd73fe4p3hfds.cpp:591:44: required from here 2024-12-18T00:40:41.4465195Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4466476Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4466793Z PASSED [10.1657s] [ 41%] 2024-12-18T00:40:41.4467850Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_contiguous_output_alias_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.2092s] [ 42%] 2024-12-18T00:40:41.4469826Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_default_gpu_device_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (requires multiple cuda devices) [ 44%] 2024-12-18T00:40:41.4472109Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_non_tensor_input_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py W1218 00:35:19.321000 25539 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4473990Z W1218 00:35:19.344000 25539 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4474960Z W1218 00:35:28.863000 25539 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4475837Z W1218 00:35:28.896000 25539 site-packages/torch/_dynamo/eval_frame.py:342] could not determine __code__ for aten.add 2024-12-18T00:40:41.4476441Z PASSED [19.5693s] [ 45%] 2024-12-18T00:40:41.4477575Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_none_args_aot_codegen_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0032s] (requires GPU) [ 47%] 2024-12-18T00:40:41.4480136Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_output_misaligned_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmp_d96q41x/ckdk5wu6gdihwg3is4c2ndwu3tfejgy6a7a5bdfvkifwfua6ibn2/cwr7meekqudo2x7a3daljpqlg5glqrlrizodsu3r33o33mzuo7iw.cpp:369: 2024-12-18T00:40:41.4483382Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4485481Z /tmp/tmp_d96q41x/ckdk5wu6gdihwg3is4c2ndwu3tfejgy6a7a5bdfvkifwfua6ibn2/cwr7meekqudo2x7a3daljpqlg5glqrlrizodsu3r33o33mzuo7iw.cpp:627:44: required from here 2024-12-18T00:40:41.4487550Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4488821Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4489147Z PASSED [10.0001s] [ 48%] 2024-12-18T00:40:41.4490139Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_output_path_2_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.9864s] [ 50%] 2024-12-18T00:40:41.4493725Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_quanatized_int8_linear_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp: In member function ‘void torch::aot_inductor::AOTInductorModel::run_impl(AtenTensorOpaque**, AtenTensorOpaque**, torch::aot_inductor::DeviceStreamType, AOTIProxyExecutorHandle)’: 2024-12-18T00:40:41.4497179Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:728:10: warning: variable ‘L__self___input_scale’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4498443Z 728 | auto L__self___input_scale = constants_->at(0); 2024-12-18T00:40:41.4498825Z | ^~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4500225Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:729:10: warning: variable ‘L__self___input_zero_point’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4501507Z 729 | auto L__self___input_zero_point = constants_->at(1); 2024-12-18T00:40:41.4501901Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4503262Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:730:10: warning: variable ‘L__self___weight’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4504488Z 730 | auto L__self___weight = constants_->at(2); 2024-12-18T00:40:41.4504840Z | ^~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4506206Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:731:10: warning: variable ‘L__self___weight_scale’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4507519Z 731 | auto L__self___weight_scale = constants_->at(3); 2024-12-18T00:40:41.4507885Z | ^~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4509344Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:732:10: warning: variable ‘L__self___weight_zero_point’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4510647Z 732 | auto L__self___weight_zero_point = constants_->at(4); 2024-12-18T00:40:41.4511044Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4512399Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:733:10: warning: variable ‘L__self___bias’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4513607Z 733 | auto L__self___bias = constants_->at(5); 2024-12-18T00:40:41.4513943Z | ^~~~~~~~~~~~~~ 2024-12-18T00:40:41.4515300Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:734:10: warning: variable ‘L__self___output_scale’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4516554Z 734 | auto L__self___output_scale = constants_->at(6); 2024-12-18T00:40:41.4516926Z | ^~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4518390Z /tmp/tmp6fkc3_kv/crmjutwdiqqjso4orow3bbg76gnu4y5n3mysbuqdriadpzsxipt2/csoef6btzktkpcnfyzxh3okfszhiral4wqbne3ltokk2aemkd5im.cpp:735:10: warning: variable ‘L__self___output_zero_point’ set but not used [-Wunused-but-set-variable] 2024-12-18T00:40:41.4519704Z 735 | auto L__self___output_zero_point = constants_->at(7); 2024-12-18T00:40:41.4520102Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:40:41.4520409Z PASSED [9.3936s] [ 51%] 2024-12-18T00:40:41.4521423Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_return_constant_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.1550s] [ 52%] 2024-12-18T00:40:41.4523263Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_reuse_kernel_dynamic_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [11.6783s] [ 54%] 2024-12-18T00:40:41.4525108Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_run_with_grad_enabled_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.2156s] [ 55%] 2024-12-18T00:40:41.4527150Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_dtype_failed_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py Error: input_handles[0]: unmatched dtype, expected: 5(at::kHalf), but got: 6 2024-12-18T00:40:41.4528354Z 2024-12-18T00:40:41.4528469Z PASSED [9.5520s] [ 57%] 2024-12-18T00:40:41.4529719Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_runtime_checks_shape_failed_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py Error: input_handles[0]: unmatched dim value at 1, expected: 4, but got: 8 2024-12-18T00:40:41.4530909Z 2024-12-18T00:40:41.4531158Z Error: input_handles[0]: unmatched stride value at 1, expected: 4, but got: 1 2024-12-18T00:40:41.4531512Z 2024-12-18T00:40:41.4531798Z Error: input_handles[0]: dim value is too large at 0, expected to be <= 1024, but got: 2048 2024-12-18T00:40:41.4532188Z 2024-12-18T00:40:41.4532452Z Error: input_handles[0]: dim value is too large at 0, expected to be <= 1024, but got: 2048 2024-12-18T00:40:41.4532851Z 2024-12-18T00:40:41.4532947Z PASSED [8.7357s] [ 58%] 2024-12-18T00:40:41.4533997Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_scatter_fallback_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0003s] (Skipped!) [ 60%] 2024-12-18T00:40:41.4535942Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_sdpa_2_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0002s] (bfloat16 only supported in sm80+) [ 61%] 2024-12-18T00:40:41.4538524Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_seq_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py In file included from /tmp/tmpxzyvw6fn/cpt2qkjbtlwf4o6h7sbqkwf5eqnfvfdcbvk6sqw3yvecy7cwtkrh/cpzuwkdvbl26q2pua5svc6dnghel36me3clkyzvsghiob64g2fkb.cpp:369: 2024-12-18T00:40:41.4541744Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h: In instantiation of ‘void torch::aot_inductor::ThreadLocalCachedOutputTensor >::copy_data_from(const torch::aot_inductor::ArrayRefTensor&) [with T = float]’: 2024-12-18T00:40:41.4543883Z /tmp/tmpxzyvw6fn/cpt2qkjbtlwf4o6h7sbqkwf5eqnfvfdcbvk6sqw3yvecy7cwtkrh/cpzuwkdvbl26q2pua5svc6dnghel36me3clkyzvsghiob64g2fkb.cpp:682:44: required from here 2024-12-18T00:40:41.4545993Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/inductor/aoti_runtime/thread_local.h:53:19: warning: comparison of integer expressions of different signedness: ‘long unsigned int’ and ‘int64_t’ {aka ‘long int’} [-Wsign-compare] 2024-12-18T00:40:41.4547273Z 53 | if (t.numel() > capacity_) { 2024-12-18T00:40:41.4547599Z PASSED [10.7727s] [ 63%] 2024-12-18T00:40:41.4548677Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_shifted_constraint_ranges_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 64%] 2024-12-18T00:40:41.4550504Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_simple_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.9719s] [ 66%] 2024-12-18T00:40:41.4552264Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_small_constant_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.3248s] [ 67%] 2024-12-18T00:40:41.4554190Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_torchvision_transforms_functional_tensor_resize_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [11.9600s] [ 69%] 2024-12-18T00:40:41.4556244Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_dynamic_shape_with_div_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0047s] (requires GPU) [ 70%] 2024-12-18T00:40:41.4558532Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 72%] 2024-12-18T00:40:41.4560756Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_1_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 73%] 2024-12-18T00:40:41.4562983Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_1_dynamic_False_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 75%] 2024-12-18T00:40:41.4565210Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_2_num_dims_2_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 76%] 2024-12-18T00:40:41.4567451Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_False_autotune_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 77%] 2024-12-18T00:40:41.4569749Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_1_dynamic_True_autotune_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0043s] (requires GPU) [ 79%] 2024-12-18T00:40:41.4571981Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_grid_type_3_num_dims_2_dynamic_False_autotune_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0043s] (requires GPU) [ 80%] 2024-12-18T00:40:41.4574326Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_reinterpret_view_mem_leak_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0043s] (requires GPU) [ 82%] 2024-12-18T00:40:41.4576385Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_sympy_fn_like_arg_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0052s] (requires GPU) [ 83%] 2024-12-18T00:40:41.4578445Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_tma_descriptor_2d_dynamic_True_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0043s] (requires GPU) [ 85%] 2024-12-18T00:40:41.4580633Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_unbacked_symint_in_grid_dynamic_True_autotuning_False_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py SKIPPED [0.0044s] (requires GPU) [ 86%] 2024-12-18T00:40:41.4582775Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_triton_kernel_with_none_input_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0044s] (requires GPU) [ 88%] 2024-12-18T00:40:41.4584758Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_unsupported_input_dtype_cpu_with_stack_allocation <- test/inductor/test_aot_inductor.py PASSED [0.0722s] [ 89%] 2024-12-18T00:40:41.4586650Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_nested_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0003s] (Skipped!) [ 91%] 2024-12-18T00:40:41.4588507Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_simple_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 92%] 2024-12-18T00:40:41.4590407Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_while_loop_with_outer_code_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [ 94%] 2024-12-18T00:40:41.4592286Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_no_triton_profiler_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [9.0640s] [ 95%] 2024-12-18T00:40:41.4594065Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_offset_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.4369s] [ 97%] 2024-12-18T00:40:41.4595804Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_with_profiler_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py PASSED [10.0414s] [ 98%] 2024-12-18T00:40:41.4597662Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0002s] (Skipped!) [100%] 2024-12-18T00:40:41.4598774Z 2024-12-18T00:40:41.4599513Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-deb7cba2c0539f20.xml - 2024-12-18T00:40:41.4600587Z ================== 36 passed, 32 skipped in 380.02s (0:06:20) ================== 2024-12-18T00:40:41.4600999Z Got exit code -11 (SIGSEGV) 2024-12-18T00:40:41.4601294Z Retrying single test... 2024-12-18T00:40:41.4602068Z Test results will be stored in test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-8e23f748d670f707.xml 2024-12-18T00:40:41.4602919Z ============================= test session starts ============================== 2024-12-18T00:40:41.4603508Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:41.4604042Z cachedir: .pytest_cache 2024-12-18T00:40:41.4604670Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:41.4605365Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:41.4605679Z configfile: pytest.ini 2024-12-18T00:40:41.4606329Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:41.4607118Z collecting ... collected 183 items / 67 deselected / 116 selected 2024-12-18T00:40:41.4608323Z stepcurrent: skipping 67 already run items. Running only test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation 2024-12-18T00:40:41.4609420Z Running 1 items in this shard 2024-12-18T00:40:41.4609609Z 2024-12-18T00:40:41.4610546Z inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation <- test/inductor/test_torchinductor.py SKIPPED [0.0005s] (Skipped!) [100%] 2024-12-18T00:40:41.4611621Z 2024-12-18T00:40:41.4612352Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-8e23f748d670f707.xml - 2024-12-18T00:40:41.4613364Z ====================== 1 skipped, 67 deselected in 0.05s ======================= 2024-12-18T00:40:41.4613737Z Got exit code 0 2024-12-18T00:40:41.4614104Z Test succeeeded in new process, continuing with the rest of the tests 2024-12-18T00:40:41.4615042Z Test results will be stored in test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-74d2c1a6dc6a59c9.xml 2024-12-18T00:40:41.4615884Z ============================= test session starts ============================== 2024-12-18T00:40:41.4616463Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:41.4616991Z cachedir: .pytest_cache 2024-12-18T00:40:41.4617602Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:41.4618289Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:41.4618606Z configfile: pytest.ini 2024-12-18T00:40:41.4619243Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:41.4620031Z collecting ... collected 183 items / 68 deselected / 115 selected 2024-12-18T00:40:41.4620473Z stepcurrent: skipping 68 already run items. 2024-12-18T00:40:41.4620829Z Running 0 items in this shard 2024-12-18T00:40:41.4621030Z 2024-12-18T00:40:41.4621752Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/inductor.test_aot_inductor_arrayref/inductor.test_aot_inductor_arrayref-74d2c1a6dc6a59c9.xml - 2024-12-18T00:40:41.4622753Z ============================ 68 deselected in 0.04s ============================ 2024-12-18T00:40:41.4623970Z The following tests failed and then succeeded when run in a new process['ul', 'test/inductor/test_aot_inductor_arrayref.py::AOTInductorTestABICompatibleCpuWithStackAllocation::test_zero_grid_with_backed_symbols_cpu_with_stack_allocation'] 2024-12-18T00:40:41.4625074Z 2024-12-18T00:40:41.4625640Z FINISHED PRINTING LOG FILE of inductor/test_aot_inductor_arrayref 1/3 (test/test-reports/inductor.test_aot_inductor_arrayref_1.3_5e5c281f794ddef9_.log) 2024-12-18T00:40:41.4626303Z 2024-12-18T00:40:44.7352572Z Running inductor/test_torchinductor_opinfo 20/47 ... [2024-12-18 00:40:44.734811] 2024-12-18T00:40:44.7353601Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:40:44.7355759Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=20', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:40:44.735129] 2024-12-18T00:44:55.5279160Z 2024-12-18T00:44:55.5280690Z inductor/test_torchinductor_opinfo 18/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_18.47_84c08619c60bf6e8_.log 2024-12-18T00:44:55.5344306Z Running 62 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_addcmul_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clone_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagflat_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diff_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erf_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_grid_sampler_2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hstack_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_lstsq_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_pinv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vecdot_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_or_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lu_unpack_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_dropout_backward_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_strided_cpu_bool, 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_binary_cross_entropy_with_logits_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_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_hardsigmoid_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_circular_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_shuffle_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu6_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_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_polygamma_polygamma_n_2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_prod_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_put_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_sum_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_blackman_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_erfcx_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i0e_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_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_special_spherical_bessel_j0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_xlog1py_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trunc_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unflatten_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_uint32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_bool 2024-12-18T00:44:55.5415383Z 2024-12-18T00:44:56.1093831Z Uploading artifacts took 0.58 seconds 2024-12-18T00:44:58.8706278Z Running inductor/test_torchinductor_opinfo 21/47 ... [2024-12-18 00:44:58.870190] 2024-12-18T00:44:58.8707130Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:44:58.8709450Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=21', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:44:58.870560] 2024-12-18T00:45:35.3880958Z 2024-12-18T00:45:35.3882073Z inductor/test_torchinductor_opinfo 6/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_6.47_3e5a1d58429dc5cd_.log 2024-12-18T00:45:35.3919960Z Running 77 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___rpow___cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__softmax_backward_data_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_all_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan2_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_and_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chunk_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_count_nonzero_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_embed_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_scatter_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_as_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expm1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftshift_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flip_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ge_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ge_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hypot_cpu_float32, 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_kron_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vecdot_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_and_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_var_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_conv_transpose3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_glu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_logsigmoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_float16, 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_silu_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_soft_margin_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_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_positive_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reciprocal_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_mean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_prod_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sign_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_general_hamming_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sin_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_sampled_addmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_legendre_polynomial_p_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_float64 2024-12-18T00:45:35.3957194Z 2024-12-18T00:45:38.7590672Z Running inductor/test_torchinductor_opinfo 33/47 ... [2024-12-18 00:45:38.758649] 2024-12-18T00:45:38.7591641Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:45:38.7593718Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=33', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:45:38.758968] 2024-12-18T00:50:31.4321445Z 2024-12-18T00:50:31.4322562Z inductor/test_torchinductor_opinfo 20/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_20.47_0698ec35b92a0684_.log 2024-12-18T00:50:31.4359520Z Running 73 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rsub___cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_abs_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_copysign_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumsum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_equal_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftn_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftshift_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_divide_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_mean_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_select_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_det_singular_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_householder_product_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_log_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp2_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_scatter_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_sum_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mean_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_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_binary_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_dropout_backward_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_celu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bilinear_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_linear_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_leaky_relu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_mse_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_normalize_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_replicate_negative_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_prelu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_outer_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_remainder_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsqrt_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_scatter_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_short_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_airy_ai_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_log_ndtr_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i0_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_torch_ops_aten__safe_softmax_default_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_uniform_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_consecutive_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_float32 2024-12-18T00:50:31.4395827Z 2024-12-18T00:50:34.7963353Z Running inductor/test_torchinductor_opinfo 35/47 ... [2024-12-18 00:50:34.795929] 2024-12-18T00:50:34.7964335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:50:34.7966785Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=35', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:50:34.796262] 2024-12-18T00:53:26.1367313Z 2024-12-18T00:53:26.1368486Z inductor/test_torchinductor_opinfo 21/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_21.47_5c1f7dc25e5a5db7_.log 2024-12-18T00:53:26.1404778Z Running 73 items in this shard: 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_addcmul_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argsort_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumprod_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumsum_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagflat_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftn_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flip_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geometric_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hstack_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hypot_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_put_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isneginf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_solve_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lt_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_mean_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_median_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_prod_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_softmin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_binary_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool2d_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_triplet_margin_loss_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_unfold_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_like_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_remainder_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_as_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_conj_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_conj_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rot90_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_scatter_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_short_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_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtri_cpu_int64, 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_std_unbiased_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_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_tile_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trace_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_indices_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_uint64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_uint8 2024-12-18T00:53:26.1439716Z 2024-12-18T00:53:29.4515400Z Running inductor/test_torchinductor_opinfo 36/47 ... [2024-12-18 00:53:29.451123] 2024-12-18T00:53:29.4516360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:53:29.4518613Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=36', '--num-shards=47', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:53:29.451442] 2024-12-18T00:54:14.6091034Z 2024-12-18T00:54:14.6092296Z inductor/test_torchinductor_opinfo 33/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_33.47_ab58d4932bc1045a_.log 2024-12-18T00:54:14.6130991Z Running 78 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_argmin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bincount_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_xor_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bucketize_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bucketize_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ceil_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exponential_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfft_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_item_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_le_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vander_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_normal_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mH_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumprod_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumprod_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_fill_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_matrix_exp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_no_dim_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_minimum_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mode_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mul_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_full_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose1d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_embedding_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout3d_cpu_float16, 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_elu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_linear_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_constant_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_reflect_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pdist_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_threshold_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nonzero_static_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ravel_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_round_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sigmoid_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinh_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sqrt_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_multiple_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_unbiased_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tile_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trace_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_int64 2024-12-18T00:54:14.6169125Z 2024-12-18T00:54:17.9196024Z Running inductor/test_compiled_optimizers 1/1 ... [2024-12-18 00:54:17.919229] 2024-12-18T00:54:17.9196621Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:54:17.9198013Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_compiled_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'] ... [2024-12-18 00:54:17.919539] 2024-12-18T00:58:16.8859147Z 2024-12-18T00:58:16.8860322Z inductor/test_torchinductor_opinfo 35/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_35.47_e147fe169c4ac9c9_.log 2024-12-18T00:58:16.8897431Z Running 74 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmod___cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rpow___cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__batch_norm_with_update_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addr_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_any_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_any_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_to_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cat_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ceil_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cross_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagflat_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_digamma_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erf_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftn_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flip_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flip_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_divide_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gradient_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_put_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_prod_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_select_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_inner_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isreal_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_item_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_det_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_diagonal_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_log_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_argmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_prod_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_prod_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_similarity_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_ctc_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardtanh_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardtanh_cpu_int64, 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_linear_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_constant_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_poisson_nll_loss_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_threshold_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_inf_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_like_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_short_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signbit_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j1_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_stack_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_float64 2024-12-18T00:58:16.8933412Z 2024-12-18T00:58:20.2307199Z Running dynamo/test_base_output 1/1 ... [2024-12-18 00:58:20.230335] 2024-12-18T00:58:20.2307695Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:58:20.2309204Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.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'] ... [2024-12-18 00:58:20.230662] 2024-12-18T00:58:23.5996384Z 2024-12-18T00:58:23.5997432Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_7248d672dfb2d298_.log 2024-12-18T00:58:23.5999972Z Running 6 items in this shard: test/dynamo/test_base_output.py::TestBaseOutput::test_assign, test/dynamo/test_base_output.py::TestBaseOutput::test_create, test/dynamo/test_base_output.py::TestBaseOutput::test_getattr, test/dynamo/test_base_output.py::TestBaseOutput::test_getitem, test/dynamo/test_base_output.py::TestBaseOutput::test_index, test/dynamo/test_base_output.py::TestBaseOutput::test_tuple 2024-12-18T00:58:23.6002650Z 2024-12-18T00:58:26.9391676Z Running inductor/test_cooperative_reductions 1/1 ... [2024-12-18 00:58:26.938827] 2024-12-18T00:58:26.9392287Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:58:26.9394138Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cooperative_reductions.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'] ... [2024-12-18 00:58:26.939151] 2024-12-18T00:58:30.1327866Z 2024-12-18T00:58:30.1329015Z inductor/test_cooperative_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cooperative_reductions_1.1_cce53bd7fda6e2ca_.log 2024-12-18T00:58:30.1329870Z 2024-12-18T00:58:33.4764085Z Running test_import_stats 1/1 ... [2024-12-18 00:58:33.476065] 2024-12-18T00:58:33.4764580Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:58:33.4765959Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_import_stats.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'] ... [2024-12-18 00:58:33.476372] 2024-12-18T00:58:39.6998240Z 2024-12-18T00:58:39.6999245Z test_import_stats 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_import_stats_1.1_e98e6201324c9954_.log 2024-12-18T00:58:39.7000524Z Running 2 items in this shard: test/test_import_stats.py::TestImportTime::test_time_cuda_device_count, test/test_import_stats.py::TestImportTime::test_time_import_torch 2024-12-18T00:58:39.7001245Z 2024-12-18T00:58:43.0607255Z Running test_binary_ufuncs 1/1 ... [2024-12-18 00:58:43.060322] 2024-12-18T00:58:43.0607765Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:58:43.0609535Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_binary_ufuncs.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'] ... [2024-12-18 00:58:43.060664] 2024-12-18T00:58:43.9744116Z 2024-12-18T00:58:43.9745655Z inductor/test_compiled_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compiled_optimizers_1.1_e991f9ac2f3681bd_.log 2024-12-18T00:58:44.0101056Z Running 651 items in this shard: test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_S429861, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_rho_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_rho_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_rho_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_tensor_lr_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adadelta_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_initial_accumulator_value_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_initial_accumulator_value_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_initial_accumulator_value_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_lr_decay_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_lr_decay_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_lr_decay_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cpu_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_tensor_lr_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adagrad_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_amsgrad_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adam_tensor_lr_tensor_betas_amsgrad_capturable_foreach_cuda_multisteplr, 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test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_amsgrad_capturable_foreach_cuda_multisteplr, 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test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_tensor_lr_tensor_betas_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_amsgrad_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_adamw_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_lambd_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_lambd_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_lambd_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_maximize_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_maximize_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_recompile_default, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_recompile_foreach, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_recompile_single, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_t0_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_t0_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_t0_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_tensor_lr_weight_decay_maximize_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_maximize_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_maximize_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_asgd_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_basic_shampoo, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_closure_graph_break, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_compile_time_smoketest, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_get_value_on_static_address, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_guard_on_none_grads, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_momentum_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_momentum_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_momentum_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_tensor_lr_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_decoupled_weight_decay_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_decoupled_weight_decay_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_decoupled_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_decoupled_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_decoupled_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_nadam_weight_decay_momentum_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_weight_decay_decoupled_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_weight_decay_decoupled_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_capturable_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_eps_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_eps_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_eps_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_tensor_lr_capturable_weight_decay_decoupled_weight_decay_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_decoupled_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_decoupled_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_decoupled_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_radam_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_maximize_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_tensor_lr_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_centered_momentum_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_maximize_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rmsprop_weight_decay_maximize_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_capturable_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_capturable_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_etas_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_etas_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_etas_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_recompile, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_step_sizes_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_step_sizes_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_step_sizes_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_rprop_tensor_lr_capturable_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_dampening_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_dampening_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_dampening_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_nesterov_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_nesterov_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_nesterov_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_momentum_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_recompile_foreach, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_recompile_single, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cpu_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_constantlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_cosineannealinglr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_cosineannealingwarmrestarts, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_cycliclr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_exponentiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_lambdalr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_linearlr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_multiplicativelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_multisteplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_onecyclelr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_polynomiallr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_reducelronplateau, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_tensor_lr_foreach_cuda_steplr, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_maximize_cpu, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_maximize_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_sgd_weight_decay_maximize_foreach_cuda, test/inductor/test_compiled_optimizers.py::CompiledOptimizerTests::test_static_address_finalizer 2024-12-18T00:58:44.0444454Z 2024-12-18T00:58:47.4135279Z Running export/test_functionalized_assertions 1/1 ... [2024-12-18 00:58:47.413118] 2024-12-18T00:58:47.4136099Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:58:47.4137637Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_functionalized_assertions.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'] ... [2024-12-18 00:58:47.413465] 2024-12-18T00:58:50.8826030Z 2024-12-18T00:58:50.8827628Z export/test_functionalized_assertions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_functionalized_assertions_1.1_4bdd392337768ff6_.log 2024-12-18T00:58:50.8829562Z Running 2 items in this shard: test/export/test_functionalized_assertions.py::TestFuntionalAssertions::test_functional_assert_async_msg, test/export/test_functionalized_assertions.py::TestFuntionalAssertions::test_functional_sym_constrain_range 2024-12-18T00:58:50.8830680Z 2024-12-18T00:58:54.2706984Z Running export/test_tree_utils 1/1 ... [2024-12-18 00:58:54.270308] 2024-12-18T00:58:54.2707468Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:58:54.2708791Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_tree_utils.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'] ... [2024-12-18 00:58:54.270651] 2024-12-18T00:58:57.7395558Z 2024-12-18T00:58:57.7396574Z export/test_tree_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_tree_utils_1.1_a10c23432dc85621_.log 2024-12-18T00:58:57.7398130Z Running 2 items in this shard: test/export/test_tree_utils.py::TestTreeUtils::test_equivalence_check, test/export/test_tree_utils.py::TestTreeUtils::test_reorder_kwargs 2024-12-18T00:58:57.7398946Z 2024-12-18T00:59:01.0508702Z Running inductor/test_inductor_annotations 1/1 ... [2024-12-18 00:59:01.050492] 2024-12-18T00:59:01.0509256Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:59:01.0510688Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_annotations.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'] ... [2024-12-18 00:59:01.050813] 2024-12-18T00:59:04.4700319Z 2024-12-18T00:59:04.4702041Z inductor/test_inductor_annotations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_annotations_1.1_12723b29d92b93cd_.log 2024-12-18T00:59:04.4705382Z Running 2 items in this shard: test/inductor/test_inductor_annotations.py::InductorAnnotationTestCase::test_no_annotations, test/inductor/test_inductor_annotations.py::InductorAnnotationTestCase::test_training_annotation 2024-12-18T00:59:04.4707343Z 2024-12-18T00:59:07.9046267Z Running dynamo/test_nops 1/1 ... [2024-12-18 00:59:07.904196] 2024-12-18T00:59:07.9046739Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:59:07.9048303Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.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'] ... [2024-12-18 00:59:07.904564] 2024-12-18T00:59:11.4236038Z 2024-12-18T00:59:11.4236933Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_739e66635108fa7c_.log 2024-12-18T00:59:11.4238637Z Running 4 items in this shard: test/dynamo/test_nops.py::NopTests::test1, test/dynamo/test_nops.py::NopTests::test2, test/dynamo/test_nops.py::NopTests::test3, test/dynamo/test_nops.py::NopTests::test_extended_args 2024-12-18T00:59:11.4239558Z 2024-12-18T00:59:14.7470192Z Running inductor/test_utils 1/1 ... [2024-12-18 00:59:14.746607] 2024-12-18T00:59:14.7470911Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:59:14.7473152Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_utils.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'] ... [2024-12-18 00:59:14.746952] 2024-12-18T00:59:18.5176660Z 2024-12-18T00:59:18.5178087Z inductor/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_utils_1.1_15e63165bc9acbe7_.log 2024-12-18T00:59:18.5179317Z Running 2 items in this shard: test/inductor/test_utils.py::TestUtils::testSympySubs, test/inductor/test_utils.py::TestUtils::test_zip_schema 2024-12-18T00:59:18.5179989Z 2024-12-18T00:59:21.9753665Z Running inductor/test_indexing 1/1 ... [2024-12-18 00:59:21.974964] 2024-12-18T00:59:21.9754407Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:59:21.9756485Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_indexing.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'] ... [2024-12-18 00:59:21.975297] 2024-12-18T00:59:29.2501027Z 2024-12-18T00:59:29.2503003Z inductor/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_indexing_1.1_df597cc0c8753c2a_.log 2024-12-18T00:59:29.2516834Z Running 19 items in this shard: test/inductor/test_indexing.py::TestIndexingSimplification::test_expand_floor_div_applied, test/inductor/test_indexing.py::TestIndexingSimplification::test_expand_floor_div_skipped, test/inductor/test_indexing.py::TestIndexingSimplification::test_indexing_join, test/inductor/test_indexing.py::TestIndexingSimplification::test_indexing_simplification, test/inductor/test_indexing.py::TestIndexingSimplification::test_int8_unpack, test/inductor/test_indexing.py::TestIndexingSimplification::test_modular_indexing_pairs_merged, test/inductor/test_indexing.py::TestIndexingSimplification::test_modular_indexing_pairs_not_merged, test/inductor/test_indexing.py::ExprPrinterTests::test_print_Min_Max, test/inductor/test_indexing.py::ExprPrinterTests::test_print_ceil, test/inductor/test_indexing.py::ExprPrinterTests::test_print_floor, test/inductor/test_indexing.py::ExprPrinterTests::test_print_floor_div, test/inductor/test_indexing.py::ExprPrinterTests::test_print_mod, test/inductor/test_indexing.py::ExprPrinterTests::test_print_mod_index, test/inductor/test_indexing.py::ExprPrinterTests::test_print_pow, test/inductor/test_indexing.py::ExprPrinterTests::test_print_python_mod, test/inductor/test_indexing.py::ExprPrinterTests::test_print_round, test/inductor/test_indexing.py::ExprPrinterTests::test_print_round_decimal_ndigits_-1, test/inductor/test_indexing.py::ExprPrinterTests::test_print_round_decimal_ndigits_0, test/inductor/test_indexing.py::ExprPrinterTests::test_print_round_decimal_ndigits_1 2024-12-18T00:59:29.2529883Z 2024-12-18T00:59:33.5156059Z Running inductor/test_pad_mm 1/1 ... [2024-12-18 00:59:33.515173] 2024-12-18T00:59:33.5156887Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:59:33.5159303Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_pad_mm.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'] ... [2024-12-18 00:59:33.515581] 2024-12-18T00:59:38.5828626Z 2024-12-18T00:59:38.5830107Z inductor/test_pad_mm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_pad_mm_1.1_481db1420710f3fe_.log 2024-12-18T00:59:38.5831598Z 2024-12-18T00:59:41.9294919Z Running dynamo/test_utils 1/1 ... [2024-12-18 00:59:41.929132] 2024-12-18T00:59:41.9295392Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:59:41.9297788Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_utils.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'] ... [2024-12-18 00:59:41.929466] 2024-12-18T00:59:58.7165040Z 2024-12-18T00:59:58.7165983Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_63cde011d46e464a_.log 2024-12-18T00:59:58.7168766Z Running 6 items in this shard: test/dynamo/test_utils.py::TestUtils::test_larger_multiplier_for_even_smaller_tensor, test/dynamo/test_utils.py::TestUtils::test_larger_multiplier_for_smaller_tensor, test/dynamo/test_utils.py::TestUtils::test_nan, test/dynamo/test_utils.py::TestDynamoTimed::test_dynamo_timed, test/dynamo/test_utils.py::TestInductorConfigParsingForLogging::test_inductor_config_jsonify, test/dynamo/test_utils.py::TestInductorConfigParsingForLogging::test_inductor_config_parsing_non_conforming_items 2024-12-18T00:59:58.7171015Z 2024-12-18T01:00:02.0451504Z Running inductor/test_compile_worker 1/1 ... [2024-12-18 01:00:02.044728] 2024-12-18T01:00:02.0452103Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:00:02.0453647Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_compile_worker.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'] ... [2024-12-18 01:00:02.045061] 2024-12-18T01:00:13.8290986Z 2024-12-18T01:00:13.8292557Z inductor/test_compile_worker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compile_worker_1.1_2b3cb206b2a65730_.log 2024-12-18T01:00:13.8295758Z Running 3 items in this shard: test/inductor/test_compile_worker.py::TestCompileWorker::test_basic_jobs, test/inductor/test_compile_worker.py::TestCompileWorker::test_crash, test/inductor/test_compile_worker.py::TestCompileWorker::test_exception 2024-12-18T01:00:13.8297974Z 2024-12-18T01:00:18.0821210Z Running functorch/test_aotdispatch 1/5 ... [2024-12-18 01:00:18.081690] 2024-12-18T01:00:18.0822066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:00:18.0825323Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'not serial', '--shard-id=1', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:00:18.082120] 2024-12-18T01:00:37.7466210Z 2024-12-18T01:00:37.7467561Z test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_binary_ufuncs_1.1_56081ceb2b7b6bec_.log 2024-12-18T01:00:38.2822187Z Running 12855 items in this shard: test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_int8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_eq_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_float_power_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_floor_divide_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmax_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmin_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gcd_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gcd_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gcd_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gcd_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gcd_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_hypot_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_hypot_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_hypot_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_hypot_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igamma_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igamma_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igamma_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igamma_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igammac_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igammac_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igammac_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igammac_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_lcm_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_lcm_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_lcm_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_lcm_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_lcm_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ldexp_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_float16, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_float16, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_chebyshev_polynomial_w_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_he_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_laguerre_polynomial_l_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_legendre_polynomial_p_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_true_divide_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_xlogy_cpu_bfloat16, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_uint8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_right_shift_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_right_shift_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_right_shift_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_uint8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_complex32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_hypot_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_hypot_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_hypot_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_hypot_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igamma_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igamma_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igamma_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igamma_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igammac_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igammac_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igammac_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igammac_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_complex32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_mul_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ne_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_nextafter_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_nextafter_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_nextafter_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_nextafter_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_polar_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_polar_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_pow_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_remainder_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_rsub_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_chebyshev_polynomial_t_cpu_int8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_bfloat16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_bfloat16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cpow_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cpu_tensor_pow_cuda_scalar_tensor_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cremainder_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cross_device_binary_ops_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cross_device_inplace_error_msg_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_csub_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cuda_tensor_pow_scalar_tensor_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cumulative_trapezoid_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_and_floordiv_script_vs_python_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_and_floordiv_vs_python_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_nonfinite_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_nonfinite_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_nonfinite_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_nonfinite_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divide_by_zero_rounding_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divide_by_zero_rounding_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divide_by_zero_rounding_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divide_by_zero_rounding_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divmul_scalar_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_exceptions_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_scalar_pow_float_tensor_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_scalar_pow_float_tensor_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_float_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_float_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_float_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_integral_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_integral_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_integral_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_integral_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_integral_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_gcd_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_gcd_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_gcd_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_gcd_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_gcd_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_complex_cpu_complex128_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_complex_cpu_complex128_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_complex_cpu_complex64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_complex_cpu_complex64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cross_device_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_idiv_and_ifloordiv_vs_python_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_inplace_division_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_inplace_dunders_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_int_and_float_pow_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_int_tensor_pow_neg_ints_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lcm_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lcm_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lcm_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_ldexp_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_lowp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_lowp_cpu_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_lowp_cpu_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_lowp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp2_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp2_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp2_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logaddexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logical_and_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logical_and_cpu_bfloat16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logical_and_cpu_bfloat16_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_logical_and_cpu_bfloat16_complex64, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_and_minimum_subgradient_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex128_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_complex_cpu_complex64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_cross_device_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_nan_and_inf_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_nan_and_inf_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_nan_and_inf_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_float_nan_and_inf_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_forward_ad_float32_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_int_and_bool_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_int_and_bool_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_int_and_bool_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_int_and_bool_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_int_and_bool_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_int_and_bool_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bfloat16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_bool_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_float64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_int8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_maximum_minimum_type_promotion_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_min_max_binary_op_nan_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_min_max_binary_op_nan_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_mul_chalf_tensor_and_cpu_scalar_cpu_complex32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_mul_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_mul_intertype_scalar_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_mul_intertype_scalar_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_muldiv_scalar_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_nextafter_bfloat16_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_nextafter_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_nextafter_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_nextafter_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_non_contig___radd___cpu_float64, 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2024-12-18T01:00:38.7855487Z 2024-12-18T01:00:41.1139672Z Running functorch/test_aotdispatch 3/5 ... [2024-12-18 01:00:41.113608] 2024-12-18T01:00:41.1140204Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:00:41.1141935Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'not serial', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:00:41.113932] 2024-12-18T01:04:57.1162400Z 2024-12-18T01:04:57.1163875Z inductor/test_torchinductor_opinfo 36/47 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_36.47_5f3595efc7cb5197_.log 2024-12-18T01:04:57.1207758Z Running 82 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmatmul___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_bool, 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test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapz_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_indices_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_true_divide_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_true_divide_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_float64 2024-12-18T01:04:57.1248059Z 2024-12-18T01:04:58.0255367Z Uploading artifacts took 0.91 seconds 2024-12-18T01:05:00.8476116Z Running functorch/test_aotdispatch 5/5 ... [2024-12-18 01:05:00.847207] 2024-12-18T01:05:00.8476641Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:05:00.8477846Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'not serial', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:05:00.847548] 2024-12-18T01:06:18.6309444Z 2024-12-18T01:06:18.6310531Z functorch/test_aotdispatch 3/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_3.5_47415d81bfa5b10e_.log 2024-12-18T01:06:18.6538135Z Running 421 items in this shard: test/functorch/test_aotdispatch.py::TestAOTAutograd::test_backward_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_batch_norm_amp, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dupe_arg, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dupe_arg_returned_as_output, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dupe_arg_torture, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_inner_grad, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_aliased_with_mutation_output_alias, 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test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_nn_functional_pdist_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_nn_functional_relu6_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_nn_functional_rrelu_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_nn_functional_threshold_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_norm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_polar_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_randint_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_repeat_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_resolve_conj_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_roll_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_scatter_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_short_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_signal_windows_general_cosine_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_sin_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_bessel_y1_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_hermite_polynomial_h_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_i0e_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_i1e_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_ndtr_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_spherical_bessel_j0_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_xlog1py_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_split_list_args_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_squeeze_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_std_mean_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_stft_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_t_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_take_along_dim_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_tanh_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_triangular_solve_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_unsafe_split_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_var_mean_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_view_as_complex_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_view_as_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_view_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_AdaptiveMaxPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_GRUCell_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Hardshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Hardswish_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Hardtanh_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_InstanceNorm1d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_InstanceNorm3d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LPPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LSTMCell_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MarginRankingLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiLabelMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiLabelSoftMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_PoissonNLLLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReflectionPad1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_SoftMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softplus_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Tanhshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerEncoder_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerEncoder_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_BCELoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_BatchNorm2d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_CircularPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ConstantPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Conv1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Conv2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ELU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_FractionalMaxPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GRU_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GroupNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_HuberLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm1d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm3d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm3d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LSTM_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LayerNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LazyConv1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LazyConv2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LazyConvTranspose2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LazyConvTranspose3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Linear_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LogSigmoid_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MaxPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_RNN_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReflectionPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReflectionPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReplicationPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_TransformerDecoderLayer_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ZeroPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_backward_mutation_on_grad_out, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_batchnorm_inference, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_buffer_copied_in_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_buffer_copied_in_graph_with_different_shapes, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_aliased_with_mutation_output_alias, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_aliases_bases_out_of_order, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_and_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_mem_leak_from_save_for_bw, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_module, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_nested_subclasses, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_returned_multiple_times, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_dict, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_op_depending_on_symint, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_set__not_allowed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_subclass_metadata_mutation_req_grad_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_subclass_metadata_mutation_req_grad_True, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_buffer_copied_in_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_buffer_copied_in_graph_with_different_shapes, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_custom_tensor_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_dupe_arg_returned_as_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_inference_mode, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_inner_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_aliases_other_input2, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_and_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_false_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_modifies_autograd_meta_of_aliases, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_output_aliase_custom_autograd_function, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_output_view_metadata_mutate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_output_view_mutate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_invalid_dupe, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_mem_leak_from_save_for_bw, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_mutates_input_noncontiguous, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nested_subclasses_non_nested_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_returned_multiple_times, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_multiple_inputs_get_correct_one, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_output_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_real_weights_in_symbolic_mode_with_inplace_ops, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_some_outputs_dont_require_grad_non_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_some_outputs_dont_require_grad_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_squeeze_mutation 2024-12-18T01:06:18.6754810Z 2024-12-18T01:06:21.9673961Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2024-12-18 01:06:21.967006] 2024-12-18T01:06:21.9674604Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:06:21.9675922Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.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'] ... [2024-12-18 01:06:21.967333] 2024-12-18T01:06:25.1156702Z 2024-12-18T01:06:25.1158039Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_2f04bf1d43e0fc6f_.log 2024-12-18T01:06:25.1159255Z 2024-12-18T01:06:28.4193669Z Running functorch/test_ops 2/6 ... [2024-12-18 01:06:28.418985] 2024-12-18T01:06:28.4194145Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:06:28.4195621Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ops.py', '-m', 'not serial', '--shard-id=2', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:06:28.419313] 2024-12-18T01:10:20.8625111Z 2024-12-18T01:10:20.8626146Z functorch/test_aotdispatch 1/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_1.5_ffdb1743cfe75583_.log 2024-12-18T01:10:20.8838599Z Running 402 items in this shard: test/functorch/test_aotdispatch.py::TestAOTAutograd::test_backward_mutation_forward_inputs, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_backward_mutation_forward_inputs_create_graph, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_complex_linear, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_custom_tensor_metadata, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_embedding_bag_view_dynamic, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_data_and_metadata_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_output_view_multiple, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_return, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_storage_resize_down_and_set_, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_storage_resize_up, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_invalid_dupe, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_nonidempotent_amp, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_output_aliases_intermediate_and_returned_different_grad, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_output_all_alias_types, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_output_op_depending_on_symint, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_subclass_metadata_mutation_req_grad_True, 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test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LayerNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LazyConv1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LazyConv3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LazyConvTranspose2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LogSigmoid_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MaxPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Mish_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiheadAttention_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_RNNCell_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_RNN_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_RNN_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReLU6_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReflectionPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_SELU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Tanh_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Transformer_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ZeroPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_AdaptiveAvgPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_AvgPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_BatchNorm3d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Bilinear_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_CircularPad1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ConvTranspose2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Embedding_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GRUCell_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GaussianNLLLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Hardswish_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LPPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LPPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LocalResponseNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MultiheadAttention_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MultiheadAttention_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_NLLLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_PoissonNLLLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_RMSNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_RNN_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReLU6_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Softsign_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Tanhshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_TransformerEncoder_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_autocast_disable_guard, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_backward_mutation_forward_inputs_create_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_buffer_batch_norm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_custom_autograd, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_dupe_arg_torture, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_grad_context, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_inner_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_aliases_other_input2, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_false_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_hidden_from_autograd_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_is_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_metadata2, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_inputs_overlapping_with_mutation_guard_base, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_invalid_requires_grad_fake, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_nested_subclasses_complicated_inps, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_nonidempotent_amp, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_no_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_multiple_inputs_get_correct_one, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_outputs_are_aliased, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_real_weights_in_symbolic_mode_with_inplace_ops, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_single_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_synthetic_base_base_attribute_is_none, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_view_detach, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_data, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_buffer_batch_norm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_dupe_arg_torture, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_data_and_metadata_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_inplace_requires_grad_true, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_output_view_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_set__input_mutation, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_storage_resize_down, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_storage_resize_up, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_inputs_overlapping_with_mutation_guard_base, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_invalid_requires_grad_fake, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_list_codegen, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_module, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_no_grad_input_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_input_multi_output_view_should_raise_autograd_error, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_and_returned_different_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_and_returned_flipped, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_inplace_view_with_detach, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_multiple_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_mutation_linear, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_dict, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_outputs_are_aliased, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_set__and_data_mutation_bad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_set__and_data_mutation_good, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_set__steals_view_chain, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_some_output_requires_grad_input_doesnt, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_subclass_metadata_mutation_req_grad_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_view_detach 2024-12-18T01:10:20.9045397Z 2024-12-18T01:10:24.5214705Z Running functorch/test_ops 5/6 ... [2024-12-18 01:10:24.520998] 2024-12-18T01:10:24.5215712Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:24.5218123Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ops.py', '-m', 'not serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:10:24.521454] 2024-12-18T01:15:07.0201653Z 2024-12-18T01:15:07.0203033Z functorch/test_ops 2/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_2.6_dd629717dd996124_.log 2024-12-18T01:15:07.1429318Z Running 1615 items in this shard: test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_det_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_tensor_with_scalar_list_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_T_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_contiguous_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_diagonal_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_dsplit_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_split_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_vsplit_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mH_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mT_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_narrow_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_permute_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_real_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_logsigmoid_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_special_bessel_y0_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeNotComposableAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SelectAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_T_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__chunk_cat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__unsafe_masked_index_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__upsample_bilinear2d_aa_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_abs_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_add_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addcdiv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atan_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bfloat16_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bfloat16_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cauchy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cfloat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chalf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_min_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_contiguous_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cummin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_deg2rad_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diag_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagflat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_double_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_as_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eye_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flatten_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flip_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_floor_divide_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_geqrf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hypot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_i0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igamma_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_binary_return_by_ref_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_le_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_det_singular_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_diagonal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_slogdet_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log1p_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_xor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logspace_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_amin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_argmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_argmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_log_softmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_binary_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_bilinear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gaussian_nll_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hinge_embedding_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_area_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_grad_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_prelu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_relu6_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_selu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softsign_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_upsample_nearest_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nonzero_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pca_lowrank_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_permute_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_quantile_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_add_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_amin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_bartlett_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_nuttall_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_t_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i0e_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_v_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_square_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sub_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_t_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tensor_split_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_uniform_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_where_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_xlogy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zero__cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_equal_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_SelectAutogradFunction_cpu_float32 2024-12-18T01:15:07.2599242Z 2024-12-18T01:15:10.8546199Z Running inductor/test_snode_runtime 1/1 ... [2024-12-18 01:15:10.854183] 2024-12-18T01:15:10.8547206Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:15:10.8549718Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_snode_runtime.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'] ... [2024-12-18 01:15:10.854568] 2024-12-18T01:15:15.0031816Z 2024-12-18T01:15:15.0034683Z inductor/test_snode_runtime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_snode_runtime_1.1_83e9bff1c16e8670_.log 2024-12-18T01:15:15.0036232Z 2024-12-18T01:15:18.4694392Z Running inductor/test_perf 1/1 ... [2024-12-18 01:15:18.469052] 2024-12-18T01:15:18.4695117Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:15:18.4697651Z 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'] ... [2024-12-18 01:15:18.469434] 2024-12-18T01:15:22.4953746Z 2024-12-18T01:15:22.4955155Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_d079e076855e3d66_.log 2024-12-18T01:15:22.4956352Z 2024-12-18T01:15:27.0978705Z Running inductor/test_fp8 1/1 ... [2024-12-18 01:15:27.097423] 2024-12-18T01:15:27.0979794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:15:27.0989121Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fp8.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'] ... [2024-12-18 01:15:27.097845] 2024-12-18T01:15:31.9794967Z 2024-12-18T01:15:31.9796369Z inductor/test_fp8 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fp8_1.1_d0ed73208af43234_.log 2024-12-18T01:15:31.9797543Z 2024-12-18T01:15:36.0227120Z Running test_ops_fwd_gradients 1/1 ... [2024-12-18 01:15:36.022288] 2024-12-18T01:15:36.0227977Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:15:36.0230076Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_fwd_gradients.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'] ... [2024-12-18 01:15:36.022666] 2024-12-18T01:17:44.9039228Z 2024-12-18T01:17:44.9040758Z functorch/test_aotdispatch 5/5 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_5.5_4a088494a58af251_.log 2024-12-18T01:17:44.9450287Z Running 407 items in this shard: test/functorch/test_aotdispatch.py::TestAOTAutograd::test_backward_mutation_data, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_backward_mutation_on_grad_out, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_buffer_batch_norm, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_buffer_copied_in_graph, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_compilation_context, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_custom_autograd, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dynamic_shape_output_not_in_bw_graph, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_metadata_mutation_aliases, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_alias_everything, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_aliases_other_input2, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_modifies_autograd_meta_of_aliases, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_requires_grad_no_grad, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_set__input_mutation, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_storage_resize_before_set_, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_invalid_requires_grad, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_invalid_requires_grad_fake, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_list_codegen, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_module, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_nested_subclasses_complicated_inps_mixed, 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test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_HuberLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_InstanceNorm2d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_KLDivLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LSTM_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LazyConvTranspose1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LazyConvTranspose3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LogSoftmax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MSELoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MaxPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiheadAttention_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_NLLLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_PReLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_RMSNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReflectionPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReplicationPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ReplicationPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_SiLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_SmoothL1Loss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softmax2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softsign_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerDecoderLayer_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ZeroPad1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ZeroPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_AdaptiveAvgPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_AdaptiveMaxPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_AvgPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Conv3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ConvTranspose1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_FractionalMaxPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LazyConv3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MSELoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MaxPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MaxPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MultiLabelMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MultiMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_RNNCell_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReplicationPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_SELU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Softmax2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Threshold_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_TransformerEncoder_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ZeroPad1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ZeroPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_batch_norm_amp, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_compilation_context, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_composite_impl_compile, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_default_partitioner_saves_symints_not_tensors_for_bw, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_dupe_arg_returned_as_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_embedding_bag_view_dynamic, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_data_and_metadata_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_inplace_requires_grad_true, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_alias_everything, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_aliases_and_none_require_gradients, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_aliases_and_output_alias, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_modifies_autograd_meta_of_aliases, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_output_view_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_requires_grad_no_grad_detach_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_output_view_metadata_mutate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_output_view_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_inputs_overlapping_unsqueeze_with_mutation, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_invalid_dupe, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_invalid_requires_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_nested_subclasses_complicated_inps_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_non_tensor_and_none_inputs, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_input_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_input_multi_output_view_should_raise_autograd_error, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_and_returned_different_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_inplace_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_multiple_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_mutation_linear, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_set__steals_view_chain, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_some_outputs_dont_require_grad_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_view_and_inplace_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_forward_inputs, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_compilation_context, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_default_partitioner_saves_symints_not_tensors_for_bw, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_dupe_arg, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_aliased_with_mutation_output_alias, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_aliases_and_none_require_gradients, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_aliases_bases_out_of_order, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_noncontiguous, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_set__nop, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_storage_resize_before_set_, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_invalid_dupe_fake, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nonidempotent_amp, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_inplace_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_op_depending_on_symint, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_real_weights_in_symbolic_mode, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_set__not_allowed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_subclass_metadata_mutation_req_grad_True, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_synthetic_base_base_attribute_is_none 2024-12-18T01:17:44.9852060Z 2024-12-18T01:17:48.3136018Z Running inductor/test_cpu_select_algorithm 8/17 ... [2024-12-18 01:17:48.313267] 2024-12-18T01:17:48.3136543Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:17:48.3138220Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=8', '--num-shards=17', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:17:48.313587] 2024-12-18T01:19:36.5282607Z 2024-12-18T01:19:36.5284006Z functorch/test_ops 5/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_5.6_74eac925a89cf231_.log 2024-12-18T01:19:36.6230262Z Running 1750 items in this shard: test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_cross_entropy_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_softmax_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_expand_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_flatten_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_flatten_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_split_list_args_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mT_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_resolve_neg_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_select_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_squeeze_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_squeeze_multiple_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unflatten_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unfold_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_det_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__segment_reduce_lengths_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv_transpose1d_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_any_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_det_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_H_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyTakeAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ScaleGradGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___getitem___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__softmax_backward_data_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_acosh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argsort_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bool_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_tensors_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cdouble_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chunk_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_combinations_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cov_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumprod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_digamma_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_trunc_rounding_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eq_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exp2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifftshift_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_power_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_floor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frac_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_geometric_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_grid_sampler_2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gt_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histogramdd_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_inner_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_int_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_int_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isposinf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_binary_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cross_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lstsq_grad_oriented_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_factor_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_subgradients_at_zero_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_hermitian_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_qr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_svd_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vecdot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log10_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_softmax_with_dtype_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logaddexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mT_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_select_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_softmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_std_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_median_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_variadic_tensors_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mode_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanquantile_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nansum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_neg_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_alpha_dropout_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_batch_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_channel_shuffle_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_no_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_no_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_padding_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_similarity_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gelu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardshrink_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_huber_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_bicubic_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_linear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_trilinear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_kl_div_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mse_loss_functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multi_head_attention_forward_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multi_margin_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_constant_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_unshuffle_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_scaled_dot_product_attention_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_silu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_smooth_l1_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_outer_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rand_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ravel_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resize_as__cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resolve_neg_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scalar_tensor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_searchsorted_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_select_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_hamming_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sparse_sampled_addmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_j0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_y0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_hermite_polynomial_h_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_laguerre_polynomial_l_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_legendre_polynomial_p_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_t_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_list_args_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_with_sizes_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_stft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_svd_lowrank_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_take_along_dim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tan_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_triu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trunc_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unflatten_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unique_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_chunk_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_split_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_mean_unbiased_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_as_complex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vsplit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zeros_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_det_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_det_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_empty_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_linalg_inv_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_split_list_args_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_circular_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_shifted_chebyshev_polynomial_t_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_MulGenVmapAutogradFunction_cpu_float32 2024-12-18T01:19:36.6899335Z 2024-12-18T01:19:39.9563772Z Running inductor/test_cpu_select_algorithm 11/17 ... [2024-12-18 01:19:39.955990] 2024-12-18T01:19:39.9564510Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:39.9566133Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=11', '--num-shards=17', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:19:39.956324] 2024-12-18T01:25:32.7766248Z 2024-12-18T01:25:32.7767606Z inductor/test_cpu_select_algorithm 8/17 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_8.17_a5ca80c8100eac38_.log 2024-12-18T01:25:32.7820319Z Running 61 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order3_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order0_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order2_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order2_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_65_epilogue_mul_cpu_float16, 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_False_cpu_float32, 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_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_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_binary_input_3d_batch_size_384_in_features_196_out_features_384_bias_True_binary_add_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_div_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_tanh_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_sigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_sigmoid_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardsigmoid_cpu_float32, 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_unary_binary_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_add_cpu_bfloat16, 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_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_68_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_65_bias_True_input_3d_False_epilogue_none_cpu_bfloat16, 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_False_epilogue_none_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_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_64_bias_False_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_64_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_none_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_True_int8_mixed_bf16_False_epilogue_relu_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_True_int8_mixed_bf16_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_with_pointwise_dynamic_shapes_bs_5_Mdim_384_Kdim_96_Ndim_65_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1024_bias_False_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_out_features_1024_bias_True_input_3d_False_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_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_True_input_3d_False_cpu_float32, 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_bfloat16, 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_True_cpu_bfloat16, 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_bfloat16, 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_none_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_False_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_384_bias_False_epilogue_hardswish_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_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_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_False_epilogue_gelu_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_False_epilogue_hardtanh_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_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_gelu_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_sigmoid_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_silu_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_False_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_129_bias_False_epilogue_add_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_none_cpu_float16, 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_True_unary_relu_binary_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_32_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_3_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_3_in_features_4_out_features_64_bias_False_cpu, 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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_gelu_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_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_64_bias_True_input_3d_True_epilogue_relu_cpu_bfloat16 2024-12-18T01:25:32.7869448Z 2024-12-18T01:25:34.0018376Z Uploading artifacts took 1.22 seconds 2024-12-18T01:25:36.3232672Z Running inductor/test_cpu_select_algorithm 15/17 ... [2024-12-18 01:25:36.322879] 2024-12-18T01:25:36.3233606Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:25:36.3235972Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=15', '--num-shards=17', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:25:36.323253] 2024-12-18T01:25:38.3350981Z 2024-12-18T01:25:38.3352231Z test_ops_fwd_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.1_79361f9e68acd444_.log 2024-12-18T01:25:38.4738805Z Running 3189 items in this shard: test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_H_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_H_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_T_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_T_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___getitem___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___getitem___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___radd___cpu_complex128, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_long_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_long_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lt_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_solve_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_solve_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_unpack_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_unpack_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mH_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mH_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mT_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mT_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_amax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_amin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_argmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_argmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumprod_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumprod_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumsum_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumsum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_fill_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_fill_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_log_softmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_logaddexp_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_logsumexp_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_logsumexp_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_median_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_normalize_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_normalize_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_prod_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_prod_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_scatter_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_scatter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_select_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_select_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_softmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_softmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_std_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_std_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_sum_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_sum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_var_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_var_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matmul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matmul_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matrix_exp_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matrix_exp_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_binary_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_pool2d_with_indices_backward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_reduction_no_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_reduction_with_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_maximum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_median_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_list_of_tensors_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_list_of_tensors_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_variadic_tensors_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_variadic_tensors_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_min_binary_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_min_reduction_no_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_min_reduction_with_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_minimum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mode_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_movedim_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_movedim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_msort_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mul_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_multinomial_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mv_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mv_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nan_to_num_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanmean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanmedian_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanquantile_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nansum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_batch_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_dropout_backward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_layer_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ne_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ne_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_neg_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_neg_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_cpu_complex128, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nextafter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_alpha_dropout_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_batch_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_celu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv1d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv2d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv3d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cosine_embedding_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cosine_similarity_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cross_entropy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_ctc_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_elu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_embedding_bag_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_embedding_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_fractional_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_fractional_max_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_gaussian_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_gelu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_glu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_grid_sample_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_group_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardsigmoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardswish_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardtanh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hinge_embedding_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_huber_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_instance_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_area_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_bicubic_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_linear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_trilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_kl_div_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_l1_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_l1_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_layer_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_leaky_relu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_linear_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_linear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_local_response_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_logsigmoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_margin_ranking_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool3d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_mish_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_mse_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multi_head_attention_forward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multi_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multilabel_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_normalize_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_normalize_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_circular_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_circular_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_constant_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_constant_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_reflect_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_reflect_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pdist_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_unshuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_unshuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_poisson_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_prelu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_relu6_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_relu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rms_norm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rms_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rrelu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_selu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_silu_complex_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_silu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_smooth_l1_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_soft_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softplus_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softsign_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softsign_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_tanhshrink_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_tanhshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_threshold_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_unfold_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_unfold_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_upsample_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_upsample_nearest_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_static_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_static_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_fro_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_fro_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_inf_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_inf_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_nuc_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_nuc_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_in_place_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_in_place_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_number_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ormqr_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ormqr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_outer_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_outer_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pca_lowrank_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pca_lowrank_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_permute_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_permute_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_permute_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_permute_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pinverse_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pinverse_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polar_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_2_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_3_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_4_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_positive_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_positive_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pow_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pow_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_prod_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_prod_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_put_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_put_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_qr_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_qr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_quantile_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rad2deg_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rand_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rand_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randint_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randint_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randn_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randn_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randn_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randn_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ravel_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ravel_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_real_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_real_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_reciprocal_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_reciprocal_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_remainder_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_renorm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_renorm_cpu_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_sparse_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_sparse_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_topk_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trace_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trace_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapezoid_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapezoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapz_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapz_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triangular_solve_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triangular_solve_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tril_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tril_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triu_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_true_divide_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_true_divide_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trunc_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unflatten_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unflatten_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_uniform_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_uniform_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unique_consecutive_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unique_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_chunk_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_chunk_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_split_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_split_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vdot_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vdot_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_complex_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_real_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vsplit_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vsplit_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vstack_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vstack_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_where_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_where_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_xlogy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zero__cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zero__cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_like_cpu_float64 2024-12-18T01:25:38.6082709Z 2024-12-18T01:25:41.8421171Z Running inductor/test_halide 1/1 ... [2024-12-18 01:25:41.841774] 2024-12-18T01:25:41.8421739Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:25:41.8423497Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_halide.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'] ... [2024-12-18 01:25:41.842107] 2024-12-18T01:25:48.8974781Z 2024-12-18T01:25:48.8975740Z inductor/test_halide 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_halide_1.1_eac005a52ed821d9_.log 2024-12-18T01:25:48.8976462Z 2024-12-18T01:25:52.2805723Z Running inductor/test_unbacked_symints 1/1 ... [2024-12-18 01:25:52.280215] 2024-12-18T01:25:52.2806259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:25:52.2808085Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_unbacked_symints.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'] ... [2024-12-18 01:25:52.280578] 2024-12-18T01:25:55.5038514Z 2024-12-18T01:25:55.5040210Z inductor/test_unbacked_symints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_unbacked_symints_1.1_061716361c351834_.log 2024-12-18T01:25:55.5041598Z 2024-12-18T01:25:59.0131657Z Running test_ops 2/6 ... [2024-12-18 01:25:59.012770] 2024-12-18T01:25:59.0132416Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:25:59.0134606Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=2', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:25:59.013089] 2024-12-18T01:30:59.5370901Z 2024-12-18T01:30:59.5372327Z inductor/test_cpu_select_algorithm 11/17 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_11.17_0f41f6b7e924f687_.log 2024-12-18T01:30:59.5441101Z Running 86 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order2_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order3_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_64_epilogue_mul_cpu_bfloat16, 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_65_epilogue_div_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_cpp_coordinate_descent_tuning_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_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_1000_out_features_1_bias_True_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_float32, 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_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1000_out_features_1024_bias_False_input_3d_True_cpu_float16, 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_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_thread_factors_batch_size_1024_in_features_1024_out_features_1024_bias_True_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_add_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_add_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_tanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_gelu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_silu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardsigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardswish_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_sigmoid_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_mul_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_add_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_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_mul_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_none_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_sub_cpu_float16, 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_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_384_bias_True_unary_relu_binary_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_16_in_features_4_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_16_in_features_68_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_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_4_out_features_65_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_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_epilogue_relu_cpu_bfloat16, 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_False_epilogue_none_cpu_float32, 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_none_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_False_epilogue_gelu_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_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_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_amx_dynamic_shapes_batch_size_1024_in_features_1024_out_features_1024_bias_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_cache_blocking_dynamic_shapes_batch_size_512_in_features_1024_out_features_1024_bias_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1024_bias_True_input_3d_True_cpu_float16, 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_False_input_3d_False_cpu_float16, 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_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_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_1000_out_features_1024_bias_False_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_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_1000_out_features_1_bias_False_input_3d_False_cpu_float32, 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_1_bias_True_input_3d_True_cpu_bfloat16, 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_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_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_thread_factors_dynamic_shapes_batch_size_1024_in_features_1024_out_features_1024_bias_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_embedding_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_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_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_384_bias_False_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_False_epilogue_silu_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_div_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_silu_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_sub_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_add_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_False_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_False_epilogue_hardsigmoid_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_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_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_False_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_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_none_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_True_epilogue_sub_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_384_bias_True_unary_relu_binary_mul_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_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_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_32_in_features_128_out_features_65_bias_True_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_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_32_in_features_68_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_68_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_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_64_bias_True_input_3d_True_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_False_input_3d_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_True_input_3d_False_epilogue_none_cpu_bfloat16 2024-12-18T01:30:59.5509125Z 2024-12-18T01:31:03.3894990Z Running test_ops 6/6 ... [2024-12-18 01:31:03.389120] 2024-12-18T01:31:03.3895420Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:31:03.3896881Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:31:03.389454] 2024-12-18T01:35:22.5290250Z 2024-12-18T01:35:22.5291606Z test_ops 2/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_2.6_61ebaa648411a365_.log 2024-12-18T01:35:22.8501441Z Running 5676 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___radd___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___rpow___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_T_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_as_strided_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_atleast_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_atleast_3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_bucketize_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_column_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_div_floor_rounding_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_index_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linalg_diagonal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linalg_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linalg_svdvals_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_logaddexp2_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_movedim_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_new_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_new_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_normal__in_place_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_permute_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_randn_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_rot90_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_special_xlog1py_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_unflatten_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_unsqueeze_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_view_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_view_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_vstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_as_strided_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_bitwise_right_shift_cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu_cdist_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_contiguous_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cov_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_diagonal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_diagonal_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_fft_ifftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_fliplr_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_fmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_geqrf_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_inner_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_isin_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_ldexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_eigh_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_lstsq_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_lu_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_long_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_fill_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_log_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_logsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_meshgrid_list_of_tensors_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_mm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_narrow_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_native_dropout_backward_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_new_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nextafter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_glu_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_relu6_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_unfold_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_ones_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_polar_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_randint_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_randint_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_resolve_neg_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_rsub_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_legendre_polynomial_p_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_xlog1py_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_std_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_std_mean_unbiased_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_take_along_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_take_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_trapezoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_uniform_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_unsqueeze_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_var_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_view_as_real_cpu_complex64, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_T_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_cdouble_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_diag_embed_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_full_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_new_full_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_new_ones_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_rand_like_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_real_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_transpose_copy_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_tril_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_unbind_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_unsafe_chunk_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_unsqueeze_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_zeros_cpu_complex32, test/test_ops.py::TestCommonCPU::test_dtypes_H_cpu, test/test_ops.py::TestCommonCPU::test_dtypes___getitem___cpu, test/test_ops.py::TestCommonCPU::test_dtypes___rand___cpu, test/test_ops.py::TestCommonCPU::test_dtypes___rmod___cpu, test/test_ops.py::TestCommonCPU::test_dtypes___rmul___cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_cdouble_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_abs_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_amax_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_atleast_2d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_bitwise_xor_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_block_diag_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_broadcast_shapes_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_ceil_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_clone_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_column_stack_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_cumsum_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_div_trunc_rounding_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_empty_strided_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_fft_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_hfftn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_ifftn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_ihfftn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_floor_divide_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fmin_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_frac_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_gcd_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_geometric_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_isclose_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_istft_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_item_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_lcm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_linalg_matrix_norm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_log1p_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_logaddexp2_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_logical_or_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_logsumexp_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_movedim_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_new_zeros_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_hardshrink_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_nll_loss_cpu, 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test/test_ops.py::TestCommonCPU::test_dtypes__refs_special_multigammaln_mvlgamma_p_3_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_special_softmax_with_dtype_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_special_spherical_bessel_j0_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_tanh_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_tril_indices_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_true_divide_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_vsplit_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_add_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_addcdiv_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_addmv_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_addr_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_all_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_as_strided_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_atanh_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_atleast_3d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_bfloat16_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_bitwise_or_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_broadcast_tensors_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_byte_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_cdist_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_cdouble_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_ceil_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_char_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_complex_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_conj_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_cumprod_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_deg2rad_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_diag_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_diagflat_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_diff_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_div_trunc_rounding_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_dsplit_cpu, 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test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_interpolate_linear_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_layer_norm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_leaky_relu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_pool3d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_unpool2d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_unpool3d_grad_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_multilabel_soft_margin_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_nll_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_normalize_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_pairwise_distance_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_soft_margin_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_prod_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_resize__cpu, 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test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cummin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_diagonal_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_exp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_fft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_irfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_rfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_rfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_float_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_hypot_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_igamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_reduce_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ldexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_le_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_det_singular_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_eigh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_householder_product_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_norm_subgradients_at_zero_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_vector_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log1p_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logaddexp2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logical_xor_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_min_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_msort_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_multinomial_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nansum_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polar_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sign_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_j1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_y0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_xlog1py_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_tan_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_trunc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_H_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_T_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices___radd___cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices__batch_norm_with_update_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__upsample_bilinear2d_aa_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addmv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_alias_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_angle_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_any_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_argmin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_asinh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_atan_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atanh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_baddbmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_baddbmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_to_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cartesian_prod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cauchy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cdouble_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cfloat_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chalf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chalf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cholesky_inverse_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_max_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_min_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_conj_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_contiguous_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_count_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cummin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diagonal_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diff_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eq_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_erf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_erf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expm1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_expm1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ihfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_flatten_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_flatten_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_flipud_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_floor_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_floor_divide_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_full_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gather_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_geometric_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_geqrf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_fill_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_put_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_int_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isfinite_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isinf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_binary_return_by_ref_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_unary_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_kthvalue_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lcm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_lgamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lgamma_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cross_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_det_singular_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_inv_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lu_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_pinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_tensorinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_vecdot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_not_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_not_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_or_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_or_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logsumexp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mH_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_argmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_fill_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_log_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_sum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_var_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_var_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_matrix_exp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_meshgrid_list_of_tensors_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_no_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_with_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_narrow_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_native_layer_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ne_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_empty_strided_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_avg_pool1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv_transpose1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_embedding_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_dropout_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_margin_ranking_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_replicate_negative_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pairwise_distance_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pixel_shuffle_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_unfold_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_normal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_outer_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_outer_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_permute_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_permute_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_pinverse_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_3_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_positive_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_pow_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ravel_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_reciprocal_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_repeat_interleave_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_reshape_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_reshape_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rsqrt_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_rsub_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_amin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_searchsorted_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_hann_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signbit_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_bessel_j1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_chebyshev_polynomial_v_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_polygamma_special_polygamma_n_0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_scaled_modified_bessel_k1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_w_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sqrt_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_square_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_squeeze_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_squeeze_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_std_unbiased_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_stft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_svd_lowrank_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_t_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_t_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_tanh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_tensordot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_trace_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_tril_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unbind_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unfold_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unique_consecutive_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsqueeze_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsqueeze_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_var_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_vdot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_vdot_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_vsplit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_where_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_where_cpu_int64, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values___rxor___cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values__unsafe_masked_index_put_accumulate_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_addr_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_as_strided_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_as_strided_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_as_strided_partial_views_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_atleast_3d_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_bfloat16_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_bitwise_xor_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cat_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_chalf_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_expand_as_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_expm1_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_fft2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_hfft2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_ifft_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_irfft_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_rfft2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_flatten_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_float_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_gather_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_ge_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_heaviside_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_hsplit_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_index_fill_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_isclose_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_kron_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_ldexp_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_logit_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_masked_fill_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_masked_prod_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_max_reduction_with_dim_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_minimum_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nansum_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nn_functional_cosine_embedding_loss_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nn_functional_pixel_unshuffle_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_ones_like_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_ravel_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_rot90_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_scatter_reduce_amax_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_select_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_select_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_sinc_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_slice_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_slice_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_laguerre_polynomial_l_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_modified_bessel_k1_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_spherical_bessel_j0_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_split_list_args_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_stack_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_tile_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_to_sparse_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_true_divide_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_unflatten_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_unfold_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_zeros_like_cpu_bool, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_H_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_T_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rmatmul___cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rmatmul___cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rpow___cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rxor___cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples__batch_norm_with_update_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples__unsafe_masked_index_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_abs_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_abs_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acos_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addbmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addcdiv_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addmv_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addr_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_aminmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_aminmax_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_any_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_any_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_arange_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_arange_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_argmax_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_argsort_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_as_strided_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atanh_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atleast_1d_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atleast_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atleast_3d_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atleast_3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atleast_3d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_broadcast_tensors_cpu_complex64, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_mode_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_movedim_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_mul_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_mul_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nan_to_num_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nan_to_num_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nanmedian_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_narrow_copy_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_narrow_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_narrow_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ne_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_new_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_new_full_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_celu_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv1d_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv_transpose1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv_transpose1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv_transpose2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv_transpose3d_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_glu_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_linear_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_pool2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_unpool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_normalize_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_constant_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_reflect_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pairwise_distance_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pdist_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pixel_unshuffle_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_relu_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_relu_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_rms_norm_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_threshold_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_normal_in_place_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ones_like_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ones_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_permute_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_polygamma_polygamma_n_0_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_positive_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_pow_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_qr_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_rand_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randint_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randint_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randint_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randn_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randn_like_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ravel_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_real_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_remainder_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_repeat_interleave_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_reshape_as_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_reshape_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_rot90_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_round_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_rsqrt_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_scatter_reduce_amin_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_scatter_reduce_mean_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_select_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sgn_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sigmoid_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_signal_windows_blackman_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sin_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sort_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_chebyshev_polynomial_w_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_i1_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_legendre_polynomial_p_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_legendre_polynomial_p_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_modified_bessel_i0_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_modified_bessel_i1_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_xlog1py_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_split_with_sizes_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_split_with_sizes_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sqrt_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_numpy_ref_broadcast_to_cpu_complex128, test/test_ops.py::TestCommonCPU::test_numpy_ref_broadcast_to_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_clamp_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_clone_cpu_complex128, test/test_ops.py::TestCommonCPU::test_numpy_ref_diag_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_diagflat_cpu_complex128, test/test_ops.py::TestCommonCPU::test_numpy_ref_flatten_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_linalg_tensorsolve_cpu_complex128, test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_conv_transpose2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_group_norm_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_mse_loss_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_one_hot_cpu_int64, 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test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_sum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_take_along_dim_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_tan_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_tanh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_trace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_add_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_addmm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_argwhere_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_asin_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_bfloat16_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_bool_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_broadcast_to_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_char_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_column_stack_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cumprod_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cumulative_trapezoid_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_diagonal_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_einsum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_empty_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_eq_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_hfft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_hfftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_ifft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fill_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_full_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_imag_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_index_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_index_put_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isfinite_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isinf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_binary_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_binary_return_by_ref_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_unary_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_lerp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_det_singular_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_ldl_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lstsq_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lstsq_grad_oriented_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lu_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_matrix_power_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_matrix_rank_hermitian_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_qr_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_vecdot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linspace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log1p_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logical_and_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_long_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_lu_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_lu_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mH_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_prod_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_std_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_matrix_exp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_meshgrid_list_of_tensors_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_ones_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv_transpose1d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_linear_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pad_replicate_negative_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_triplet_margin_with_distance_loss_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nonzero_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_pow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ravel_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_real_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_renorm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_resolve_conj_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_roll_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_rot90_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sigmoid_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_softmax_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_split_list_args_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_std_mean_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_t_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tril_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_triu_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unbind_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unflatten_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_view_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_view_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_H_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___rsub___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_T_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_cdouble_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_cfloat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_double_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_int_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atan_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atleast_3d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_broadcast_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_column_stack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_conj_physical_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cumsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_dsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_empty_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_equal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_exp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expm1_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_fft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fill_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fliplr_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_fill_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_isclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_lerp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linspace_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_narrow_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_narrow_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_empty_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_nn_functional_log_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_special_log_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_special_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_std_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_stft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sum_to_size_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_t_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_t_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_take_along_dim_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unfold_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unfold_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_view_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_vstack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_atanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_char_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_clone_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_conj_physical_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dist_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_ifft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_irfft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_irfftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_index_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_2inputs_2outputs_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_binary_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cross_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_eigvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_matrix_rank_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_multi_dot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_solve_triangular_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_svdvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linspace_tensor_overload_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logdet_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logical_not_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_prod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_matrix_exp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_movedim_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_ne_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_normalize_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_pixel_unshuffle_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_softsign_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_tanhshrink_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_outer_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_permute_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_positive_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_renorm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_scatter_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sparse_sampled_addmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_with_sizes_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_squeeze_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_std_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_std_mean_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_std_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_stft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tensordot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_to_sparse_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_true_divide_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_unfold_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_mean_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view__batch_norm_with_update_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_bool_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_chalf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_int_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_acos_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_all_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_as_strided_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atleast_1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_clamp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_count_nonzero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_div_trunc_rounding_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dstack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_exp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_expand_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_expand_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_hfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_hfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_floor_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_igammac_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_item_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_vector_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logaddexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logical_not_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logsumexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_narrow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nextafter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_channel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_elu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_gelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_group_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_relu6_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_relu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softplus_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_normal_cpu_float64, 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test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_constant_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_select_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_stft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_zero__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___rsub___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_acos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_acosh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addcdiv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_angle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_baddbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_corrcoef_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_dist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_einsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_erf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_flatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_float_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_frac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_grid_sampler_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_inner_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_kthvalue_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_inv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_inv_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lstsq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_matrix_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_max_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_native_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_real_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_roll_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_rot90_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_stft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unsafe_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_deg2rad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_div_floor_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_eye_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_flip_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fmod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_histogramdd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_igammac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_inner_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isposinf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_item_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_lcm_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logcumsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mT_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_matmul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nanmean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_empty_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_full_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_normal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_outer_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_real_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_airy_ai_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_list_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_with_sizes_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rand___cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rdiv___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__segment_reduce_offsets_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atleast_1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bitwise_not_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_block_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bucketize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_byte_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cholesky_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_copysign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_deg2rad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_diff_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_empty_permuted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_eq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_equal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erfinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_eye_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ihfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_histc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_int_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isnan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isreal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_istft_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_kthvalue_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_le_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cross_cpu_float32, 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test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mT_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_meshgrid_list_of_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_msort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nanmedian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_gaussian_nll_loss_cpu_float32, 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test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_upsample_nearest_cpu_float32, 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test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_searchsorted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_blackman_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_hann_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_j1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_i0e_cpu_float32, 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test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_complex32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_int32, test/test_ops.py::TestTagsCPU::test_tags_T_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rmod___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___ror___cpu_int64, test/test_ops.py::TestTagsCPU::test_tags___rxor___cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_T_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_byte_cpu_float32, 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test/test_ops.py::TestTagsCPU::test_tags_chunk_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_combinations_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cumsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagflat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_div_floor_rounding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_erfc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_fft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_rfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_flatten_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_frac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_geqrf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_add_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isneginf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_istft_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_kthvalue_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_householder_product_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lu_factor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_pinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linspace_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logical_or_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_matmul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_binary_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_movedim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_msort_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_narrow_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ne_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_new_zeros_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_dropout_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_embedding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softplus_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softsign_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_number_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_outer_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_qr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_randint_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_randn_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_repeat_interleave_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_resize__cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rot90_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_select_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sort_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_entr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_ndtri_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_split_with_sizes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_stft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_svd_lowrank_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_take_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_to_sparse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_triangular_solve_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_triu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unique_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsafe_split_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_var_mean_unbiased_cpu_float32 2024-12-18T01:35:23.2115272Z 2024-12-18T01:35:26.3041538Z Running test_matmul_cuda 1/1 ... [2024-12-18 01:35:26.303714] 2024-12-18T01:35:26.3042359Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:26.3044376Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.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'] ... [2024-12-18 01:35:26.304097] 2024-12-18T01:35:30.3239204Z 2024-12-18T01:35:30.3240195Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_ccf203b0f0f763f6_.log 2024-12-18T01:35:30.3240959Z Running 0 items in this shard: 2024-12-18T01:35:30.3241159Z 2024-12-18T01:35:30.6465985Z 2024-12-18T01:35:30.6467415Z inductor/test_cpu_select_algorithm 15/17 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_15.17_5081501e9d0e8ecc_.log 2024-12-18T01:35:30.6562703Z Running 72 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order3_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_freezing_bs_1_Mdim_192_Kdim_196_Ndim_65_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_self_permute_bs_5_Mdim_64_Kdim_96_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_k_slicing_batch_size_2_in_features_1000_out_features_2_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_k_slicing_batch_size_2_in_features_1000_out_features_2_bias_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1024_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_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_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_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_False_input_3d_False_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_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1_bias_False_input_3d_False_cpu_float16, 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, 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_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_hardtanh_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_leaky_relu_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_silu_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_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_False_epilogue_gelu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardswish_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardswish_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_sigmoid_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_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_none_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_True_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_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_128_out_features_65_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_3_in_features_68_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_3_in_features_68_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_49_in_features_68_out_features_65_bias_True_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_gelu_cpu_float32, 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_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_gelu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_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_64_bias_False_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_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_False_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_float32, 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_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1_bias_True_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_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_1024_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1000_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_1000_out_features_1024_bias_True_input_3d_True_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_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_none_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_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_384_bias_True_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_385_bias_False_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_False_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_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_none_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_none_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_4_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_68_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_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_68_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_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_False_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_False_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_65_bias_False_input_3d_True_int8_mixed_bf16_False_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_True_int8_mixed_bf16_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_False_input_3d_False_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_False_epilogue_gelu_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_True_input_3d_False_epilogue_gelu_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_True_input_3d_False_epilogue_none_cpu_float32 2024-12-18T01:35:30.6619747Z 2024-12-18T01:35:34.0408877Z Running test_hub 1/1 ... [2024-12-18 01:35:34.040391] 2024-12-18T01:35:34.0409579Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:34.0411507Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.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'] ... [2024-12-18 01:35:34.040716] 2024-12-18T01:35:34.7948415Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2024-12-18 01:35:34.794415] 2024-12-18T01:35:34.7949696Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:34.7952069Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.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'] ... [2024-12-18 01:35:34.794893] 2024-12-18T01:35:37.2711540Z 2024-12-18T01:35:37.2712763Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_f79d628bbbfbd8a1_.log 2024-12-18T01:35:37.2713799Z 2024-12-18T01:35:40.7415460Z Running test_out_dtype_op 1/1 ... [2024-12-18 01:35:40.741158] 2024-12-18T01:35:40.7416279Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:40.7418686Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_out_dtype_op.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'] ... [2024-12-18 01:35:40.741523] 2024-12-18T01:35:44.6628940Z 2024-12-18T01:35:44.6629941Z test_out_dtype_op 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_out_dtype_op_1.1_acf828ba469d3d1d_.log 2024-12-18T01:35:44.6634117Z Running 12 items in this shard: test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_dynamo, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_inductor_decomp, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_inductor_decomp_trace, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_int_mm_default_trace, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_make_fx, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_mm_numerical, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_mul_scalar_numerical, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_no_autograd, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_non_functional, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_non_op_overload, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_op_functional, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_wrong_output 2024-12-18T01:35:44.6637844Z 2024-12-18T01:35:48.0563165Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2024-12-18 01:35:48.055882] 2024-12-18T01:35:48.0564353Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:48.0566459Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.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'] ... [2024-12-18 01:35:48.056207] 2024-12-18T01:35:52.2771790Z 2024-12-18T01:35:52.2773867Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_812720ab15e878c5_.log 2024-12-18T01:35:52.2823320Z Running 60 items in this shard: test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_arr_weights_mismatch, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_big_arrays, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_array_dims, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_edge_cases, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bool_conversion, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_density, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_error_binnum_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_exotic_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_f32_rounding, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_histogram_bin_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_invalid_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_last_bin_inclusive_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_no_side_effects, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_object_array_of_0d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_one_bin, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_outliers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_precision, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_some_nan_values, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_unsigned_monotonicity_check, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_incorrect_methods, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_limited_variance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_novariance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_outlier, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_scott_vs_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_auto, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_doane, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_fd, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_rice, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_scott, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_sturges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_weighted, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_small, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_array, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_error_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_errors, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_1d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_2d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_edge_dtype, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_equal_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_identical_samples, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_inf_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_large_integers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_rightmost_binedge, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_3d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_4d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_weights 2024-12-18T01:35:52.2868428Z 2024-12-18T01:35:52.9878649Z 2024-12-18T01:35:52.9880356Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_d027358293b59ddc_.log 2024-12-18T01:35:52.9914852Z Running 50 items in this shard: test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_broadcasting_dot_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_collapse, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_combined_views_mapping, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_complex, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_B, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_D, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_F, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_b, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_d, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_e, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_f, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_h, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_i, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_l, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_edge_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_all_contig_non_contig_output, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_broadcast, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_errors, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_failed_on_p9_and_s390x, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixed_collapsingbug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixedstridebug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_misc, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat128, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_uint8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_views, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_expand, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_hadamard_like_products, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_index_transformations, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_inner_product, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_out_is_res, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_output_order, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_random_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_small_boolean_arrays, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_subscript_range, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_edge_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_long_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_memory_contraints, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_internal_trace, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_invalid, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_spaces, test/torch_np/numpy_tests/core/test_einsum.py::TestMisc::test_overlap 2024-12-18T01:35:52.9948134Z 2024-12-18T01:35:56.3215784Z Running test_logging 1/1 ... [2024-12-18 01:35:56.321156] 2024-12-18T01:35:56.3216544Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:56.3219073Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', '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'] ... [2024-12-18 01:35:56.321594] 2024-12-18T01:35:57.4482947Z Running test_namedtensor 1/1 ... [2024-12-18 01:35:57.447858] 2024-12-18T01:35:57.4483804Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:35:57.4485984Z 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'] ... [2024-12-18 01:35:57.448282] 2024-12-18T01:36:02.3706445Z 2024-12-18T01:36:02.3707870Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_e7cf92d93b9649f5_.log 2024-12-18T01:36:02.3753937Z 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 2024-12-18T01:36:02.3801614Z 2024-12-18T01:36:03.2963177Z 2024-12-18T01:36:03.2964451Z test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_logging_1.1_8f6f3d73375a3b1a_.log 2024-12-18T01:36:03.2966033Z Running 1 items in this shard: test/test_logging.py::LoggingTest::testApiUsage 2024-12-18T01:36:03.2966735Z 2024-12-18T01:36:06.3140583Z Running distributions/test_constraints 1/1 ... [2024-12-18 01:36:06.312871] 2024-12-18T01:36:06.3141738Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:36:06.3143893Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_constraints.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'] ... [2024-12-18 01:36:06.313334] 2024-12-18T01:36:07.7828304Z Running test_decomp 1/10 ... [2024-12-18 01:36:07.782418] 2024-12-18T01:36:07.7829048Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:36:07.7831065Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=1', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:36:07.782792] 2024-12-18T01:36:10.5861239Z 2024-12-18T01:36:10.5862959Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_77e4443ef8af9a61_.log 2024-12-18T01:36:10.5963495Z Running 136 items in this shard: test/distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[True-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24] 2024-12-18T01:36:10.6062689Z 2024-12-18T01:36:15.4982612Z Running test_decomp 6/10 ... [2024-12-18 01:36:15.497854] 2024-12-18T01:36:15.4983363Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:36:15.4985809Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=6', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:36:15.498303] 2024-12-18T01:40:01.9743956Z 2024-12-18T01:40:01.9744820Z test_ops 6/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_6.6_25c666ac32a23e17_.log 2024-12-18T01:40:02.1733037Z Running 5778 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___rand___cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu___rdiv___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___rsub___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__chunk_cat_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_chalf_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_half_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_addr_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_bitwise_right_shift_cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_cauchy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_constant_pad_nd_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_diagonal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_dsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_expand_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_index_fill_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linalg_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_meshgrid_list_of_tensors_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_mul_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_new_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_glu_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_normal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_renorm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_rsub_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_t_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_take_along_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_transpose_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addcdiv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addmv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_arange_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cfloat_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_combinations_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_dist_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_empty_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_empty_permuted_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_expand_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_fft_fftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_gradient_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_half_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_igamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_igammac_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_index_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_index_reduce_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_int_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_kron_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_kthvalue_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_lerp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_householder_product_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_inv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_lu_factor_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_pinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_vector_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_logaddexp2_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_logcumsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_lu_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_median_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_errors_arange_cpu, test/test_ops.py::TestCommonCPU::test_errors_bitwise_or_cpu, test/test_ops.py::TestCommonCPU::test_errors_bitwise_right_shift_cpu, test/test_ops.py::TestCommonCPU::test_errors_clamp_max_cpu, test/test_ops.py::TestCommonCPU::test_errors_diag_embed_cpu, test/test_ops.py::TestCommonCPU::test_errors_diagonal_copy_cpu, test/test_ops.py::TestCommonCPU::test_errors_dsplit_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_fftn_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_ihfft2_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_rfft2_cpu, test/test_ops.py::TestCommonCPU::test_errors_flipud_cpu, test/test_ops.py::TestCommonCPU::test_errors_ge_cpu, test/test_ops.py::TestCommonCPU::test_errors_jiterator_binary_cpu, test/test_ops.py::TestCommonCPU::test_errors_log_normal_cpu, test/test_ops.py::TestCommonCPU::test_errors_logical_and_cpu, test/test_ops.py::TestCommonCPU::test_errors_masked_fill_cpu, test/test_ops.py::TestCommonCPU::test_errors_narrow_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_adaptive_max_pool1d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_avg_pool3d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_conv2d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_embedding_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_rrelu_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_softshrink_cpu, test/test_ops.py::TestCommonCPU::test_errors_normal_in_place_cpu, test/test_ops.py::TestCommonCPU::test_errors_rsub_cpu, test/test_ops.py::TestCommonCPU::test_errors_scatter_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_bartlett_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_blackman_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_gaussian_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_mul_layout1_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_mul_layout2_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_randn_like_layout1_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_randn_like_layout4_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_sum_layout3_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_chebyshev_polynomial_t_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_hermite_polynomial_h_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_hermite_polynomial_he_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_laguerre_polynomial_l_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_shifted_chebyshev_polynomial_v_cpu, test/test_ops.py::TestCommonCPU::test_errors_t_copy_cpu, test/test_ops.py::TestCommonCPU::test_errors_trace_cpu, test/test_ops.py::TestCommonCPU::test_errors_view_as_cpu, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_arange_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_argmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_argmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_column_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cummax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_erfc_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_floor_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_frac_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_gather_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_inner_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_isin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_kron_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_ldl_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_lstsq_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_lu_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_matrix_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_pinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_slogdet_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_tensorinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_masked_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_matmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nanquantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_native_batch_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_gelu_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ormqr_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_rad2deg_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_rsqrt_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_searchsorted_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_signbit_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_erfcx_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_legendre_polynomial_p_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_squeeze_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sub_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_true_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___rmatmul___cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_abs_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_acosh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_acosh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_add_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_aminmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_any_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argsort_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argsort_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_partial_views_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_asinh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atanh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bernoulli_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bitwise_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bitwise_left_shift_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bool_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_tensors_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_to_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bucketize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ceil_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_char_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_char_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cholesky_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chunk_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_max_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_column_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_combinations_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_conj_physical_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_constant_pad_nd_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cos_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_count_nonzero_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cummax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diag_embed_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_digamma_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_einsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_empty_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eq_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_erfc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_as_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_eye_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ihfft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fliplr_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_float_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_float_power_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_floor_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_frexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gather_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gradient_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_histogram_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_igamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_fill_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_put_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_prod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_inner_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isposinf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_item_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_2inputs_2outputs_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_unary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_kron_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_le_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cholesky_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eig_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigvals_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_inv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_vector_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log1p_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log1p_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logdet_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lu_unpack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mH_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_argmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_mean_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_std_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_max_reduction_no_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_max_reduction_with_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_multinomial_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanmedian_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanquantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_narrow_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_native_dropout_backward_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_neg_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nextafter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_elu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_hardsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_hardtanh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool3d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_constant_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_reflect_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_relu6_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nonzero_static_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_norm_nuc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_normal_number_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ones_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_permute_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_4_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_qr_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_quantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_repeat_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize_as__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_resolve_conj_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resolve_conj_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_resolve_neg_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_round_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_searchsorted_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_scatter_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_short_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_cosine_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_hamming_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signbit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_softmax_with_dtype_cpu_int64, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_half_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_half_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_hsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_index_select_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_isclose_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_isin_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_isneginf_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_item_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_jiterator_2inputs_2outputs_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_jiterator_4inputs_with_extra_args_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_lu_factor_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_pinv_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_tensorinv_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_vander_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_vecdot_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_linalg_vecdot_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_log10_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_logical_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_logit_cpu_int64, 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test/test_ops.py::TestMathBitsCPU::test_conj_view_atleast_3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_constant_pad_nd_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_contiguous_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cov_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_dstack_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_empty_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_fft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_fftshift_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_hfft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_ifftshift_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_irfft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_flip_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_float_power_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_full_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_index_select_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_int_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_istft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_ldl_factor_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_ldl_factor_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_matrix_rank_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_tensorinv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logaddexp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logical_or_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logspace_tensor_overload_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_logsumexp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_matmul_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_movedim_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ne_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_channel_shuffle_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_normalize_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pad_replicate_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pixel_unshuffle_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_tanhshrink_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_norm_fro_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_norm_inf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_norm_nuc_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ones_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_rand_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_randn_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_reciprocal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_resize__cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sinh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_std_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_stft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_svd_lowrank_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_t_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tensor_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tensordot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_true_divide_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_uniform_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_vdot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_vsplit_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_zero__cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___radd___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___rmatmul___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_float_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_long_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_abs_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_any_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_as_strided_partial_views_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_asin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_chunk_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_clone_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_conj_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_empty_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expand_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_fft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_hfft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_ifftshift_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_item_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_diagonal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_matrix_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_vecdot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_log_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_or_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_meshgrid_variadic_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_nn_functional_pairwise_distance_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_normal__in_place_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_pow_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_prod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_real_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reciprocal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reshape_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_rot90_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_split_with_sizes_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_squeeze_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_to_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_transpose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__unsafe_masked_index_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addbmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addr_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_allclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_scatter_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_asinh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_atleast_3d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_baddbmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_bool_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_broadcast_to_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cdouble_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cfloat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cholesky_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_chunk_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_contiguous_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cumprod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cumsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_diag_embed_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_exp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_fft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_hfftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_ifftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_flip_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_geqrf_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_gradient_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_index_put_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_isclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_isreal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_item_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cholesky_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_eigh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_eigvalsh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lu_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_solve_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_vector_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_log1p_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logaddexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logcumsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mH_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_cumprod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_matmul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_meshgrid_list_of_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_meshgrid_variadic_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_narrow_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_neg_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_empty_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_ones_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv1d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nonzero_static_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_ones_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_pow_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_prod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_rand_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_randn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_rsqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_rsub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_scatter_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sigmoid_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_list_args_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_squeeze_multiple_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sum_to_size_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_take_along_dim_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_to_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_transpose_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_unbind_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view___getitem___cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view___rdiv___cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view___rmatmul___cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_byte_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_cfloat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_float_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_addr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_amin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_broadcast_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_clamp_max_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_contiguous_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cos_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diag_embed_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_digamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_div_no_rounding_mode_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_erfc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_expm1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fill_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_flip_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_floor_divide_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_frexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_ge_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_gt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_heaviside_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_hsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_igamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_index_add_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_lerp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_lgamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_matrix_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_svd_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log_normal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_meshgrid_variadic_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_movedim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nan_to_num_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_ne_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_empty_strided_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_dropout_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_hardtanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_layer_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_margin_ranking_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_pairwise_distance_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_poisson_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softmin_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softshrink_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_normal__in_place_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_normal_number_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_positive_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_pow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_prod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_rad2deg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_reshape_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_rsub_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sigmoid_cpu_float64, 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test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_hsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_log1p_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nanquantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardswish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_selu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_rad2deg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ravel_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_remainder_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_roll_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_round_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_topk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_vdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_where_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___radd___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rpow___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__batch_norm_with_update_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addmm_decomposed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_block_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cartesian_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cdouble_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_ceil_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cholesky_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_combinations_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_contiguous_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cosh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_digamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_erfc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_det_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eig_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eigvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_vecdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_vector_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lu_unpack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nanquantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_narrow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_celu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardswish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_mish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_mse_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polar_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_ravel_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_triangular_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unbind_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_view_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagonal_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagonal_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_expm1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ihfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_irfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_full_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_histc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_istft_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_kron_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_det_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_det_singular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lu_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_pinv_singular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_native_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rand_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randint_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randint_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_round_decimals_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_transpose_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_transpose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unsafe_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_vsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rmod___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__chunk_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_all_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atan2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_baddbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bitwise_or_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bitwise_right_shift_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cartesian_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cauchy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_chalf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_column_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_conj_physical_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_einsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erfc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expand_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expand_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_exponential_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_hfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flip_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_frac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isclose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_unary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_det_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nan_to_num_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nanmean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_native_dropout_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_new_empty_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_new_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nextafter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_silu_complex_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nonzero_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_permute_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resize__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resolve_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_slice_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_split_with_sizes_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_triu_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_vsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_complex128, test/test_ops.py::TestTagsCPU::test_tags___rdiv___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rmatmul___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rmul___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_bfloat16_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_cdouble_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_cfloat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_char_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_long_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_asin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atan2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atleast_2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_bitwise_not_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_ceil_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diagonal_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_div_trunc_rounding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_empty_strided_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_eq_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_equal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_erfinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_hfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_rfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_gcd_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_ge_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_hstack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_i0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_igammac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_index_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_isposinf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_istft_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_svd_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logical_not_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logical_or_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logsumexp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_lt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_new_ones_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_gelu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sinc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_multigammaln_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_zeta_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_std_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_tan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_transpose_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_var_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_view_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_view_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_vstack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_acos_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_add_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addmm_decomposed_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_aminmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_angle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_arange_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_argmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_argwhere_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_as_strided_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_atanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_baddbmm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bernoulli_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bitwise_not_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_bucketize_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cholesky_inverse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_contiguous_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cov_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cummin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_div_trunc_rounding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_einsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_strided_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_expm1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_exponential_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_hfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_ifft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_ifftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_ihfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fmod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_full_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_gcd_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_grid_sampler_2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_igamma_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_put_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isfinite_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isinf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isnan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isreal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_lcm_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_linalg_det_singular_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_multi_dot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_norm_subgradients_at_zero_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_solve_ex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_vector_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logaddexp2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logdet_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logical_xor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mT_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_softmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nanmean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nanmedian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_native_layer_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_new_full_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_celu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_in_place_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_permute_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_positive_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rad2deg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_randn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reciprocal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_remainder_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_repeat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_round_decimals_0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rsqrt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_short_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signbit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_airy_ai_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_i1e_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_squeeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_mean_unbiased_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_t_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_to_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tril_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_triu_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_var_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_vdot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_where_cpu_float32 2024-12-18T01:40:02.3682727Z 2024-12-18T01:40:05.3769528Z Running test_decomp 10/10 ... [2024-12-18 01:40:05.376602] 2024-12-18T01:40:05.3770109Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:40:05.3771914Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=10', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:40:05.376920] 2024-12-18T01:44:24.6383798Z 2024-12-18T01:44:24.6384795Z test_decomp 6/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_6.10_59487bc5a6ef38d7_.log 2024-12-18T01:44:24.6682604Z Running 930 items in this shard: test/test_decomp.py::TestDecompCPU::test_bernoulli_default_cpu, test/test_decomp.py::TestDecompCPU::test_comprehensive_H_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rpow___cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_offsets_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_all_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_amax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_aminmax_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_angle_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmax_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argwhere_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_partial_views_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_partial_views_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan2_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_and_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_and_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_block_diag_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_block_diag_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bool_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_bool_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_broadcast_tensors_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_broadcast_to_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cartesian_prod_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cdist_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cdist_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cdouble_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_ceil_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_char_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_char_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cholesky_inverse_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_chunk_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_chunk_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_chunk_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_max_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_max_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_max_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_min_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_clone_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_clone_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_conj_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_conj_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_conj_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_constant_pad_nd_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_constant_pad_nd_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_contiguous_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_copysign_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cos_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cosh_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_count_nonzero_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cumulative_trapezoid_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cumulative_trapezoid_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_deg2rad_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_embed_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_diff_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_digamma_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_dist_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_floor_rounding_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_no_rounding_mode_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_trunc_rounding_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_dot_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_dot_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_double_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_double_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_dsplit_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_dstack_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_einsum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_like_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_like_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_like_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_strided_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_strided_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_erf_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_erf_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_erfc_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_erfc_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_erfinv_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_exp2_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_as_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_exponential_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fft_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fft_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fftn_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fftshift_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfftn_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifftn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifftn_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ihfft2_cpu_float64, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_float_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_power_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_power_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_power_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmin_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmin_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmod_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmod_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmod_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_frexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_gather_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_geometric_cpu_bfloat16, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_t_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_take_along_dim_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_take_along_dim_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_tensor_split_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_tile_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_tile_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_trace_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_transpose_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapezoid_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapz_cpu_int8, 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test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_addmv_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_addr_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_addr_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_amax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_amin_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_any_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_asinh_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_asinh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_atan2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_atanh_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_atanh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_atanh_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_baddbmm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_and_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_not_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_xor_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_bucketize_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_clamp_min_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_clamp_min_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_clone_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_conj_physical_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_conj_physical_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_copysign_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_core_backward__unsafe_masked_index_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_diag_embed_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_max_unpool3d_grad_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_norm_inf_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_rad2deg_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_t_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_unbind_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_cumsum_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_diag_embed_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_diag_embed_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_bfloat16, 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test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_floor_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_fmod_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_frac_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_ge_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_ge_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_geometric_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_igamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_index_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_isinf_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_isposinf_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_linalg_cross_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_linalg_cross_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_linalg_cross_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_log10_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_log10_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_log1p_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_log_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_log_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_log_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_logical_not_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_xor_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_logical_xor_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_logical_xor_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_logit_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_logspace_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logspace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_lt_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_lt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_lt_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_masked_fill_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mean_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_minimum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_narrow_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_narrow_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_strided_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_new_ones_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_binary_cross_entropy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_binary_cross_entropy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_gelu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardshrink_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardtanh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_logsigmoid_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool2d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softshrink_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_norm_inf_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_norm_inf_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_normal_in_place_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_ones_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_ones_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_rad2deg_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_randn_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_renorm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_round_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_select_scatter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_special_erfcx_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_special_i1_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_special_log_ndtr_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_zeta_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_bool, 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test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_take_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_tan_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_tan_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_tanh_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_triu_indices_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_unbind_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_var_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_int64, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_GRU_eval_mode_cpu_float32, test/test_decomp.py::DecompOneOffTestsCPU::test_contiguous_softmax_cpu, test/test_decomp.py::DecompOneOffTestsCPU::test_native_layer_norm_cpu_decomp_cpu 2024-12-18T01:44:24.6972172Z 2024-12-18T01:44:28.0336341Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2024-12-18 01:44:28.033280] 2024-12-18T01:44:28.0337095Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:44:28.0338540Z 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'] ... [2024-12-18 01:44:28.033604] 2024-12-18T01:44:31.8027248Z 2024-12-18T01:44:31.8029005Z 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_765535bde7398fce_.log 2024-12-18T01:44:31.8041442Z 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 2024-12-18T01:44:31.8052652Z 2024-12-18T01:44:35.2865127Z Running test_functional_optim 1/1 ... [2024-12-18 01:44:35.286159] 2024-12-18T01:44:35.2865617Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:44:35.2867197Z 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'] ... [2024-12-18 01:44:35.286489] 2024-12-18T01:44:38.8053880Z 2024-12-18T01:44:38.8054799Z test_functional_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_optim_1.1_5dc70938f368deb2_.log 2024-12-18T01:44:38.8057153Z 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 2024-12-18T01:44:38.8059162Z 2024-12-18T01:44:42.4195526Z Running nn/test_load_state_dict 1/1 ... [2024-12-18 01:44:42.419168] 2024-12-18T01:44:42.4196358Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:44:42.4198762Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_load_state_dict.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'] ... [2024-12-18 01:44:42.419531] 2024-12-18T01:44:46.8420405Z 2024-12-18T01:44:46.8421801Z nn/test_load_state_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_load_state_dict_1.1_274c80318fb6a299_.log 2024-12-18T01:44:46.8443138Z Running 27 items in this shard: test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_BC_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_BC_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_False_keep_vars_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_False_keep_vars_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_True_keep_vars_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_True_keep_vars_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_shape_stride_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_shape_stride_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_with_optimizer_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_with_optimizer_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_child_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_child_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_custom_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_custom_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_invalid_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_invalid_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_ref_cycle_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_type_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_type_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_warn_assign_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_warn_assign_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_with_unexpected_key_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_with_unexpected_key_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDictSwap::test_swap_subclass_swap_True_assign_False, test/nn/test_load_state_dict.py::TestLoadStateDictSwap::test_swap_subclass_swap_True_assign_True 2024-12-18T01:44:46.8463578Z 2024-12-18T01:44:50.7346210Z Running torch_np/test_function_base 1/1 ... [2024-12-18 01:44:50.734224] 2024-12-18T01:44:50.7347073Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:44:50.7349429Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_function_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'] ... [2024-12-18 01:44:50.734603] 2024-12-18T01:44:55.0068173Z 2024-12-18T01:44:55.0069667Z torch_np/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_function_base_1.1_c2b2ff9a1724fe6f_.log 2024-12-18T01:44:55.0071837Z Running 1 items in this shard: test/torch_np/test_function_base.py::TestAppend::test_basic 2024-12-18T01:44:55.0072688Z 2024-12-18T01:44:58.7043591Z Running test_mkldnn 1/1 ... [2024-12-18 01:44:58.703943] 2024-12-18T01:44:58.7044313Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:44:58.7046283Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mkldnn.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'] ... [2024-12-18 01:44:58.704320] 2024-12-18T01:45:34.9106992Z 2024-12-18T01:45:34.9108287Z test_decomp 1/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.10_4586edb714fd330d_.log 2024-12-18T01:45:34.9614503Z Running 867 items in this shard: test/test_decomp.py::TestDecompCPU::test_comprehensive_H_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rand___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmatmul___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmatmul___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive__chunk_cat_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_abs_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addcmul_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_all_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_arange_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_argwhere_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argwhere_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_asin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_asin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bincount_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_bincount_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_or_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_block_diag_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_broadcast_to_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bucketize_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_byte_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cat_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cauchy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cdouble_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_max_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_complex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_conj_physical_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_contiguous_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_copysign_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_corrcoef_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cos_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_cos_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cosh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_count_nonzero_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cross_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cumsum_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_embed_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagflat_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_scatter_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diff_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_diff_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_floor_rounding_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_no_rounding_mode_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_no_rounding_mode_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_trunc_rounding_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_dot_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_double_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_dsplit_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_dsplit_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_strided_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_eq_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_equal_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_equal_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_equal_cpu_uint8, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_eigvals_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_eigvals_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_eigvalsh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_inv_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lstsq_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lu_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lu_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lu_factor_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lu_factor_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_multi_dot_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_ex_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_ex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_solve_triangular_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_svdvals_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_tensorinv_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_tensorsolve_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_vector_norm_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_vector_norm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_vector_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_vector_norm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_log1p_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_normal_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_with_dtype_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logaddexp2_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logaddexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logcumsumexp_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_logcumsumexp_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_xor_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_xor_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_xor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logspace_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_long_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_lu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_mH_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_mT_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumprod_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumprod_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumsum_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumsum_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_fill_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_log_softmax_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logaddexp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_mean_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_mean_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_mean_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_normalize_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_prod_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_select_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_softmin_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_sum_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_matrix_exp_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_matrix_exp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_binary_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_reduction_with_dim_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_mean_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_binary_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_reduction_no_dim_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_minimum_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_mode_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_movedim_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mul_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_mul_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nan_to_num_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nan_to_num_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nan_to_num_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nanmedian_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nanquantile_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_narrow_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_narrow_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_batch_norm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_dropout_backward_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_layer_norm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_native_layer_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_ne_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_neg_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_empty_strided_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_ones_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_new_zeros_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_batch_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_batch_norm_cpu_float64, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_embedding_bag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_glu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_instance_norm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_instance_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_interpolate_bicubic_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_interpolate_bilinear_cpu_bfloat16, 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test/test_decomp.py::TestDecompCPU::test_quick_core_backward_addcmul_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_special_entr_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_trace_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cumsum_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_float32, 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test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fmod_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_gcd_cpu_int8, 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_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_isin_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_lerp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_lerp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_linalg_vector_norm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_log1p_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_logaddexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_logical_not_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_logical_xor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_maximum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_maximum_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_minimum_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_minimum_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_native_batch_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_ne_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_ne_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_new_ones_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_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_glu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_huber_loss_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_logsigmoid_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool2d_grad_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool3d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_bool, 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_prelu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_prelu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_silu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softshrink_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_norm_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_norm_inf_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_normal_in_place_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_randn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_round_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_round_decimals_neg_3_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_select_scatter_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_i0e_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_i1_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_special_log_ndtr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtri_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_std_unbiased_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_take_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_tanh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_trace_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_trace_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_complex128, 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_float16, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_zeros_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_int16, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_GRU_train_mode_cpu_float32, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_LSTM_eval_mode_cpu_float64, test/test_decomp.py::TestDecompCPU::test_uniform_cpu, test/test_decomp.py::DecompOneOffTestsCPU::test_sdpa_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_decomp.py::DecompOneOffTestsCPU::test_weight_norm_interface_cpu 2024-12-18T01:45:35.0106546Z 2024-12-18T01:45:36.2181117Z Uploading artifacts took 1.30 seconds 2024-12-18T01:45:39.3757143Z Running test_xnnpack_integration 1/1 ... [2024-12-18 01:45:39.375188] 2024-12-18T01:45:39.3758545Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:45:39.3760643Z 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'] ... [2024-12-18 01:45:39.375628] 2024-12-18T01:45:57.5116298Z 2024-12-18T01:45:57.5117827Z test_xnnpack_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_xnnpack_integration_1.1_ffdb9fcbedb28000_.log 2024-12-18T01:45:57.5126698Z 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 2024-12-18T01:45:57.5134407Z 2024-12-18T01:46:01.4089088Z 2024-12-18T01:46:01.4090327Z test_mkldnn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_1.1_76feb624085c74a1_.log 2024-12-18T01:46:01.4133040Z Running 83 items in this shard: test/test_mkldnn.py::TestMkldnnCPU::test_0_dimension_tensor_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_adaptive_avg_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_adaptive_avg_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_add_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_autograd_from_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_autograd_to_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_stride_none_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_clone_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv1d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv1d_functional_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv2d_legacy_jit_model_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_1d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_1d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_2d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_2d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_3d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_3d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_fp32_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose1d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_fp32_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conversion_byte_char_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conversion_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_copy_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_detach_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_empty_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_gelu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_gelu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_is_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_is_mkldnn_jit_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_legacy_new_failure_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_backward_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_lowp_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_linear_lowp_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_linear_non_contiguous_weight_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_lstm_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_matmul_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_matmul_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_stride_none_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool_unsupported_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_mkldnn_conv_shapecheck_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_mul_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_prelu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_prelu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu__cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_inplace_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_repr_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_backward_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_blocked_format_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_resnet18_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_resnext50_32x4d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_set_data_tensorimpl_type_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_sigmoid_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_softmax_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_tanh_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_transpose_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_transpose_invalid_dime_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_unsupported_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_view_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_zero__cpu 2024-12-18T01:46:01.4175271Z 2024-12-18T01:46:01.7210008Z Running test_indexing 1/1 ... [2024-12-18 01:46:01.720581] 2024-12-18T01:46:01.7210770Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:01.7212768Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_indexing.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'] ... [2024-12-18 01:46:01.720926] 2024-12-18T01:46:04.8699994Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2024-12-18 01:46:04.869462] 2024-12-18T01:46:04.8700702Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:04.8702514Z 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'] ... [2024-12-18 01:46:04.869988] 2024-12-18T01:46:06.8002736Z 2024-12-18T01:46:06.8004073Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_da8c65ffe99ef5ae_.log 2024-12-18T01:46:06.8030011Z Running 88 items in this shard: test/test_indexing.py::TestIndexingCPU::test_advancedindex_big_cpu, test/test_indexing.py::TestIndexingCPU::test_advancedindex_cpu_float16, test/test_indexing.py::TestIndexingCPU::test_advancedindex_cpu_float64, test/test_indexing.py::TestIndexingCPU::test_basic_advanced_combined_cpu, test/test_indexing.py::TestIndexingCPU::test_bool_indices_accumulate_cpu, test/test_indexing.py::TestIndexingCPU::test_bool_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_mask2d_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_mask_accumulate_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_mask_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_tensor_assignment_cpu, test/test_indexing.py::TestIndexingCPU::test_cpu_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_cuda_broadcast_index_use_deterministic_algorithms_cpu, test/test_indexing.py::TestIndexingCPU::test_ellipsis_tensor_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_index_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_ndim_index_bool_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_ndim_index_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_slice_cpu, test/test_indexing.py::TestIndexingCPU::test_gather_take_along_dim_cross_device_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_getitem_scalars_cpu, test/test_indexing.py::TestIndexingCPU::test_index_cpu, test/test_indexing.py::TestIndexingCPU::test_index_getitem_copy_bools_slices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_ind_dtype_cpu, test/test_indexing.py::TestIndexingCPU::test_index_limits_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_duplicate_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_empty_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_expanded_values_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_large_tensor_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_non_contiguous_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_with_optional_tensors_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_large_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_bfloat16, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_bool, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_complex128, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_complex64, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_index_scalar_with_bool_mask_cpu, test/test_indexing.py::TestIndexingCPU::test_index_setitem_bools_slices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_bfloat16, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_bool, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_int_assignment_cpu, test/test_indexing.py::TestIndexingCPU::test_int_indices2d_cpu, test/test_indexing.py::TestIndexingCPU::test_int_indices_broadcast_cpu, test/test_indexing.py::TestIndexingCPU::test_int_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_invalid_device_cpu, test/test_indexing.py::TestIndexingCPU::test_invalid_index_cpu, test/test_indexing.py::TestIndexingCPU::test_jit_indexing_cpu, test/test_indexing.py::TestIndexingCPU::test_multiple_bool_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_multiple_byte_mask_cpu, test/test_indexing.py::TestIndexingCPU::test_multiple_int_cpu, test/test_indexing.py::TestIndexingCPU::test_none_cpu, test/test_indexing.py::TestIndexingCPU::test_out_of_bound_index_cpu, test/test_indexing.py::TestIndexingCPU::test_set_item_to_scalar_tensor_cpu, test/test_indexing.py::TestIndexingCPU::test_setitem_expansion_error_cpu, test/test_indexing.py::TestIndexingCPU::test_setitem_scalars_cpu, test/test_indexing.py::TestIndexingCPU::test_single_int_cpu, test/test_indexing.py::TestIndexingCPU::test_step_assignment_cpu, test/test_indexing.py::TestIndexingCPU::test_step_cpu, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_invalid_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_invalid_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_unravel_index_errors_cpu, test/test_indexing.py::TestIndexingCPU::test_variable_slicing_cpu, test/test_indexing.py::TestIndexingCPU::test_zero_dim_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_assignment_value_mismatch_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_alldims_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_onedim_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_twodim_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_weirdness_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_weirdness_tensors_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_list_indexing_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_shape_mismatch_cpu, test/test_indexing.py::NumpyTestsCPU::test_broadcast_subspace_cpu, test/test_indexing.py::NumpyTestsCPU::test_broaderrors_indexing_cpu, test/test_indexing.py::NumpyTestsCPU::test_ellipsis_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_empty_fancy_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_empty_tuple_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_everything_returns_views_cpu, test/test_indexing.py::NumpyTestsCPU::test_index_is_larger_cpu, test/test_indexing.py::NumpyTestsCPU::test_index_no_floats_cpu, test/test_indexing.py::NumpyTestsCPU::test_none_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_single_bool_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_single_int_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_trivial_fancy_out_of_bounds_cpu, test/test_indexing.py::NumpyTestsCPU::test_truncate_leading_1s_cpu 2024-12-18T01:46:06.8055787Z 2024-12-18T01:46:09.1907621Z 2024-12-18T01:46:09.1908748Z 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_57ee812ad0e92f33_.log 2024-12-18T01:46:09.1935006Z 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 2024-12-18T01:46:09.1960401Z 2024-12-18T01:46:10.3478016Z Running test_datapipe 1/1 ... [2024-12-18 01:46:10.347451] 2024-12-18T01:46:10.3478514Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:10.3481120Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_datapipe.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'] ... [2024-12-18 01:46:10.347882] 2024-12-18T01:46:12.5797639Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2024-12-18 01:46:12.579389] 2024-12-18T01:46:12.5798360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:12.5799917Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.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'] ... [2024-12-18 01:46:12.579720] 2024-12-18T01:46:19.5537479Z 2024-12-18T01:46:19.5538667Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_64b5686853b3becd_.log 2024-12-18T01:46:19.5572408Z Running 79 items in this shard: test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTShift::test_fft_n, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_all_1d_norm_preserving, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_hfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_identity, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_backward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_forward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_ortho, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ihfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_ifft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_rfft 2024-12-18T01:46:19.5603460Z 2024-12-18T01:46:23.0998996Z Running torch_np/test_ndarray_methods 1/1 ... [2024-12-18 01:46:23.099500] 2024-12-18T01:46:23.0999862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:23.1001973Z 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'] ... [2024-12-18 01:46:23.099823] 2024-12-18T01:46:28.0362173Z 2024-12-18T01:46:28.0363676Z test_datapipe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_datapipe_1.1_7305b4726a77a105_.log 2024-12-18T01:46:28.0420842Z Running 94 items in this shard: test/test_datapipe.py::TestDataChunk::test_as_string, test/test_datapipe.py::TestDataChunk::test_getitem, test/test_datapipe.py::TestDataChunk::test_iter, test/test_datapipe.py::TestDataChunk::test_len, test/test_datapipe.py::TestDataChunk::test_random_shuffle, test/test_datapipe.py::TestDataChunk::test_reverse, test/test_datapipe.py::TestDataChunk::test_sort, test/test_datapipe.py::TestStreamWrapper::test_api, test/test_datapipe.py::TestStreamWrapper::test_dir, test/test_datapipe.py::TestStreamWrapper::test_pickle, test/test_datapipe.py::TestStreamWrapper::test_repr, test/test_datapipe.py::TestIterableDataPipeBasic::test_demux_mux_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_groupby_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_listdirfiles_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_listdirfilesdeterministic_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_map_with_col_file_handle_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_openfilesfromdisk_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_routeddecoder_iterable_datapipe, test/test_datapipe.py::TestCaptureDataFrame::test_basic_capture, test/test_datapipe.py::TestDataFramesPipes::test_batch, test/test_datapipe.py::TestDataFramesPipes::test_capture, test/test_datapipe.py::TestDataFramesPipes::test_collate, test/test_datapipe.py::TestDataFramesPipes::test_filter, test/test_datapipe.py::TestDataFramesPipes::test_shuffle, test/test_datapipe.py::TestDataFramesPipes::test_unbatch, test/test_datapipe.py::TestFunctionalIterDataPipe::test_batch_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_collate_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_concat_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_demux_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_docstring, test/test_datapipe.py::TestFunctionalIterDataPipe::test_filter_datapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_fork_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_iterable_wrapper_datapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_dict_with_col_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_tuple_list_with_col_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_mux_iterdatapipe, 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test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data61, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data62, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data63, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data64, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data65, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data66, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data67, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data68, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data69, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data7, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data70, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data71, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data72, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data73, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data8, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data9, test/torch_np/test_ndarray_methods.py::TestArgmin::test_minimum_signed_integers, test/torch_np/test_ndarray_methods.py::TestAmax::test_basic, test/torch_np/test_ndarray_methods.py::TestAmin::test_basic, test/torch_np/test_ndarray_methods.py::TestContains::test_contains, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_fn, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_ivar, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_method, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_name, 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 2024-12-18T01:46:30.2011660Z 2024-12-18T01:46:31.3766907Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2024-12-18 01:46:31.376231] 2024-12-18T01:46:31.3767919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:31.3770565Z 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'] ... [2024-12-18 01:46:31.376655] 2024-12-18T01:46:33.5156582Z Running torch_np/numpy_tests/core/test_dlpack 1/1 ... [2024-12-18 01:46:33.515254] 2024-12-18T01:46:33.5157534Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:33.5159809Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_dlpack.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'] ... [2024-12-18 01:46:33.515593] 2024-12-18T01:46:35.1459152Z 2024-12-18T01:46:35.1460386Z 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_ca5eddf50a1f78a8_.log 2024-12-18T01:46:35.1473422Z 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 2024-12-18T01:46:35.1485799Z 2024-12-18T01:46:37.2355523Z 2024-12-18T01:46:37.2356620Z torch_np/numpy_tests/core/test_dlpack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dlpack_1.1_920ba1e87080ff25_.log 2024-12-18T01:46:37.2376012Z Running 53 items in this shard: test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dlpack_destructor_exception, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dlpack_device, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype1, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype2, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype3, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype4, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype5, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype6, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype7, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype8, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype9, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dunder_dlpack_refcount, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dunder_dlpack_stream, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_from_dlpack_refcount, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_from_torch, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_1, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_10, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_11, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_12, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_13, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_14, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_15, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_16, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_17, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_18, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_19, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_2, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_20, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_21, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_22, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_23, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_24, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_25, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_26, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_27, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_28, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_29, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_3, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_30, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_31, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_32, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_4, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_5, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_6, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_7, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_8, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_9, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_ndim0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_non_contiguous, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_readonly, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_to_torch 2024-12-18T01:46:37.2394443Z 2024-12-18T01:46:38.4982182Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2024-12-18 01:46:38.497878] 2024-12-18T01:46:38.4982756Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:38.4984575Z 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'] ... [2024-12-18 01:46:38.498221] 2024-12-18T01:46:40.5899866Z Running nn/test_module_hooks 1/1 ... [2024-12-18 01:46:40.589570] 2024-12-18T01:46:40.5900379Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:40.5901759Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_module_hooks.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'] ... [2024-12-18 01:46:40.589929] 2024-12-18T01:46:42.3175256Z 2024-12-18T01:46:42.3176472Z 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_5a531254c94d98ad_.log 2024-12-18T01:46:42.3194691Z 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 2024-12-18T01:46:42.3212291Z 2024-12-18T01:46:44.3592568Z 2024-12-18T01:46:44.3594148Z nn/test_module_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_module_hooks_1.1_c5ccc801cf4bb482_.log 2024-12-18T01:46:44.3631087Z Running 53 items in this shard: test/nn/test_module_hooks.py::TestModuleHooks::test_always_called_forward_hooks, test/nn/test_module_hooks.py::TestModuleHooks::test_bw_hook_warning_for_non_tensor_or_tuple, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_pre_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_pre_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_pre_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_pre_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_kwarg_hooks, test/nn/test_module_hooks.py::TestModuleHooks::test_mixed_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_mixed_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_remove_kwarg_hooks, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_module_pre_hook_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_module_pre_hook_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_backward_compatibility_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_backward_compatibility_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_pre_hook_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_pre_hook_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_no_extra_ref_to_module, test/nn/test_module_hooks.py::TestStateDictHooks::test_pickled_hook, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_post_hook_private_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_post_hook_private_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_pre_hook, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_pre_hook_backward_compat, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_pre_hook_lazy_module, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_global_and_local_hooks_order, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_backward_global_hook_writeable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_forward_forward_hook_removable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_forward_preforward_hook_removable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_forward_preforward_hook_writeable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_hook_invalid_outputs, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_hooks, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_hooks_with_kwargs, test/nn/test_module_hooks.py::TestModuleHookNN::test_backward_hooks_interaction, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_backward_size, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_backward_writeable, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_buffer_registration, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_cpp, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_extra_input, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_forward_preforward_writable, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_inplace, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_invalid_outputs, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_last_arg_requires_grad, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_no_requires_grad, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_non_full_warning, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_parameter_registration, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_requires_grad, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_submodule_registration, test/nn/test_module_hooks.py::TestModuleHookNN::test_hooks 2024-12-18T01:46:44.3666984Z 2024-12-18T01:46:45.7551397Z Running test_function_schema 1/1 ... [2024-12-18 01:46:45.754803] 2024-12-18T01:46:45.7551892Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:45.7553581Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_function_schema.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'] ... [2024-12-18 01:46:45.755126] 2024-12-18T01:46:47.7298811Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2024-12-18 01:46:47.729555] 2024-12-18T01:46:47.7299434Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:47.7301209Z 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'] ... [2024-12-18 01:46:47.729869] 2024-12-18T01:46:49.4741065Z 2024-12-18T01:46:49.4742018Z test_function_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_function_schema_1.1_b092659fe589ccb7_.log 2024-12-18T01:46:49.4748259Z Running 15 items in this shard: test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_arguments, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_outputs, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_structure, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_with_smart_serialization, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_real_use_case, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_with_out, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_without_out, test/test_function_schema.py::TestFunctionSchema::test_hash_schema, test/test_function_schema.py::TestFunctionSchema::test_out_schema, test/test_function_schema.py::TestFunctionSchema::test_schema_error, test/test_function_schema.py::TestFunctionSchema::test_serialize_and_deserialize, test/test_function_schema.py::TestFunctionSchema::test_string_optional_parameter_default_value, test/test_function_schema.py::TestFunctionSchema::test_sym_int_argument_properly_parsed, test/test_function_schema.py::TestFunctionSchema::test_tensor_list_alias_annotation_properly_parsed, test/test_function_schema.py::TestFunctionSchema::test_tensor_option_arguments_properly_parsed 2024-12-18T01:46:49.4755192Z 2024-12-18T01:46:52.8261876Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2024-12-18 01:46:52.825751] 2024-12-18T01:46:52.8262677Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:52.8264509Z 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'] ... [2024-12-18 01:46:52.826169] 2024-12-18T01:46:55.5054226Z 2024-12-18T01:46:55.5055338Z 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_6b43803ecf3bb86a_.log 2024-12-18T01:46:55.5059192Z 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 2024-12-18T01:46:55.5061947Z 2024-12-18T01:46:56.5454997Z 2024-12-18T01:46:56.5456595Z 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_512220f1ac37236f_.log 2024-12-18T01:46:56.5490724Z 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 2024-12-18T01:46:56.5523421Z 2024-12-18T01:46:58.9332551Z Running test_schema_check 1/1 ... [2024-12-18 01:46:58.932846] 2024-12-18T01:46:58.9333334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:58.9335805Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_schema_check.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'] ... [2024-12-18 01:46:58.933265] 2024-12-18T01:47:00.0153104Z Running test_quantization 2/2 ... [2024-12-18 01:47:00.014883] 2024-12-18T01:47:00.0153894Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:00.0156106Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.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'] ... [2024-12-18 01:47:00.015251] 2024-12-18T01:49:03.0211493Z 2024-12-18T01:49:03.0213043Z test_decomp 10/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_10.10_0cee1942ce7aade4_.log 2024-12-18T01:49:03.0740994Z Running 874 items in this shard: test/test_decomp.py::TestDecompCPU::test_batch_norm_unflatten_weight_bias_cpu, test/test_decomp.py::TestDecompCPU::test_comprehensive_H_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmod___cpu_float32, 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___rpow___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_lengths_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_lengths_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_offsets_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_abs_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_abs_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_abs_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addcmul_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmv_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmv_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_all_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_allclose_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_allclose_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_amax_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_angle_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_angle_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_angle_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_arange_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_arange_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_partial_views_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_scatter_cpu_bool, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_linspace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_normal_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_with_dtype_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logaddexp_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logdet_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_and_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_and_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_and_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_not_cpu_float32, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_normalize_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_std_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_sum_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_sum_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_matmul_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_matmul_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_matrix_exp_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_reduction_with_dim_cpu_int32, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_tril_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_triu_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_true_divide_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_true_divide_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_trunc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unflatten_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unflatten_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_unfold_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_unravel_index_cpu_int64, 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_unsafe_split_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsafe_split_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsqueeze_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsqueeze_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_unsqueeze_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_var_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_var_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_var_unbiased_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_vdot_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_vdot_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_as_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_vsplit_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_zero__cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_zero__cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_zero__cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick__batch_norm_with_update_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_put_accumulate_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_abs_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_abs_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_acosh_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_addcdiv_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_addcdiv_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_addmv_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addmv_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_amin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_amin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_aminmax_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_any_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_any_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_scatter_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_atan2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_baddbmm_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_bernoulli_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_bernoulli_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_left_shift_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_or_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_or_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_xor_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_cauchy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_bfloat16, 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_complex_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_frac_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_mvlgamma_mvlgamma_p_1_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_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nan_to_num_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_max_unpool2d_grad_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_unfold_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_rot90_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_sinc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_split_with_sizes_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_std_mean_unbiased_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_triu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_cumsum_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_diag_embed_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_eq_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_erf_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_bfloat16, 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_complex64, test/test_decomp.py::TestDecompCPU::test_quick_expm1_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft2_cpu_complex128, 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_ifft_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_floor_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_floor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_fmod_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_gcd_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_grid_sampler_2d_cpu_float32, 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_gt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_hypot_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_hypot_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_isinf_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_isinf_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_isinf_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_isposinf_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_lcm_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_linalg_cross_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_linalg_vector_norm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_log10_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_log10_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_log1p_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_log1p_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_log_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_logaddexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_logical_not_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_logspace_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_logspace_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_logsumexp_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_masked_fill_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_masked_fill_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_masked_fill_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_native_layer_norm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_strided_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_new_ones_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_nextafter_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_embedding_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_embedding_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_glu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardsigmoid_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardsigmoid_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool3d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_mse_loss_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_mse_loss_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softshrink_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_unfold_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_normal_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_ones_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_rad2deg_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_randn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_renorm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_round_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_erfcx_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_i0e_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_i1e_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_special_i1e_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_i1e_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_special_log_ndtr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_float32, 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_stack_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_std_mean_unbiased_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_std_mean_unbiased_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_std_unbiased_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_tan_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_trace_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_tril_indices_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_unbind_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unfold_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_unbiased_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_complex64, test/test_decomp.py::HasDecompTest::test_has_decomposition 2024-12-18T01:49:03.1255728Z 2024-12-18T01:49:07.6396744Z Running optim/test_lrscheduler 1/1 ... [2024-12-18 01:49:07.639220] 2024-12-18T01:49:07.6397388Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:49:07.6401091Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_lrscheduler.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'] ... [2024-12-18 01:49:07.639720] 2024-12-18T01:49:11.8724943Z 2024-12-18T01:49:11.8726478Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_5c62760a066d1779_.log 2024-12-18T01:49:11.8784061Z 2024-12-18T01:53:20.0164776Z 2024-12-18T01:53:20.0166494Z test_schema_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_schema_check_1.1_0d5e2320d29867b6_.log 2024-12-18T01:53:20.3705965Z Running 6000 items in this shard: test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_output_is_input, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_secretly_aliasing, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_secretly_mutating, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_multiple_operators, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_multiple_operators_centered, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_outputs_unexpectedly_aliasing, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_simple, test/test_schema_check.py::TestSchemaCheck::test_is_alias_of_basic, test/test_schema_check.py::TestSchemaCheck::test_is_alias_of_empty_container, test/test_schema_check.py::TestSchemaCheck::test_mutation_check_fail, test/test_schema_check.py::TestSchemaCheck::test_mutation_check_fail_multiple_operators, test/test_schema_check.py::TestSchemaCheck::test_overlaps_basic, test/test_schema_check.py::TestSchemaCheck::test_overlaps_empty_container, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_empty_list_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_aliasing_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_default_replaced, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_device_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_kwarg_tensor, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_list_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_mutable_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_nested_training_op, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_training_op, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_wildcard_after, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_with_multiple_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_with_multiple_outputs_aliasing, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_aliasing_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_aliasing_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_as_strided, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_multiple_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_mutation, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_none, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_resize_, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_operator_order, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_operator_order_without_grad, test/test_schema_check.py::TestSchemaCheck::test_schema_info_bind_basic, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmod___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmod___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmod___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmod___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmul___cpu_int32, 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test/test-reports/test_quantization_2.2_9b3a535b2675f7df_.log 2024-12-18T01:53:55.9456895Z Running 545 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool, test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool3d_ndhwc, test/test_quantization.py::TestQuantizedOps::test_add_scalar_relu, test/test_quantization.py::TestQuantizedOps::test_advanced_indexing, test/test_quantization.py::TestQuantizedOps::test_avg_pool2d, test/test_quantization.py::TestQuantizedOps::test_avg_pool3d, test/test_quantization.py::TestQuantizedOps::test_avg_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_batch_norm, test/test_quantization.py::TestQuantizedOps::test_cat, test/test_quantization.py::TestQuantizedOps::test_custom_module_lstm, test/test_quantization.py::TestQuantizedOps::test_custom_module_multi_head_attention, 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test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_duplicate_nodes, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_multiple_users, test/test_quantization.py::TestQuantizeFx::test_dequantize, test/test_quantization.py::TestQuantizeFx::test_dict_output, test/test_quantization.py::TestQuantizeFx::test_dynamic_linear_input_multiple_use, test/test_quantization.py::TestQuantizeFx::test_dynamic_quant_weight_observer, test/test_quantization.py::TestQuantizeFx::test_fold_quant_dequant, test/test_quantization.py::TestQuantizeFx::test_fp32_input_fp32_output, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_fused_module_qat_swap, test/test_quantization.py::TestQuantizeFx::test_fusion_pattern_unquantized, test/test_quantization.py::TestQuantizeFx::test_get_default_qconfig_valid_backend, test/test_quantization.py::TestQuantizeFx::test_getattr_with_nontensor_result, test/test_quantization.py::TestQuantizeFx::test_linear_bn, test/test_quantization.py::TestQuantizeFx::test_linear_leaky_relu_lowering, test/test_quantization.py::TestQuantizeFx::test_linear_qint8_activation, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_linear_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_mixed_dtypes, test/test_quantization.py::TestQuantizeFx::test_not_used, test/test_quantization.py::TestQuantizeFx::test_observer_fqn, test/test_quantization.py::TestQuantizeFx::test_packed_weight_fused_op, test/test_quantization.py::TestQuantizeFx::test_pattern_match_constant, test/test_quantization.py::TestQuantizeFx::test_permute_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_float_to_observed_mapping, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_classes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_class, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_prepare_mode, test/test_quantization.py::TestQuantizeFx::test_preserve_qconfig, test/test_quantization.py::TestQuantizeFx::test_preserve_tuple, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_args, 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_split_list_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_split_tuple_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qconfig_for_call_method, test/test_quantization.py::TestQuantizeFx::test_qconfig_function, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_from_dict, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_object_type, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_type, test/test_quantization.py::TestQuantizeFx::test_qconfig_none, test/test_quantization.py::TestQuantizeFx::test_qconfig_precedence, test/test_quantization.py::TestQuantizeFx::test_qnnpack_backend_config, test/test_quantization.py::TestQuantizeFx::test_qparams_buffers, test/test_quantization.py::TestQuantizeFx::test_quantized_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_quantized_model_type, test/test_quantization.py::TestQuantizeFx::test_ref_linear_module, test/test_quantization.py::TestQuantizeFx::test_remove_qconfig, test/test_quantization.py::TestQuantizeFx::test_return_none, test/test_quantization.py::TestQuantizeFx::test_reuse_input_qconfig, test/test_quantization.py::TestQuantizeFx::test_save_observer_state_dict, test/test_quantization.py::TestQuantizeFx::test_shape_followed_by_quantized_op, test/test_quantization.py::TestQuantizeFx::test_stack_trace_preserved_linear, test/test_quantization.py::TestQuantizeFx::test_standalone_module_float_interface, test/test_quantization.py::TestQuantizeFx::test_standalone_module_quantized_interface, test/test_quantization.py::TestQuantizeFx::test_state_dict, test/test_quantization.py::TestQuantizeFx::test_static_lstm_consume_tuple, test/test_quantization.py::TestQuantizeFx::test_symmetric_qnnpack_qat_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_symmetric_qnnpack_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_torch_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_torch_unsqueeze_nontensor_args_not_observed, 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_ave_pool_with_custom_cfg, test/test_quantization.py::TestQuantizeFxOps::test_boolean_tensor, test/test_quantization.py::TestQuantizeFxOps::test_cat, test/test_quantization.py::TestQuantizeFxOps::test_chunk, test/test_quantization.py::TestQuantizeFxOps::test_conv_module, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeFxOps::test_copy_node_fp32_input, test/test_quantization.py::TestQuantizeFxOps::test_div, test/test_quantization.py::TestQuantizeFxOps::test_elu, test/test_quantization.py::TestQuantizeFxOps::test_embedding, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops, test/test_quantization.py::TestQuantizeFxOps::test_gelu_normal, test/test_quantization.py::TestQuantizeFxOps::test_hardswish, test/test_quantization.py::TestQuantizeFxOps::test_instance_norm, test/test_quantization.py::TestQuantizeFxOps::test_int8_input_no_unnecessary_fq, test/test_quantization.py::TestQuantizeFxOps::test_layer_norm, test/test_quantization.py::TestQuantizeFxOps::test_leaky_relu, test/test_quantization.py::TestQuantizeFxOps::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizeFxOps::test_mish_reference, test/test_quantization.py::TestQuantizeFxOps::test_norm_weight_bias, test/test_quantization.py::TestQuantizeFxOps::test_qbatch_norm, test/test_quantization.py::TestQuantizeFxOps::test_quantized_add_qat, test/test_quantization.py::TestQuantizeFxOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeFxOps::test_quantized_mul_qat, test/test_quantization.py::TestQuantizeFxOps::test_rnn, test/test_quantization.py::TestQuantizeFxOps::test_silu_reference, test/test_quantization.py::TestQuantizeFxOps::test_softmax_reference, test/test_quantization.py::TestQuantizeFxOps::test_sum, test/test_quantization.py::TestQuantizeFxModels::test_qat_functional_linear, test/test_quantization.py::TestQuantizeFxModels::test_resnet18_ddp, test/test_quantization.py::TestQuantizeFxModels::test_resnet_base, test/test_quantization.py::TestQuantizeFxModels::test_switch_device_prepare_convert, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_writer_replace_consecutive_submodules, test/test_quantization.py::TestGraphUtils::test_conv_bn_conv_relu, test/test_quantization.py::TestGraphUtils::test_conv_bn_relu, test/test_quantization.py::TestDuplicateDQPass::test_avgpool_use_different_qconfig, test/test_quantization.py::TestDuplicateDQPass::test_no_add_quant_duplicate_dq, test/test_quantization.py::TestDuplicateDQPass::test_simple_duplicate_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_dq_no_static_q, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_with_no_quant_inbetween, test/test_quantization.py::TestMetaDataPorting::test_simple_metadata_porting, test/test_quantization.py::TestNumericDebugger::test_control_flow, test/test_quantization.py::TestNumericDebugger::test_prepare_for_propagation_comparison, test/test_quantization.py::TestFXGraphMatcher::test_dict_return_type, test/test_quantization.py::TestFXGraphMatcher::test_methods, test/test_quantization.py::TestFXGraphMatcher::test_nodes_before_cat, test/test_quantization.py::TestFXGraphMatcher::test_simple_fun, test/test_quantization.py::TestFXGraphMatcher::test_simple_fusion, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod, test/test_quantization.py::TestFXGraphMatcher::test_simple_tensor_ops, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_meth_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extend_logger_results_with_comparison, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_linear_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_layer_names, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_weights, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_loggers_preserve_qat_numerics, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_logging_inputs, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_mul_add_cat_stack_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_unsupported_op_copy_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_defined_function, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_module_scriptable, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_conv_bn_relu_fusion_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_conv_bn_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_partial_qconfig_mapping, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_deduplication, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_insert_padding, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_ordering, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_retroactive_padding, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_mobilenet_v2, test/test_quantization.py::TestFxModelReportDetector::test_multi_linear_model_without_per_channel, test/test_quantization.py::TestFxModelReportDetector::test_multiple_q_config_options, test/test_quantization.py::TestFxModelReportDetector::test_sequential_model_format, test/test_quantization.py::TestFxModelReportDetector::test_simple_conv, test/test_quantization.py::TestFxModelReportObserver::test_random_epochs_and_batches, test/test_quantization.py::TestFxModelReportObserver::test_zero_tensor_errors, test/test_quantization.py::TestFxModelReportDetectDynamicStatic::test_nested_detection_case, test/test_quantization.py::TestFxModelReportClass::test_equalization_mapping_generation, test/test_quantization.py::TestFxModelReportClass::test_generate_report, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_determine_points, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen, test/test_quantization.py::TestFxDetectOutliers::test_all_outlier_report_gen, test/test_quantization.py::TestFxDetectOutliers::test_no_outlier_report_gen, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_match_with_report, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_single_feat_match, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_convert, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_equalization_scales, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_prepare, 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_v2, test/test_quantization.py::TestSerialization::test_conv2d_graph_v3, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v2, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v3, test/test_quantization.py::TestSerialization::test_conv2d_relu, test/test_quantization.py::TestSerialization::test_conv3d_relu, test/test_quantization.py::TestSerialization::test_default_qat_qconfig, test/test_quantization.py::TestSerialization::test_linear, test/test_quantization.py::TestSerialization::test_linear_relu, test/test_quantization.py::TestQuantizeJit::test_nested, test/test_quantization.py::TestQuantizeJit::test_skip_quant, test/test_quantization.py::TestQuantizeJitPasses::test_conv_trace, test/test_quantization.py::TestQuantizeJitPasses::test_dedup_module_uses, test/test_quantization.py::TestQuantizeJitPasses::test_finalize_debug, test/test_quantization.py::TestQuantizeJitPasses::test_finalize_for_linear, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_shared_classtype, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_trivial_nobias, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_if_consistent_observation, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface_unshare_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_for_function, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_interface_with_fork, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize_in_block, test/test_quantization.py::TestQuantizeJitOps::test_clamp, test/test_quantization.py::TestQuantizeJitOps::test_conv_with_benchmark_flag, test/test_quantization.py::TestQuantizeJitOps::test_elu, test/test_quantization.py::TestQuantizeJitOps::test_hardswish, test/test_quantization.py::TestQuantizeJitOps::test_instance_norm, test/test_quantization.py::TestQuantizeJitOps::test_layer_norm, test/test_quantization.py::TestQuantizeJitOps::test_linear, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNFuncRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_relu_alpha, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_scalar_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_cat, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar_relu, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_multi_op, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_quant_multi_uses, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_weight_observer, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_with_if, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_prepare_dynamic_child_qconfig, test/test_quantization.py::TestQuantizeDynamicJitOps::test_embedding_bag, test/test_quantization.py::TestQuantizeDynamicJitOps::test_embedding_bag_padding_idx_error, test/test_quantization.py::TestFusionPasses::test_quantized_add_relu_fusion, test/test_quantization.py::TestDeprecatedJitQuantized::test_erase_class_tensor_shapes, test/test_quantization.py::TestDeprecatedJitQuantized::test_quantization_modules, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_cell_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_fake_quantize, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantization_mappings, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_stubs, test/test_quantization.py::TestAOMigrationNNQuantized::test_functional_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_dynamic_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_embedding_ops, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_rnn, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_activation, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_dropout, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_normalization, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_quantized, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_conv_fused, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_bn_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_linear_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fuse, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_graph_module, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_match_utils, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_pattern_utils, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_quantization_patterns, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_quantize_fx, 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_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_pt2_traceable_aot_eager_cpu_float8_e4m3fn 2024-12-18T01:53:55.9637678Z 2024-12-18T01:53:56.6680635Z Running test batch 'tests to run' cost 7257.42 seconds 2024-12-18T01:53:57.4736457Z 2024-12-18T01:53:57.4736877Z real 121m1.705s 2024-12-18T01:53:57.4737184Z user 630m14.618s 2024-12-18T01:53:57.4737426Z sys 41m18.263s 2024-12-18T01:53:57.4737675Z + assert_git_not_dirty 2024-12-18T01:53:57.4737990Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-18T01:53:57.4738367Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2024-12-18T01:53:57.4742479Z ++ git status --porcelain 2024-12-18T01:53:57.4743443Z ++ grep -v '?? third_party' 2024-12-18T01:54:13.7464733Z ++ true 2024-12-18T01:54:13.7465630Z + git_status= 2024-12-18T01:54:13.7465954Z + [[ -n '' ]] 2024-12-18T01:54:13.7466198Z + test_aten 2024-12-18T01:54:13.7466506Z + echo 'Running ATen tests with pytorch lib' 2024-12-18T01:54:13.7466934Z Running ATen tests with pytorch lib 2024-12-18T01:54:13.7467278Z + [[ -n '' ]] 2024-12-18T01:54:13.7467674Z + echo 'Running test with the build folder' 2024-12-18T01:54:13.7468074Z Running test with the build folder 2024-12-18T01:54:13.7468415Z + TEST_BASE_DIR=build/bin 2024-12-18T01:54:13.7468987Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2024-12-18T01:54:13.7492725Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2024-12-18T01:54:13.7502278Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2024-12-18T01:54:13.7511637Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2024-12-18T01:54:13.7522800Z + 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 2024-12-18T01:54:13.7530196Z + ls build/bin 2024-12-18T01:54:13.7572401Z BackoffTest cpu_generator_test 2024-12-18T01:54:13.7572953Z CMakeFiles cpu_profiling_allocator_test 2024-12-18T01:54:13.7573341Z CTestTestfile.cmake cpu_rng_test 2024-12-18T01:54:13.7573981Z CppSignature_test dispatch_key_set_test 2024-12-18T01:54:13.7574416Z Dict_test dlconvertor_test 2024-12-18T01:54:13.7574756Z Dimname_test example_allreduce 2024-12-18T01:54:13.7575135Z FileStoreTest extension_backend_test 2024-12-18T01:54:13.7575502Z HashStoreTest half_test 2024-12-18T01:54:13.7575848Z IListRef_test inline_container_test 2024-12-18T01:54:13.7576271Z KernelFunction_test ivalue_test 2024-12-18T01:54:13.7576655Z List_test kernel_function_legacy_test 2024-12-18T01:54:13.7577034Z Makefile kernel_function_test 2024-12-18T01:54:13.7577421Z MaybeOwned_test kernel_lambda_legacy_test 2024-12-18T01:54:13.7577805Z NamedTensor_test kernel_lambda_test 2024-12-18T01:54:13.7578230Z ProcessGroupGlooTest kernel_stackbased_test 2024-12-18T01:54:13.7578646Z StorageUtils_test lazy_tensor_test 2024-12-18T01:54:13.7579013Z TCPStoreTest legacy_vmap_test 2024-12-18T01:54:13.7579363Z aot_model_compiler_test libc10.so 2024-12-18T01:54:13.7579717Z apply_utils_test 'libcaffe2*' 2024-12-18T01:54:13.7580031Z atest 'libmkldnn*' 2024-12-18T01:54:13.7580342Z backend_fallback_test 'libnccl*' 2024-12-18T01:54:13.7580681Z basic libtorch.so 2024-12-18T01:54:13.7580991Z broadcast_test libtorch_cpu.so 2024-12-18T01:54:13.7581371Z c10_ArrayRef_test libtorch_global_deps.so 2024-12-18T01:54:13.7581751Z c10_Bitset_test libtorch_python.so 2024-12-18T01:54:13.7582184Z c10_CompileTimeFunctionPointer_test libtorchbind_test.so 2024-12-18T01:54:13.7582732Z c10_ConstexprCrc_test make_boxed_from_unboxed_functor_test 2024-12-18T01:54:13.7583211Z c10_DeadlockDetection_test math_kernel_test 2024-12-18T01:54:13.7583622Z c10_DeviceGuard_test memory_format_test 2024-12-18T01:54:13.7584023Z c10_Device_test memory_overlapping_test 2024-12-18T01:54:13.7584423Z c10_DispatchKeySet_test mobile_memory_cleanup 2024-12-18T01:54:13.7584811Z c10_Half_test native_test 2024-12-18T01:54:13.7585182Z c10_InlineDeviceGuard_test op_allowlist_test 2024-12-18T01:54:13.7585625Z c10_InlineStreamGuard_test op_registration_test 2024-12-18T01:54:13.7586050Z c10_LeftRight_test operator_name_test 2024-12-18T01:54:13.7586428Z c10_Metaprogramming_test operators_test 2024-12-18T01:54:13.7586860Z c10_NetworkFlow_test packedtensoraccessor_test 2024-12-18T01:54:13.7587271Z c10_Scalar_test parallel_benchmark 2024-12-18T01:54:13.7587633Z c10_SizesAndStrides_test pow_test 2024-12-18T01:54:13.7587984Z c10_StreamGuard_test protoc 2024-12-18T01:54:13.7588306Z c10_SymInt_test protoc-3.13.0.0 2024-12-18T01:54:13.7588666Z c10_Synchronized_test quantized_test 2024-12-18T01:54:13.7589046Z c10_ThreadLocal_test reduce_ops_test 2024-12-18T01:54:13.7589435Z c10_TypeIndex_test reportMemoryUsage_test 2024-12-18T01:54:13.7589830Z c10_TypeList_test scalar_tensor_test 2024-12-18T01:54:13.7590175Z c10_TypeTraits_test scalar_test 2024-12-18T01:54:13.7590545Z c10_accumulate_test static_runtime_bench 2024-12-18T01:54:13.7590933Z c10_bfloat16_test static_runtime_test 2024-12-18T01:54:13.7591317Z c10_bit_cast_test stride_properties_test 2024-12-18T01:54:13.7591719Z c10_complex_math_test tensor_iterator_test 2024-12-18T01:54:13.7592083Z c10_complex_test test_api 2024-12-18T01:54:13.7592407Z c10_cow_test test_cpp_rpc 2024-12-18T01:54:13.7592745Z c10_error_test test_dist_autograd 2024-12-18T01:54:13.7593137Z c10_exception_test test_edge_op_registration 2024-12-18T01:54:13.7593516Z c10_flags_test test_jit 2024-12-18T01:54:13.7593887Z c10_generic_math_test test_lazy 2024-12-18T01:54:13.7594353Z c10_intrusive_ptr_benchmark test_mobile_nnc 2024-12-18T01:54:13.7594754Z c10_intrusive_ptr_test test_parallel 2024-12-18T01:54:13.7595121Z c10_irange_test test_tensorexpr 2024-12-18T01:54:13.7595453Z c10_lazy_test thread_init_test 2024-12-18T01:54:13.7595804Z c10_logging_test torch_shm_manager 2024-12-18T01:54:13.7596181Z c10_optional_test tutorial_tensorexpr 2024-12-18T01:54:13.7596595Z c10_ordered_preserving_dict_test type_ptr_test 2024-12-18T01:54:13.7596979Z c10_registry_test type_test 2024-12-18T01:54:13.7597360Z c10_small_vector_test undefined_tensor_test 2024-12-18T01:54:13.7597774Z c10_ssize_test vec_test_all_types_AVX2 2024-12-18T01:54:13.7598191Z c10_string_util_test vec_test_all_types_AVX512 2024-12-18T01:54:13.7598717Z c10_string_view_test vec_test_all_types_DEFAULT 2024-12-18T01:54:13.7599129Z c10_tempfile_test verify_api_visibility 2024-12-18T01:54:13.7599507Z c10_typeid_test weakref_test 2024-12-18T01:54:13.7599860Z cmake_install.cmake wrapdim_test 2024-12-18T01:54:13.7600223Z cpu_allocator_test xla_tensor_test 2024-12-18T01:54:13.7600583Z + aten/tools/run_tests.sh build/bin 2024-12-18T01:54:13.7603814Z + set -e 2024-12-18T01:54:13.7606639Z ++ dirname aten/tools/run_tests.sh 2024-12-18T01:54:13.7620104Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-12-18T01:54:13.7620581Z + export CPP_TESTS_DIR=build/bin 2024-12-18T01:54:13.7620909Z + CPP_TESTS_DIR=build/bin 2024-12-18T01:54:13.7621216Z + VALGRIND=ON 2024-12-18T01:54:13.7623019Z + 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 2024-12-18T01:54:13.8646287Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-18T01:54:13.8647212Z import pkg_resources 2024-12-18T01:54:17.0344270Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-18T01:54:17.0495786Z Found test times from artifacts 2024-12-18T01:54:17.1108656Z Found test times from artifacts 2024-12-18T01:54:17.1130068Z Running all tests 2024-12-18T01:54:17.1134485Z Running parallel tests on 3 processes 2024-12-18T01:54:17.1135505Z Name: tests to run (est. time: 0.0min) 2024-12-18T01:54:17.1135875Z Serial tests (0): 2024-12-18T01:54:17.1136163Z Parallel tests (19): 2024-12-18T01:54:17.1136447Z cpp/Dict_test 1/1 2024-12-18T01:54:17.1136748Z cpp/Dimname_test 1/1 2024-12-18T01:54:17.1137059Z cpp/NamedTensor_test 1/1 2024-12-18T01:54:17.1137350Z cpp/apply_utils_test 1/1 2024-12-18T01:54:17.1137644Z cpp/atest 1/1 2024-12-18T01:54:17.1137901Z cpp/basic 1/1 2024-12-18T01:54:17.1138161Z cpp/broadcast_test 1/1 2024-12-18T01:54:17.1138465Z cpp/cpu_generator_test 1/1 2024-12-18T01:54:17.1138757Z cpp/dlconvertor_test 1/1 2024-12-18T01:54:17.1139063Z cpp/extension_backend_test 1/1 2024-12-18T01:54:17.1139382Z cpp/lazy_tensor_test 1/1 2024-12-18T01:54:17.1139681Z cpp/legacy_vmap_test 1/1 2024-12-18T01:54:17.1139958Z cpp/native_test 1/1 2024-12-18T01:54:17.1140235Z cpp/operators_test 1/1 2024-12-18T01:54:17.1140531Z cpp/scalar_tensor_test 1/1 2024-12-18T01:54:17.1140827Z cpp/scalar_test 1/1 2024-12-18T01:54:17.1141111Z cpp/tensor_iterator_test 1/1 2024-12-18T01:54:17.1141414Z cpp/undefined_tensor_test 1/1 2024-12-18T01:54:17.1141730Z cpp/wrapdim_test 1/1 2024-12-18T01:54:17.1142187Z Name: excluded (est. time: 0.0min) 2024-12-18T01:54:17.1142604Z Serial tests (0): 2024-12-18T01:54:17.1142882Z Parallel tests (0): 2024-12-18T01:54:17.1189605Z Running cpp/Dict_test 1/1 ... [2024-12-18 01:54:17.118717] 2024-12-18T01:54:17.1190037Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:17.1194081Z 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-477c2f73be6ef63d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:17.119159] 2024-12-18T01:54:18.9373759Z 2024-12-18T01:54:18.9374952Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_bb22d7e031ab3c6f_.log 2024-12-18T01:54:18.9375617Z 2024-12-18T01:54:18.9375818Z Running cpp/Dimname_test 1/1 ... [2024-12-18 01:54:18.937249] 2024-12-18T01:54:18.9376244Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:18.9378401Z 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-6193441891dcf2bf.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:18.937603] 2024-12-18T01:54:20.6045311Z 2024-12-18T01:54:20.6046261Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_e2cf461f8dbb8617_.log 2024-12-18T01:54:20.6046987Z 2024-12-18T01:54:20.6047201Z Running cpp/NamedTensor_test 1/1 ... [2024-12-18 01:54:20.604358] 2024-12-18T01:54:20.6047698Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:20.6049496Z 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-f8bbfa144a18e18d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:20.604701] 2024-12-18T01:54:22.2714507Z 2024-12-18T01:54:22.2715880Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_9088752658698fcf_.log 2024-12-18T01:54:22.2716973Z 2024-12-18T01:54:22.2717342Z Running cpp/apply_utils_test 1/1 ... [2024-12-18 01:54:22.271301] 2024-12-18T01:54:22.2718004Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:22.2720205Z 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-febca74f0f01f77d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:22.271698] 2024-12-18T01:54:23.9384453Z 2024-12-18T01:54:23.9385423Z 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_e879790716090d53_.log 2024-12-18T01:54:23.9386093Z 2024-12-18T01:54:23.9386276Z Running cpp/atest 1/1 ... [2024-12-18 01:54:23.938313] 2024-12-18T01:54:23.9386688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:23.9388750Z 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-0413cbdbfc7a61bc.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:23.938660] 2024-12-18T01:54:25.6052653Z 2024-12-18T01:54:25.6053709Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_0d5e9250c060dc44_.log 2024-12-18T01:54:25.6054293Z 2024-12-18T01:54:25.6054465Z Running cpp/basic 1/1 ... [2024-12-18 01:54:25.605144] 2024-12-18T01:54:25.6054861Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:25.6057321Z 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-0a0d25e710849169.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:25.605474] 2024-12-18T01:54:27.2723383Z 2024-12-18T01:54:27.2724588Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_c9c01315942f2e09_.log 2024-12-18T01:54:27.2725330Z 2024-12-18T01:54:27.2725545Z Running cpp/broadcast_test 1/1 ... [2024-12-18 01:54:27.272173] 2024-12-18T01:54:27.2725964Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:27.2727196Z 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-363733ab77493bfa.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:27.272499] 2024-12-18T01:54:29.0394995Z 2024-12-18T01:54:29.0396849Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_f44ce3de7f6d322d_.log 2024-12-18T01:54:29.0397673Z 2024-12-18T01:54:29.0397910Z Running cpp/cpu_generator_test 1/1 ... [2024-12-18 01:54:29.039283] 2024-12-18T01:54:29.0398464Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:29.0399805Z 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-4062507fe703d7fb.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:29.039614] 2024-12-18T01:54:30.7062438Z 2024-12-18T01:54:30.7063640Z 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_aa302b484499a9b8_.log 2024-12-18T01:54:30.7064351Z 2024-12-18T01:54:30.7064578Z Running cpp/dlconvertor_test 1/1 ... [2024-12-18 01:54:30.706115] 2024-12-18T01:54:30.7065050Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:30.7067364Z 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-89ac91f345ccc492.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:30.706487] 2024-12-18T01:54:32.3731667Z 2024-12-18T01:54:32.3732715Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_b29263069ba8a9c1_.log 2024-12-18T01:54:32.3733468Z 2024-12-18T01:54:32.3733719Z Running cpp/extension_backend_test 1/1 ... [2024-12-18 01:54:32.373014] 2024-12-18T01:54:32.3734254Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:32.3735669Z 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-19d2e1bd96eae36a.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:32.373359] 2024-12-18T01:54:34.0402654Z 2024-12-18T01:54:34.0404025Z 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_4379b8c57172395f_.log 2024-12-18T01:54:34.0405182Z 2024-12-18T01:54:34.0405555Z Running cpp/lazy_tensor_test 1/1 ... [2024-12-18 01:54:34.040130] 2024-12-18T01:54:34.0406240Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:34.0408362Z 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-0ddcb693db7a4a29.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:34.040526] 2024-12-18T01:54:35.6572373Z 2024-12-18T01:54:35.6573551Z 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_51922a5818409564_.log 2024-12-18T01:54:35.6574778Z 2024-12-18T01:54:35.6575017Z Running cpp/legacy_vmap_test 1/1 ... [2024-12-18 01:54:35.657119] 2024-12-18T01:54:35.6575500Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:35.6577348Z 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-ce4a9555d0f76ecb.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:35.657460] 2024-12-18T01:54:37.3243954Z 2024-12-18T01:54:37.3244913Z 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_f3f89a3f911e85ef_.log 2024-12-18T01:54:37.3245611Z 2024-12-18T01:54:37.3245805Z Running cpp/native_test 1/1 ... [2024-12-18 01:54:37.324293] 2024-12-18T01:54:37.3246232Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:37.3248639Z 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-907d70236866b429.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:37.324618] 2024-12-18T01:54:38.9914961Z 2024-12-18T01:54:38.9916221Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_7bbacd8bb9772577_.log 2024-12-18T01:54:38.9917278Z 2024-12-18T01:54:38.9917623Z Running cpp/operators_test 1/1 ... [2024-12-18 01:54:38.991376] 2024-12-18T01:54:38.9918366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:38.9920566Z 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-06117d5ec5ec654b.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:38.991771] 2024-12-18T01:54:40.6585653Z 2024-12-18T01:54:40.6586892Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_ec53ff5d839840ee_.log 2024-12-18T01:54:40.6587609Z 2024-12-18T01:54:40.6587831Z Running cpp/scalar_tensor_test 1/1 ... [2024-12-18 01:54:40.658419] 2024-12-18T01:54:40.6588335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:40.6589976Z 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-51d85d32e5884f57.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:40.658740] 2024-12-18T01:54:42.3253159Z 2024-12-18T01:54:42.3254556Z 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_99e030e6361ceebe_.log 2024-12-18T01:54:42.3255700Z 2024-12-18T01:54:42.3256030Z Running cpp/scalar_test 1/1 ... [2024-12-18 01:54:42.325164] 2024-12-18T01:54:42.3256665Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:42.3258810Z 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-b1e34ff1a0b8a849.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:42.325550] 2024-12-18T01:54:43.9922720Z 2024-12-18T01:54:43.9923988Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_2791254b57946a3d_.log 2024-12-18T01:54:43.9924650Z 2024-12-18T01:54:43.9924895Z Running cpp/tensor_iterator_test 1/1 ... [2024-12-18 01:54:43.992146] 2024-12-18T01:54:43.9925531Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:43.9927176Z 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-03881ad43f105350.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:43.992438] 2024-12-18T01:54:45.6091293Z 2024-12-18T01:54:45.6092853Z 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_0d728fe13d7a0917_.log 2024-12-18T01:54:45.6094079Z 2024-12-18T01:54:45.6094430Z Running cpp/undefined_tensor_test 1/1 ... [2024-12-18 01:54:45.608971] 2024-12-18T01:54:45.6095208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:45.6098007Z 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-5b86c2a615ade367.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:45.609355] 2024-12-18T01:54:47.2764124Z 2024-12-18T01:54:47.2765317Z 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_1a998f5dad7e0f12_.log 2024-12-18T01:54:47.2766056Z 2024-12-18T01:54:47.2766265Z Running cpp/wrapdim_test 1/1 ... [2024-12-18 01:54:47.276246] 2024-12-18T01:54:47.2766696Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:47.2768406Z 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-a6cac62b8bc2919f.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:47.276608] 2024-12-18T01:54:48.9435355Z 2024-12-18T01:54:48.9436319Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_75fd06b70ea930d7_.log 2024-12-18T01:54:48.9436977Z 2024-12-18T01:54:48.9446864Z Running cpp/Dict_test 1/1 ... [2024-12-18 01:54:48.944430] 2024-12-18T01:54:48.9447316Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:48.9447799Z Running cpp/Dimname_test 1/1 ... [2024-12-18 01:54:48.944613] 2024-12-18T01:54:48.9448234Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:48.9448700Z Running cpp/NamedTensor_test 1/1 ... [2024-12-18 01:54:48.944663] 2024-12-18T01:54:48.9449188Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:48.9451802Z 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-01df6c08bead2599.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:48.944929] 2024-12-18T01:54:48.9454060Z 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-c51cd30a08eb071c.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:48.945086] 2024-12-18T01:54:48.9456339Z 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-6438af29e31d7a95.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:48.945152] 2024-12-18T01:54:51.6642495Z 2024-12-18T01:54:51.6643946Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_cde95f3cf79f4a62_.log 2024-12-18T01:54:51.6645189Z 2024-12-18T01:54:52.5157167Z 2024-12-18T01:54:52.5158367Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_569c9ebe54955c1a_.log 2024-12-18T01:54:52.5159376Z 2024-12-18T01:54:55.1689525Z Running cpp/apply_utils_test 1/1 ... [2024-12-18 01:54:55.168574] 2024-12-18T01:54:55.1690076Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:55.1692332Z 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-a4224c1a36700dd6.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:55.168971] 2024-12-18T01:54:55.9435976Z Running cpp/atest 1/1 ... [2024-12-18 01:54:55.943171] 2024-12-18T01:54:55.9436717Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:54:55.9439731Z 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-dd31f54c12df26c3.xml', '-x', '--reruns=2'] ... [2024-12-18 01:54:55.943632] 2024-12-18T01:54:56.8726154Z 2024-12-18T01:54:56.8727734Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_8c2f9a2e43fc8765_.log 2024-12-18T01:54:56.8728585Z 2024-12-18T01:54:57.7879290Z 2024-12-18T01:54:57.7880712Z 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_75d37fa4957c98b6_.log 2024-12-18T01:54:57.7881994Z 2024-12-18T01:55:00.0646522Z 2024-12-18T01:55:00.0647508Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_5041ef0577b46d28_.log 2024-12-18T01:55:00.0648160Z 2024-12-18T01:55:00.3750752Z Running cpp/basic 1/1 ... [2024-12-18 01:55:00.374775] 2024-12-18T01:55:00.3751474Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:00.3754270Z 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-e085da5ef3dfa565.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:00.375182] 2024-12-18T01:55:01.2896418Z Running cpp/broadcast_test 1/1 ... [2024-12-18 01:55:01.289212] 2024-12-18T01:55:01.2897303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:01.2902017Z 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-b04681c55b401cfc.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:01.289830] 2024-12-18T01:55:03.0437739Z 2024-12-18T01:55:03.0438966Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_897dd749b460767f_.log 2024-12-18T01:55:03.0439912Z 2024-12-18T01:55:03.5253772Z 2024-12-18T01:55:03.5254658Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_30a4ffc252f0653b_.log 2024-12-18T01:55:03.5255318Z 2024-12-18T01:55:03.5685347Z Running cpp/cpu_generator_test 1/1 ... [2024-12-18 01:55:03.568230] 2024-12-18T01:55:03.5685862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:03.5689320Z 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-d6ba140a1f38afa6.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:03.568689] 2024-12-18T01:55:06.5781682Z Running cpp/dlconvertor_test 1/1 ... [2024-12-18 01:55:06.577744] 2024-12-18T01:55:06.5782456Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:06.5786063Z 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-b770999d44b31db2.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:06.578214] 2024-12-18T01:55:06.9872786Z Running cpp/extension_backend_test 1/1 ... [2024-12-18 01:55:06.986852] 2024-12-18T01:55:06.9873791Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:06.9876611Z 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-081deeb0618d53b6.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:06.987302] 2024-12-18T01:55:07.6393554Z 2024-12-18T01:55:07.6395098Z 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_33aa271c2bf1e4c5_.log 2024-12-18T01:55:07.6396389Z 2024-12-18T01:55:08.7466769Z 2024-12-18T01:55:08.7467841Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_17cbe3ec099fd4c2_.log 2024-12-18T01:55:08.7468551Z 2024-12-18T01:55:09.2055650Z 2024-12-18T01:55:09.2057000Z 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_8fb9ea5296beb063_.log 2024-12-18T01:55:09.2057922Z 2024-12-18T01:55:11.1582016Z Running cpp/lazy_tensor_test 1/1 ... [2024-12-18 01:55:11.157866] 2024-12-18T01:55:11.1582515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:11.1585028Z 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-50ddd99916c4686c.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:11.158266] 2024-12-18T01:55:12.2175960Z Running cpp/legacy_vmap_test 1/1 ... [2024-12-18 01:55:12.217168] 2024-12-18T01:55:12.2176445Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:12.2179920Z 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-e8148b9b6477841b.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:12.217688] 2024-12-18T01:55:12.5913354Z Running cpp/native_test 1/1 ... [2024-12-18 01:55:12.590956] 2024-12-18T01:55:12.5913825Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:12.5916204Z 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-0d4ff535ed679f65.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:12.591369] 2024-12-18T01:55:13.2259503Z 2024-12-18T01:55:13.2260724Z 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_d0fa3c3730176916_.log 2024-12-18T01:55:13.2261562Z 2024-12-18T01:55:14.8094478Z 2024-12-18T01:55:14.8095555Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_64d2842015a7ec4b_.log 2024-12-18T01:55:14.8096209Z 2024-12-18T01:55:16.7518926Z Running cpp/operators_test 1/1 ... [2024-12-18 01:55:16.751496] 2024-12-18T01:55:16.7519460Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:16.7521956Z 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-bd07208a1c479909.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:16.751902] 2024-12-18T01:55:17.2403023Z 2024-12-18T01:55:17.2404494Z 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_9c57a272070ac840_.log 2024-12-18T01:55:17.2405810Z 2024-12-18T01:55:18.2402888Z Running cpp/scalar_tensor_test 1/1 ... [2024-12-18 01:55:18.239874] 2024-12-18T01:55:18.2403778Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:18.2406146Z 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-0bab2653af4fe8e4.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:18.240295] 2024-12-18T01:55:19.2703660Z 2024-12-18T01:55:19.2705238Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_42a81334751df89c_.log 2024-12-18T01:55:19.2706448Z 2024-12-18T01:55:20.4084110Z 2024-12-18T01:55:20.4085081Z 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_5d96b338e3b2b1dc_.log 2024-12-18T01:55:20.4085779Z 2024-12-18T01:55:20.7263424Z Running cpp/scalar_test 1/1 ... [2024-12-18 01:55:20.725975] 2024-12-18T01:55:20.7263882Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:20.7266140Z 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-8df6804ea251fb76.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:20.726371] 2024-12-18T01:55:22.7797000Z Running cpp/tensor_iterator_test 1/1 ... [2024-12-18 01:55:22.779265] 2024-12-18T01:55:22.7798081Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:22.7800445Z 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-7a468670e907753f.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:22.779675] 2024-12-18T01:55:23.2442613Z 2024-12-18T01:55:23.2443548Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_981d5ca6bdf68229_.log 2024-12-18T01:55:23.2444209Z 2024-12-18T01:55:23.8719544Z Running cpp/undefined_tensor_test 1/1 ... [2024-12-18 01:55:23.871463] 2024-12-18T01:55:23.8720415Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:23.8722721Z 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-b888c679d775160c.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:23.871920] 2024-12-18T01:55:25.9896817Z 2024-12-18T01:55:25.9897835Z 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_bc5d783998760d27_.log 2024-12-18T01:55:25.9898587Z 2024-12-18T01:55:26.7360722Z Running cpp/wrapdim_test 1/1 ... [2024-12-18 01:55:26.735678] 2024-12-18T01:55:26.7361203Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:55:26.7363439Z 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-5439f9acf9248446.xml', '-x', '--reruns=2'] ... [2024-12-18 01:55:26.736104] 2024-12-18T01:55:28.8542628Z 2024-12-18T01:55:28.8543964Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_6f3c9b5b2447d765_.log 2024-12-18T01:55:28.8544937Z 2024-12-18T01:55:33.0107046Z 2024-12-18T01:55:33.0108347Z 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_25e2b1fe8c019851_.log 2024-12-18T01:55:33.0109440Z 2024-12-18T01:55:33.7398914Z Running test batch 'tests to run' cost 76.63 seconds 2024-12-18T01:55:34.3491751Z + run_if_exists tensor_interop_test 2024-12-18T01:55:34.3492238Z + local test_name=tensor_interop_test 2024-12-18T01:55:34.3492684Z + [[ -x build/bin/tensor_interop_test ]] 2024-12-18T01:55:34.3493261Z + echo 'Warning: tensor_interop_test does not exist.' 2024-12-18T01:55:34.3493875Z Warning: tensor_interop_test does not exist. 2024-12-18T01:55:34.3494388Z + run_if_exists cudnn_test 2024-12-18T01:55:34.3494831Z + local test_name=cudnn_test 2024-12-18T01:55:34.3495224Z + [[ -x build/bin/cudnn_test ]] 2024-12-18T01:55:34.3495706Z + echo 'Warning: cudnn_test does not exist.' 2024-12-18T01:55:34.3496166Z Warning: cudnn_test does not exist. 2024-12-18T01:55:34.3496558Z + run_if_exists cuda_generator_test 2024-12-18T01:55:34.3496894Z + local test_name=cuda_generator_test 2024-12-18T01:55:34.3497275Z + [[ -x build/bin/cuda_generator_test ]] 2024-12-18T01:55:34.3497668Z + echo 'Warning: cuda_generator_test does not exist.' 2024-12-18T01:55:34.3498079Z Warning: cuda_generator_test does not exist. 2024-12-18T01:55:34.3498442Z + run_if_exists apply_test 2024-12-18T01:55:34.3498745Z + local test_name=apply_test 2024-12-18T01:55:34.3499037Z + [[ -x build/bin/apply_test ]] 2024-12-18T01:55:34.3499367Z + echo 'Warning: apply_test does not exist.' 2024-12-18T01:55:34.3499735Z Warning: apply_test does not exist. 2024-12-18T01:55:34.3500062Z + run_if_exists stream_test 2024-12-18T01:55:34.3500359Z + local test_name=stream_test 2024-12-18T01:55:34.3500645Z + [[ -x build/bin/stream_test ]] 2024-12-18T01:55:34.3500972Z + echo 'Warning: stream_test does not exist.' 2024-12-18T01:55:34.3501547Z Warning: stream_test does not exist. 2024-12-18T01:55:34.3501991Z + run_if_exists cuda_half_test 2024-12-18T01:55:34.3502297Z + local test_name=cuda_half_test 2024-12-18T01:55:34.3502594Z + [[ -x build/bin/cuda_half_test ]] 2024-12-18T01:55:34.3502943Z + echo 'Warning: cuda_half_test does not exist.' 2024-12-18T01:55:34.3503317Z Warning: cuda_half_test does not exist. 2024-12-18T01:55:34.3503656Z + run_if_exists cuda_vectorized_test 2024-12-18T01:55:34.3503987Z + local test_name=cuda_vectorized_test 2024-12-18T01:55:34.3504357Z + [[ -x build/bin/cuda_vectorized_test ]] 2024-12-18T01:55:34.3504734Z + echo 'Warning: cuda_vectorized_test does not exist.' 2024-12-18T01:55:34.3505204Z Warning: cuda_vectorized_test does not exist. 2024-12-18T01:55:34.3505575Z + run_if_exists cuda_distributions_test 2024-12-18T01:55:34.3505931Z + local test_name=cuda_distributions_test 2024-12-18T01:55:34.3506295Z + [[ -x build/bin/cuda_distributions_test ]] 2024-12-18T01:55:34.3506690Z + echo 'Warning: cuda_distributions_test does not exist.' 2024-12-18T01:55:34.3507130Z Warning: cuda_distributions_test does not exist. 2024-12-18T01:55:34.3507512Z + run_if_exists cuda_optional_test 2024-12-18T01:55:34.3507843Z + local test_name=cuda_optional_test 2024-12-18T01:55:34.3508184Z + [[ -x build/bin/cuda_optional_test ]] 2024-12-18T01:55:34.3508553Z + echo 'Warning: cuda_optional_test does not exist.' 2024-12-18T01:55:34.3508956Z Warning: cuda_optional_test does not exist. 2024-12-18T01:55:34.3509318Z + run_if_exists cuda_tensor_interop_test 2024-12-18T01:55:34.3509674Z + local test_name=cuda_tensor_interop_test 2024-12-18T01:55:34.3510039Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2024-12-18T01:55:34.3510440Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2024-12-18T01:55:34.3510877Z Warning: cuda_tensor_interop_test does not exist. 2024-12-18T01:55:34.3511250Z + run_if_exists cuda_complex_test 2024-12-18T01:55:34.3511575Z + local test_name=cuda_complex_test 2024-12-18T01:55:34.3511905Z + [[ -x build/bin/cuda_complex_test ]] 2024-12-18T01:55:34.3512261Z + echo 'Warning: cuda_complex_test does not exist.' 2024-12-18T01:55:34.3512658Z Warning: cuda_complex_test does not exist. 2024-12-18T01:55:34.3513013Z + run_if_exists cuda_complex_math_test 2024-12-18T01:55:34.3513355Z + local test_name=cuda_complex_math_test 2024-12-18T01:55:34.3513704Z + [[ -x build/bin/cuda_complex_math_test ]] 2024-12-18T01:55:34.3514091Z + echo 'Warning: cuda_complex_math_test does not exist.' 2024-12-18T01:55:34.3514509Z Warning: cuda_complex_math_test does not exist. 2024-12-18T01:55:34.3514869Z + run_if_exists cuda_cub_test 2024-12-18T01:55:34.3515170Z + local test_name=cuda_cub_test 2024-12-18T01:55:34.3515478Z + [[ -x build/bin/cuda_cub_test ]] 2024-12-18T01:55:34.3515809Z + echo 'Warning: cuda_cub_test does not exist.' 2024-12-18T01:55:34.3516176Z Warning: cuda_cub_test does not exist. 2024-12-18T01:55:34.3516512Z + run_if_exists cuda_atomic_ops_test 2024-12-18T01:55:34.3516842Z + local test_name=cuda_atomic_ops_test 2024-12-18T01:55:34.3517183Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2024-12-18T01:55:34.3517558Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2024-12-18T01:55:34.3517963Z Warning: cuda_atomic_ops_test does not exist. 2024-12-18T01:55:34.3518305Z + '[' ON == ON ']' 2024-12-18T01:55:34.3519114Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-12-18T01:55:34.3825103Z ==95852== Memcheck, a memory error detector 2024-12-18T01:55:34.3825608Z ==95852== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-12-18T01:55:34.3826182Z ==95852== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-12-18T01:55:34.3826730Z ==95852== Command: build/bin/basic --gtest_filter=-*CUDA 2024-12-18T01:55:34.3827102Z ==95852== 2024-12-18T01:55:34.8789156Z ==95852== Warning: set address range perms: large range [0x4a08000, 0x1548c000) (defined) 2024-12-18T01:56:02.3852890Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-12-18T01:56:02.4127932Z Note: Google Test filter = -*CUDA 2024-12-18T01:56:02.4174386Z [==========] Running 4 tests from 1 test suite. 2024-12-18T01:56:02.4201250Z [----------] Global test environment set-up. 2024-12-18T01:56:02.4271673Z [----------] 4 tests from BasicTest 2024-12-18T01:56:02.4295870Z [ RUN ] BasicTest.BasicTestCPU 2024-12-18T01:56:04.0502727Z 583 ms 2024-12-18T01:56:04.1334724Z 52 ms 2024-12-18T01:56:04.2062041Z 64 ms 2024-12-18T01:56:04.8054824Z [ OK ] BasicTest.BasicTestCPU (2369 ms) 2024-12-18T01:56:04.8102802Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-12-18T01:56:04.9481175Z 92 ms 2024-12-18T01:56:04.9977305Z 44 ms 2024-12-18T01:56:05.0633198Z 63 ms 2024-12-18T01:56:05.1166242Z [ OK ] BasicTest.BasicTestHalfCPU (304 ms) 2024-12-18T01:56:05.1166929Z [ RUN ] BasicTest.FactoryMethodsTest 2024-12-18T01:56:05.1555655Z [ OK ] BasicTest.FactoryMethodsTest (38 ms) 2024-12-18T01:56:05.1556056Z [ RUN ] BasicTest.BasicStdTestCPU 2024-12-18T01:56:05.2392971Z Simple example: called once 2024-12-18T01:56:05.3306199Z throw: call_once will retry 2024-12-18T01:56:05.3727121Z throw: call_once will retry 2024-12-18T01:56:05.3730737Z throw: call_once will retry 2024-12-18T01:56:05.3735583Z Didn't throw, call_once will not attempt again 2024-12-18T01:56:05.3755336Z [ OK ] BasicTest.BasicStdTestCPU (219 ms) 2024-12-18T01:56:05.3778739Z [----------] 4 tests from BasicTest (2946 ms total) 2024-12-18T01:56:05.3779013Z 2024-12-18T01:56:05.3792401Z [----------] Global test environment tear-down 2024-12-18T01:56:05.3823711Z [==========] 4 tests from 1 test suite ran. (2972 ms total) 2024-12-18T01:56:05.3834273Z [ PASSED ] 4 tests. 2024-12-18T01:56:07.2555081Z ==95852== 2024-12-18T01:56:07.2558166Z ==95852== HEAP SUMMARY: 2024-12-18T01:56:07.2558576Z ==95852== in use at exit: 241,544 bytes in 3,998 blocks 2024-12-18T01:56:07.2559107Z ==95852== total heap usage: 740,949 allocs, 736,951 frees, 214,858,760 bytes allocated 2024-12-18T01:56:07.2559591Z ==95852== 2024-12-18T01:56:07.2929972Z ==95852== LEAK SUMMARY: 2024-12-18T01:56:07.2930359Z ==95852== definitely lost: 0 bytes in 0 blocks 2024-12-18T01:56:07.2930766Z ==95852== indirectly lost: 0 bytes in 0 blocks 2024-12-18T01:56:07.2931140Z ==95852== possibly lost: 0 bytes in 0 blocks 2024-12-18T01:56:07.2931560Z ==95852== still reachable: 241,544 bytes in 3,998 blocks 2024-12-18T01:56:07.2931977Z ==95852== suppressed: 0 bytes in 0 blocks 2024-12-18T01:56:07.2932447Z ==95852== Rerun with --leak-check=full to see details of leaked memory 2024-12-18T01:56:07.2932873Z ==95852== 2024-12-18T01:56:07.2933234Z ==95852== For lists of detected and suppressed errors, rerun with: -s 2024-12-18T01:56:07.2933767Z ==95852== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-12-18T01:56:07.3424592Z + [[ -x build/bin/tensor_interop_test ]] 2024-12-18T01:56:07.3426410Z + [[ -n '' ]] 2024-12-18T01:56:07.3426664Z + assert_git_not_dirty 2024-12-18T01:56:07.3426995Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-18T01:56:07.3427364Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2024-12-18T01:56:07.3440275Z ++ git status --porcelain 2024-12-18T01:56:07.3440717Z ++ grep -v '?? third_party' 2024-12-18T01:56:07.5382108Z ++ true 2024-12-18T01:56:07.5382510Z + git_status= 2024-12-18T01:56:07.5382774Z + [[ -n '' ]] 2024-12-18T01:56:07.5383025Z + test_libtorch 1 2024-12-18T01:56:07.5383265Z + local SHARD=1 2024-12-18T01:56:07.5383530Z + [[ default != \s\l\o\w ]] 2024-12-18T01:56:07.5383838Z + echo 'Testing libtorch' 2024-12-18T01:56:07.5384124Z Testing libtorch 2024-12-18T01:56:07.5384962Z + 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 2024-12-18T01:56:07.5394916Z + 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 2024-12-18T01:56:07.5404755Z + 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 2024-12-18T01:56:07.5414590Z + 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 2024-12-18T01:56:07.5423990Z + 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 2024-12-18T01:56:07.5436144Z + 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 2024-12-18T01:56:07.5444373Z + 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 2024-12-18T01:56:07.5453299Z + export CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2024-12-18T01:56:07.5453967Z + CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2024-12-18T01:56:07.5454448Z + [[ -z 1 ]] 2024-12-18T01:56:07.5454688Z + [[ 1 == \1 ]] 2024-12-18T01:56:07.5454940Z + test_libtorch_api 2024-12-18T01:56:07.5455296Z + MNIST_DIR=/var/lib/jenkins/workspace/test/cpp/api/mnist 2024-12-18T01:56:07.5455888Z + python tools/download_mnist.py --quiet -d /var/lib/jenkins/workspace/test/cpp/api/mnist 2024-12-18T01:56:07.5936983Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ... 2024-12-18T01:56:07.6708086Z Failed to download (trying next): 2024-12-18T01:56:07.6708441Z HTTP Error 403: Forbidden 2024-12-18T01:56:07.6708999Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz ... 2024-12-18T01:56:08.0095994Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ... 2024-12-18T01:56:08.0792096Z Failed to download (trying next): 2024-12-18T01:56:08.0792467Z HTTP Error 403: Forbidden 2024-12-18T01:56:08.0792992Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz ... 2024-12-18T01:56:08.1089896Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ... 2024-12-18T01:56:08.1762589Z Failed to download (trying next): 2024-12-18T01:56:08.1762995Z HTTP Error 403: Forbidden 2024-12-18T01:56:08.1763540Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz ... 2024-12-18T01:56:08.3042875Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ... 2024-12-18T01:56:08.3726442Z Failed to download (trying next): 2024-12-18T01:56:08.3726855Z HTTP Error 403: Forbidden 2024-12-18T01:56:08.3727389Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz ... 2024-12-18T01:56:08.4078275Z + [[ linux-focal-py3.13-clang10 == *asan* ]] 2024-12-18T01:56:08.4078811Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]] 2024-12-18T01:56:08.4079194Z + OMP_NUM_THREADS=2 2024-12-18T01:56:08.4079598Z + TORCH_CPP_TEST_MNIST_PATH=/var/lib/jenkins/workspace/test/cpp/api/mnist 2024-12-18T01:56:08.4080229Z + python test/run_test.py --cpp --verbose -i cpp/test_api -k 'not IMethodTest' 2024-12-18T01:56:08.5109671Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-18T01:56:08.5110559Z import pkg_resources 2024-12-18T01:56:11.6596131Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-18T01:56:11.6746195Z Found test times from artifacts 2024-12-18T01:56:11.7358190Z Found test times from artifacts 2024-12-18T01:56:11.7380765Z Running all tests 2024-12-18T01:56:11.7383525Z Running parallel tests on 3 processes 2024-12-18T01:56:11.7384144Z Name: tests to run (est. time: 0.0min) 2024-12-18T01:56:11.7384626Z Serial tests (0): 2024-12-18T01:56:11.7384875Z Parallel tests (1): 2024-12-18T01:56:11.7385155Z cpp/test_api 1/1 2024-12-18T01:56:11.7385437Z Name: excluded (est. time: 0.0min) 2024-12-18T01:56:11.7385748Z Serial tests (0): 2024-12-18T01:56:11.7386008Z Parallel tests (0): 2024-12-18T01:56:11.7438423Z Running cpp/test_api 1/1 ... [2024-12-18 01:56:11.743561] 2024-12-18T01:56:11.7439041Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:56:11.7443117Z 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-711fca86c3c8ca3e.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2024-12-18 01:56:11.743996] 2024-12-18T01:56:14.0616045Z 2024-12-18T01:56:14.0617480Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_3cbdbc4e27f3a8d8_.log 2024-12-18T01:56:14.0618631Z 2024-12-18T01:56:14.3684291Z Running cpp/test_api 1/1 ... [2024-12-18 01:56:14.368036] 2024-12-18T01:56:14.3684970Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:56:14.3690782Z 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-a574fde111c144f7.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2024-12-18 01:56:14.368471] 2024-12-18T01:58:39.7015207Z 2024-12-18T01:58:39.7016250Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_fd56ce35abf19d0b_.log 2024-12-18T01:58:39.7027825Z 2024-12-18T01:58:40.4576093Z Running test batch 'tests to run' cost 148.72 seconds 2024-12-18T01:58:41.0792327Z + python test/run_test.py --cpp --verbose -i cpp/test_tensorexpr 2024-12-18T01:58:41.1822294Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-18T01:58:41.1823197Z import pkg_resources 2024-12-18T01:58:44.3526731Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-18T01:58:44.3681235Z Found test times from artifacts 2024-12-18T01:58:44.4291093Z Found test times from artifacts 2024-12-18T01:58:44.4313057Z Running all tests 2024-12-18T01:58:44.4316255Z Running parallel tests on 3 processes 2024-12-18T01:58:44.4316710Z Name: tests to run (est. time: 0.0min) 2024-12-18T01:58:44.4317038Z Serial tests (0): 2024-12-18T01:58:44.4317302Z Parallel tests (1): 2024-12-18T01:58:44.4317587Z cpp/test_tensorexpr 1/1 2024-12-18T01:58:44.4317895Z Name: excluded (est. time: 0.0min) 2024-12-18T01:58:44.4318217Z Serial tests (0): 2024-12-18T01:58:44.4318527Z Parallel tests (0): 2024-12-18T01:58:44.4369134Z Running cpp/test_tensorexpr 1/1 ... [2024-12-18 01:58:44.436670] 2024-12-18T01:58:44.4369586Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:58:44.4373278Z 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-513b6228c00f49ff.xml', '-x', '--reruns=2'] ... [2024-12-18 01:58:44.437071] 2024-12-18T01:58:46.2544500Z 2024-12-18T01:58:46.2545577Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_b86f5f7c3dae9f24_.log 2024-12-18T01:58:46.2546265Z 2024-12-18T01:58:47.1387666Z Running cpp/test_tensorexpr 1/1 ... [2024-12-18 01:58:47.138392] 2024-12-18T01:58:47.1388389Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:58:47.1390901Z 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-abae47b9c125ead7.xml', '-x', '--reruns=2'] ... [2024-12-18 01:58:47.138810] 2024-12-18T02:00:41.3283878Z 2024-12-18T02:00:41.3286442Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_92296654617c5627_.log 2024-12-18T02:00:41.3293413Z 2024-12-18T02:00:42.0703173Z Running test batch 'tests to run' cost 117.64 seconds 2024-12-18T02:00:42.6815696Z + [[ linux-focal-py3.13-clang10 != *android* ]] 2024-12-18T02:00:42.6816151Z + [[ linux-focal-py3.13-clang10 != *cuda* ]] 2024-12-18T02:00:42.6816504Z + [[ linux-focal-py3.13-clang10 != *asan* ]] 2024-12-18T02:00:42.6816861Z + export CPP_TESTS_DIR=build/bin 2024-12-18T02:00:42.6817179Z + CPP_TESTS_DIR=build/bin 2024-12-18T02:00:42.6817609Z + python test/run_test.py --cpp --verbose -i cpp/static_runtime_test 2024-12-18T02:00:42.7843186Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-18T02:00:42.7844106Z import pkg_resources 2024-12-18T02:00:45.9745150Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-18T02:00:45.9898560Z Found test times from artifacts 2024-12-18T02:00:46.0513809Z Found test times from artifacts 2024-12-18T02:00:46.0537431Z Running all tests 2024-12-18T02:00:46.0540782Z Running parallel tests on 3 processes 2024-12-18T02:00:46.0541218Z Name: tests to run (est. time: 0.0min) 2024-12-18T02:00:46.0541558Z Serial tests (0): 2024-12-18T02:00:46.0541836Z Parallel tests (1): 2024-12-18T02:00:46.0542142Z cpp/static_runtime_test 1/1 2024-12-18T02:00:46.0542465Z Name: excluded (est. time: 0.0min) 2024-12-18T02:00:46.0542781Z Serial tests (0): 2024-12-18T02:00:46.0543043Z Parallel tests (0): 2024-12-18T02:00:46.0592244Z Running cpp/static_runtime_test 1/1 ... [2024-12-18 02:00:46.059009] 2024-12-18T02:00:46.0592723Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T02:00:46.0596612Z 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-6a5b9ac7a297dbbf.xml', '-x', '--reruns=2'] ... [2024-12-18 02:00:46.059417] 2024-12-18T02:00:47.8771058Z 2024-12-18T02:00:47.8772112Z 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_e1ae7b5e350c608f_.log 2024-12-18T02:00:47.8772823Z 2024-12-18T02:00:48.7864484Z Running cpp/static_runtime_test 1/1 ... [2024-12-18 02:00:48.786112] 2024-12-18T02:00:48.7865013Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T02:00:48.7867532Z 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-7570ae0970997e1e.xml', '-x', '--reruns=2'] ... [2024-12-18 02:00:48.786514] 2024-12-18T02:13:15.9042432Z 2024-12-18T02:13:15.9043428Z 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_f06c97c638ad3a61_.log 2024-12-18T02:13:15.9047362Z 2024-12-18T02:13:16.6427090Z Running test batch 'tests to run' cost 750.59 seconds 2024-12-18T02:13:17.2559490Z + [[ -z 1 ]] 2024-12-18T02:13:17.2559854Z + [[ 1 == \2 ]] 2024-12-18T02:13:17.2560111Z + assert_git_not_dirty 2024-12-18T02:13:17.2560434Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-18T02:13:17.2560810Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2024-12-18T02:13:17.2564806Z ++ git status --porcelain 2024-12-18T02:13:17.2566851Z ++ grep -v '?? third_party' 2024-12-18T02:13:17.4471880Z ++ true 2024-12-18T02:13:17.4472349Z + git_status= 2024-12-18T02:13:17.4472804Z + [[ -n '' ]] 2024-12-18T02:13:17.4473301Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-18T02:13:17.4474714Z + cleanup_workspace 2024-12-18T02:13:17.4475581Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-12-18T02:13:17.4476649Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-12-18T02:13:17.4477305Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-12-18T02:13:17.4477822Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-12-18T02:13:17.4478443Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-12-18T02:13:17.4479049Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-12-18T02:13:17.4479558Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-12-18T02:13:17.9217341Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@release/2.6 2024-12-18T02:13:17.9217894Z with: 2024-12-18T02:13:17.9218169Z benchmark-results-dir: test/test-reports 2024-12-18T02:13:17.9218514Z dry-run: false 2024-12-18T02:13:17.9218774Z schema-version: v3 2024-12-18T02:13:17.9219255Z github-token: *** 2024-12-18T02:13:17.9219494Z env: 2024-12-18T02:13:17.9219731Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:17.9220198Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:17.9220705Z ##[endgroup] 2024-12-18T02:13:17.9247986Z ##[group]Run set -eux 2024-12-18T02:13:17.9248277Z set -eux 2024-12-18T02:13:17.9248555Z python3 -mpip install boto3==1.35.33 2024-12-18T02:13:17.9257604Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:17.9258016Z env: 2024-12-18T02:13:17.9258254Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:17.9258781Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:17.9259300Z ##[endgroup] 2024-12-18T02:13:17.9290006Z + python3 -mpip install boto3==1.35.33 2024-12-18T02:13:18.2296796Z Defaulting to user installation because normal site-packages is not writeable 2024-12-18T02:13:19.4617030Z Collecting boto3==1.35.33 2024-12-18T02:13:19.4957481Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2024-12-18T02:13:19.5628456Z Collecting s3transfer<0.11.0,>=0.10.0 2024-12-18T02:13:19.5667763Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2024-12-18T02:13:20.6832204Z Collecting botocore<1.36.0,>=1.35.33 2024-12-18T02:13:20.6878632Z Downloading botocore-1.35.83-py3-none-any.whl (13.3 MB) 2024-12-18T02:13:20.8906238Z 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) 2024-12-18T02:13:20.8967877Z 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) 2024-12-18T02:13:20.8973186Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2024-12-18T02:13:21.0370742Z 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) 2024-12-18T02:13:21.1196909Z Installing collected packages: botocore, s3transfer, boto3 2024-12-18T02:13:21.6744132Z Successfully installed boto3-1.35.33 botocore-1.35.83 s3transfer-0.10.4 2024-12-18T02:13:21.7807169Z ##[group]Run set -eux 2024-12-18T02:13:21.7807474Z set -eux 2024-12-18T02:13:21.7807728Z  2024-12-18T02:13:21.7807996Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2024-12-18T02:13:21.7808375Z  echo "Missing github-token input" 2024-12-18T02:13:21.7808705Z  exit 1 2024-12-18T02:13:21.7808948Z fi 2024-12-18T02:13:21.7815485Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:21.7816053Z env: 2024-12-18T02:13:21.7816295Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:21.7816764Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:21.7817627Z GITHUB_TOKEN: *** 2024-12-18T02:13:21.7817886Z ##[endgroup] 2024-12-18T02:13:21.7841595Z + [[ -z *** ]] 2024-12-18T02:13:21.7900787Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2024-12-18T02:13:21.7901253Z with: 2024-12-18T02:13:21.7901621Z github-token: *** 2024-12-18T02:13:21.7901858Z env: 2024-12-18T02:13:21.7902090Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:21.7902563Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:21.7903070Z ##[endgroup] 2024-12-18T02:13:21.7921478Z ##[group]Run set -eux 2024-12-18T02:13:21.7921773Z set -eux 2024-12-18T02:13:21.7922031Z  2024-12-18T02:13:21.7922683Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-12-18T02:13:21.7928350Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:21.7928760Z env: 2024-12-18T02:13:21.7928997Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:21.7929468Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:21.7930225Z GITHUB_TOKEN: *** 2024-12-18T02:13:21.7930471Z ##[endgroup] 2024-12-18T02:13:21.7953861Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 12383255652 i-00cc61a0ee6903cab 2024-12-18T02:13:24.9789378Z setting job-id=34566065955 2024-12-18T02:13:24.9789925Z setting job-name=linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-18T02:13:24.9900927Z ##[group]Run set -eux 2024-12-18T02:13:24.9901259Z set -eux 2024-12-18T02:13:24.9901511Z  2024-12-18T02:13:24.9901944Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2024-12-18T02:13:24.9902491Z  --schema-version "${SCHEMA_VERSION}" \ 2024-12-18T02:13:24.9902860Z  --repo "${REPO}" \ 2024-12-18T02:13:24.9903182Z  --head-branch "${HEAD_BRANCH}" \ 2024-12-18T02:13:24.9903537Z  --head-sha "${HEAD_SHA}" \ 2024-12-18T02:13:24.9903893Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2024-12-18T02:13:24.9904252Z  --run-attempt "${RUN_ATTEMPT}" \ 2024-12-18T02:13:24.9904597Z  --job-id "${JOB_ID}" \ 2024-12-18T02:13:24.9904919Z  --job-name "${JOB_NAME}" 2024-12-18T02:13:24.9910738Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:24.9911145Z env: 2024-12-18T02:13:24.9911380Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:24.9911848Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:24.9912360Z SCHEMA_VERSION: v3 2024-12-18T02:13:24.9912642Z REPO: pytorch/pytorch 2024-12-18T02:13:24.9912927Z HEAD_BRANCH: refs/heads/release/2.6 2024-12-18T02:13:24.9913293Z HEAD_SHA: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-18T02:13:24.9913659Z WORKFLOW_RUN_ID: 12383255652 2024-12-18T02:13:24.9913949Z RUN_ATTEMPT: 1 2024-12-18T02:13:24.9914195Z JOB_ID: 34566065955 2024-12-18T02:13:24.9914580Z JOB_NAME: linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge) 2024-12-18T02:13:24.9915045Z ##[endgroup] 2024-12-18T02:13:24.9940400Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/release/2.6/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/release/2.6 --head-sha 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 --workflow-id 12383255652 --run-attempt 1 --job-id 34566065955 --job-name 'linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge)' 2024-12-18T02:13:25.0255953Z ##[group]Run set -eux 2024-12-18T02:13:25.0256258Z set -eux 2024-12-18T02:13:25.0256624Z  2024-12-18T02:13:25.0256895Z # TODO (huydhn): Implement this part 2024-12-18T02:13:25.0257291Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2024-12-18T02:13:25.0262862Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.0263267Z env: 2024-12-18T02:13:25.0263507Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.0263964Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.0264479Z ##[endgroup] 2024-12-18T02:13:25.0287303Z + echo 'runners=[]' 2024-12-18T02:13:25.0311358Z ##[group]Run set -eux 2024-12-18T02:13:25.0311641Z set -eux 2024-12-18T02:13:25.0311869Z  2024-12-18T02:13:25.0312135Z # TODO (huydhn): Implement this part 2024-12-18T02:13:25.0312543Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2024-12-18T02:13:25.0317795Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.0318461Z env: 2024-12-18T02:13:25.0318724Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.0319179Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.0319686Z ##[endgroup] 2024-12-18T02:13:25.0341100Z + echo 'dependencies={}' 2024-12-18T02:13:25.0362217Z ##[group]Run set -eux 2024-12-18T02:13:25.0362514Z set -eux 2024-12-18T02:13:25.0362762Z  2024-12-18T02:13:25.0363045Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2024-12-18T02:13:25.0363540Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2024-12-18T02:13:25.0364044Z  # We don't want the job to fail if the directory doesn't exist 2024-12-18T02:13:25.0364462Z  exit 0 2024-12-18T02:13:25.0364708Z fi 2024-12-18T02:13:25.0364942Z  2024-12-18T02:13:25.0365196Z if [[ "${DRY_RUN}" == "true" ]]; then 2024-12-18T02:13:25.0365696Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2024-12-18T02:13:25.0366286Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2024-12-18T02:13:25.0366740Z  --metadata "${BENCHMARK_METADATA}" \ 2024-12-18T02:13:25.0367114Z  --runners "${RUNNER_INFO}" \ 2024-12-18T02:13:25.0367480Z  --dependencies "${DEPENDENCIES}" \ 2024-12-18T02:13:25.0367834Z  --dry-run 2024-12-18T02:13:25.0368087Z else 2024-12-18T02:13:25.0368482Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2024-12-18T02:13:25.0369054Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2024-12-18T02:13:25.0369499Z  --metadata "${BENCHMARK_METADATA}" \ 2024-12-18T02:13:25.0369868Z  --runners "${RUNNER_INFO}" \ 2024-12-18T02:13:25.0370216Z  --dependencies "${DEPENDENCIES}" 2024-12-18T02:13:25.0370554Z fi 2024-12-18T02:13:25.0375676Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.0376095Z env: 2024-12-18T02:13:25.0376325Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.0376792Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.0377315Z BENCHMARK_RESULTS_DIR: test/test-reports 2024-12-18T02:13:25.0377649Z DRY_RUN: false 2024-12-18T02:13:25.0378991Z BENCHMARK_METADATA: {"timestamp": 1734488005, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/release/2.6", "head_sha": "0cdf8b1d09254cfda66191d1bd01e3041c3c76f7", "workflow_id": 12383255652, "run_attempt": 1, "job_id": 34566065955} 2024-12-18T02:13:25.0380396Z RUNNER_INFO: [] 2024-12-18T02:13:25.0380648Z DEPENDENCIES: {} 2024-12-18T02:13:25.0380901Z ##[endgroup] 2024-12-18T02:13:25.0402131Z + [[ ! -d test/test-reports ]] 2024-12-18T02:13:25.0402452Z + [[ false == \t\r\u\e ]] 2024-12-18T02:13:25.0404863Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/release/2.6/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1734488005, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (default, 1, 5, linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/release/2.6", "head_sha": "0cdf8b1d09254cfda66191d1bd01e3041c3c76f7", "workflow_id": 12383255652, "run_attempt": 1, "job_id": 34566065955}' --runners '[]' --dependencies '{}' 2024-12-18T02:13:25.2117623Z ##[group]Run cat test/**/*_toprint.log || true 2024-12-18T02:13:25.2118042Z cat test/**/*_toprint.log || true 2024-12-18T02:13:25.2123789Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.2124195Z env: 2024-12-18T02:13:25.2124432Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.2124899Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.2125410Z ##[endgroup] 2024-12-18T02:13:25.2191253Z cat: 'test/**/*_toprint.log': No such file or directory 2024-12-18T02:13:25.2221611Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-12-18T02:13:25.2221975Z kill "$MONITOR_SCRIPT_PID" 2024-12-18T02:13:25.2227566Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.2227969Z env: 2024-12-18T02:13:25.2228202Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.2228671Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.2229195Z MONITOR_SCRIPT_PID: 40649 2024-12-18T02:13:25.2229477Z ##[endgroup] 2024-12-18T02:13:25.2379263Z Prepare all required actions 2024-12-18T02:13:25.2379694Z Getting action download info 2024-12-18T02:13:25.3889421Z Download action repository 'actions/upload-artifact@v4' (SHA:6f51ac03b9356f520e9adb1b1b7802705f340c2b) 2024-12-18T02:13:25.7480777Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-12-18T02:13:25.7481430Z with: 2024-12-18T02:13:25.7481947Z file-suffix: test-default-1-5-linux.4xlarge_34566065955 2024-12-18T02:13:25.7482623Z s3-bucket: gha-artifacts 2024-12-18T02:13:25.7483105Z env: 2024-12-18T02:13:25.7483479Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.7484308Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.7485247Z ##[endgroup] 2024-12-18T02:13:25.7539253Z ##[group]Run # Remove any previous test jsons if they exist 2024-12-18T02:13:25.7540021Z # Remove any previous test jsons if they exist 2024-12-18T02:13:25.7540645Z rm -f test-jsons-*.zip 2024-12-18T02:13:25.7541484Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2024-12-18T02:13:25.7549824Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.7550429Z env: 2024-12-18T02:13:25.7550786Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.7551553Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.7552492Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_34566065955 2024-12-18T02:13:25.7553144Z ##[endgroup] 2024-12-18T02:13:25.7707593Z adding: test/test-reports/td_exclusions-bbaa2f40b49f6415ef3c.json (deflated 82%) 2024-12-18T02:13:25.7708323Z adding: test/test-reports/td_exclusions-e14a8a0daab900ca6d53.json (deflated 73%) 2024-12-18T02:13:25.7708998Z adding: test/test-reports/td_exclusions-9258f41657395ed5e777.json (deflated 18%) 2024-12-18T02:13:25.7709668Z adding: test/test-reports/td_exclusions-a97cafe6b9c305e2e1b9.json (deflated 14%) 2024-12-18T02:13:25.7710357Z adding: test/test-reports/td_exclusions-488aca29bf989387074f.json (deflated 16%) 2024-12-18T02:13:25.7735268Z ##[group]Run # Remove any previous test reports if they exist 2024-12-18T02:13:25.7735768Z # Remove any previous test reports if they exist 2024-12-18T02:13:25.7736184Z rm -f test-reports-*.zip 2024-12-18T02:13:25.7736692Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2024-12-18T02:13:25.7742209Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.7742769Z env: 2024-12-18T02:13:25.7743008Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.7743488Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.7744074Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_34566065955 2024-12-18T02:13:25.7744465Z ##[endgroup] 2024-12-18T02:13:25.7865907Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-56a5b8a098683e3e.xml (deflated 96%) 2024-12-18T02:13:25.7867653Z adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-edbf3d2bf2e577e6.xml (deflated 89%) 2024-12-18T02:13:25.7869571Z adding: test/test-reports/python-pytest/test_cpp_extensions_open_device_registration/test_cpp_extensions_open_device_registration-638e1b44064b14ff.xml (deflated 77%) 2024-12-18T02:13:25.7932916Z adding: test/test-reports/python-pytest/test_utils/test_utils-f76602b5123ce941.xml (deflated 96%) 2024-12-18T02:13:25.7933999Z adding: test/test-reports/python-pytest/test_cpp_extensions_stream_and_event/test_cpp_extensions_stream_and_event-3570fd0f20fb00a4.xml (deflated 52%) 2024-12-18T02:13:25.7935240Z adding: test/test-reports/python-pytest/test_cpp_extensions_mtia_backend/test_cpp_extensions_mtia_backend-04cbc08e3fc18a3c.xml (deflated 69%) 2024-12-18T02:13:25.7942107Z adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-039976fa5df6fcd8.xml (deflated 96%) 2024-12-18T02:13:25.7943260Z adding: test/test-reports/python-pytest/test_dispatch/test_dispatch-bdd635ed13a717d7.xml (deflated 82%) 2024-12-18T02:13:25.7944320Z adding: 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test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241218001633.xml (deflated 43%) 2024-12-18T02:13:25.8923744Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241218001654.xml (deflated 43%) 2024-12-18T02:13:25.8953182Z ##[group]Run # Remove any previous usage logs if they exist 2024-12-18T02:13:25.8953663Z # Remove any previous usage logs if they exist 2024-12-18T02:13:25.8954050Z rm -f logs-*.zip 2024-12-18T02:13:25.8954552Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-12-18T02:13:25.8955106Z # so check to see if the file exists first 2024-12-18T02:13:25.8955478Z if [ -f 'usage_log.txt' ]; then 2024-12-18T02:13:25.8955861Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-12-18T02:13:25.8956230Z fi 2024-12-18T02:13:25.8956614Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2024-12-18T02:13:25.8957197Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2024-12-18T02:13:25.8957600Z fi 2024-12-18T02:13:25.8963150Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.8963552Z env: 2024-12-18T02:13:25.8963784Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.8964258Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.8964846Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_34566065955 2024-12-18T02:13:25.8965310Z ##[endgroup] 2024-12-18T02:13:25.9211034Z adding: usage_log.txt (deflated 97%) 2024-12-18T02:13:25.9315697Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-12-18T02:13:25.9316243Z # Remove any previous debugging artifacts if they exist 2024-12-18T02:13:25.9316668Z rm -f debug-*.zip 2024-12-18T02:13:25.9316957Z if [ -d 'test/debug' ]; then 2024-12-18T02:13:25.9317331Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-12-18T02:13:25.9317707Z fi 2024-12-18T02:13:25.9323081Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:25.9323484Z env: 2024-12-18T02:13:25.9323721Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.9324178Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.9324760Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_34566065955 2024-12-18T02:13:25.9325143Z ##[endgroup] 2024-12-18T02:13:25.9429359Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T02:13:25.9429730Z with: 2024-12-18T02:13:25.9429951Z s3-bucket: gha-artifacts 2024-12-18T02:13:25.9430289Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:25.9430659Z retention-days: 14 2024-12-18T02:13:25.9430933Z if-no-files-found: warn 2024-12-18T02:13:25.9431222Z path: test-jsons-*.zip 2024-12-18T02:13:25.9431486Z name: artifact 2024-12-18T02:13:25.9431732Z region: us-east-1 2024-12-18T02:13:25.9431989Z env: 2024-12-18T02:13:25.9432216Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:25.9432668Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:25.9433175Z ##[endgroup] 2024-12-18T02:13:26.3122960Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T02:13:26.3123445Z With the provided path, there will be 1 file uploaded 2024-12-18T02:13:26.3123922Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:26.3163480Z Starting upload of test-jsons-test-default-1-5-linux.4xlarge_34566065955.zip 2024-12-18T02:13:26.4805053Z Finished upload of test-jsons-test-default-1-5-linux.4xlarge_34566065955.zip 2024-12-18T02:13:26.4992689Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T02:13:26.4993053Z with: 2024-12-18T02:13:26.4993290Z s3-bucket: gha-artifacts 2024-12-18T02:13:26.4993636Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:26.4994014Z retention-days: 14 2024-12-18T02:13:26.4994433Z if-no-files-found: error 2024-12-18T02:13:26.4994735Z path: test-reports-*.zip 2024-12-18T02:13:26.4995003Z name: artifact 2024-12-18T02:13:26.4995254Z region: us-east-1 2024-12-18T02:13:26.4995507Z env: 2024-12-18T02:13:26.4995735Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:26.4996202Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:26.4996693Z ##[endgroup] 2024-12-18T02:13:26.8292223Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T02:13:26.8292745Z With the provided path, there will be 1 file uploaded 2024-12-18T02:13:26.8293229Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:26.8332202Z Starting upload of test-reports-test-default-1-5-linux.4xlarge_34566065955.zip 2024-12-18T02:13:27.0661073Z Finished upload of test-reports-test-default-1-5-linux.4xlarge_34566065955.zip 2024-12-18T02:13:27.0846141Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T02:13:27.0846501Z with: 2024-12-18T02:13:27.0846766Z s3-bucket: gha-artifacts 2024-12-18T02:13:27.0847097Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:27.0847470Z retention-days: 14 2024-12-18T02:13:27.0847750Z if-no-files-found: ignore 2024-12-18T02:13:27.0848041Z path: logs-*.zip 2024-12-18T02:13:27.0848295Z name: artifact 2024-12-18T02:13:27.0848531Z region: us-east-1 2024-12-18T02:13:27.0848780Z env: 2024-12-18T02:13:27.0849011Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:27.0849620Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:27.0850134Z ##[endgroup] 2024-12-18T02:13:27.4143760Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T02:13:27.4144266Z With the provided path, there will be 1 file uploaded 2024-12-18T02:13:27.4144739Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:27.4183488Z Starting upload of logs-test-default-1-5-linux.4xlarge_34566065955.zip 2024-12-18T02:13:27.6279390Z Finished upload of logs-test-default-1-5-linux.4xlarge_34566065955.zip 2024-12-18T02:13:27.6482619Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T02:13:27.6483235Z with: 2024-12-18T02:13:27.6483607Z s3-bucket: gha-artifacts 2024-12-18T02:13:27.6484199Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T02:13:27.6484848Z retention-days: 14 2024-12-18T02:13:27.6485300Z if-no-files-found: ignore 2024-12-18T02:13:27.6485785Z path: debug-*.zip 2024-12-18T02:13:27.6486183Z name: artifact 2024-12-18T02:13:27.6486577Z region: us-east-1 2024-12-18T02:13:27.6487000Z env: 2024-12-18T02:13:27.6487381Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:27.6488200Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:27.6489090Z ##[endgroup] 2024-12-18T02:13:27.9735759Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-12-18T02:13:27.9930980Z ##[group]Run # shellcheck disable=SC2156 2024-12-18T02:13:27.9931400Z # shellcheck disable=SC2156 2024-12-18T02:13:27.9932022Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-12-18T02:13:27.9937968Z shell: /usr/bin/bash -e {0} 2024-12-18T02:13:27.9938266Z env: 2024-12-18T02:13:27.9938508Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:27.9938970Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:27.9939489Z ##[endgroup] 2024-12-18T02:13:28.2172184Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@release/2.6 2024-12-18T02:13:28.2172684Z with: 2024-12-18T02:13:28.2172912Z env: 2024-12-18T02:13:28.2173136Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:28.2173794Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:28.2174312Z ##[endgroup] 2024-12-18T02:13:28.2195142Z ##[group]Run set -eou pipefail 2024-12-18T02:13:28.2195485Z set -eou pipefail 2024-12-18T02:13:28.2195903Z  2024-12-18T02:13:28.2196283Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-12-18T02:13:28.2196776Z for _ in $(seq 1440); do 2024-12-18T02:13:28.2197133Z  # Break if no ssh session exists anymore 2024-12-18T02:13:28.2197511Z  if [ "$(who)" = "" ]; then 2024-12-18T02:13:28.2197830Z  break 2024-12-18T02:13:28.2198072Z  fi 2024-12-18T02:13:28.2198456Z  echo "." 2024-12-18T02:13:28.2198725Z  sleep 5 2024-12-18T02:13:28.2198967Z done 2024-12-18T02:13:28.2204786Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:28.2205223Z env: 2024-12-18T02:13:28.2205466Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:28.2205932Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:28.2206452Z ##[endgroup] 2024-12-18T02:13:28.2229198Z Holding runner for 2 hours until all ssh sessions have logged out 2024-12-18T02:13:28.2308397Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-12-18T02:13:28.2308995Z # ignore expansion of "docker ps -q" since it could be empty 2024-12-18T02:13:28.2309457Z # shellcheck disable=SC2046 2024-12-18T02:13:28.2309808Z docker stop $(docker ps -q) || true 2024-12-18T02:13:28.2310185Z # Prune all of the docker images 2024-12-18T02:13:28.2310541Z docker system prune -af 2024-12-18T02:13:28.2315900Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:28.2316310Z env: 2024-12-18T02:13:28.2316554Z GIT_DEFAULT_BRANCH: main 2024-12-18T02:13:28.2317019Z DOCKER_CONTAINER_ID: 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:28.2317533Z ##[endgroup] 2024-12-18T02:13:29.1704892Z 009cb12578f8 2024-12-18T02:13:30.4163323Z Deleted Containers: 2024-12-18T02:13:30.4163783Z 009cb12578f86505ffe69142034f2032e7650a104ded79657577304810991369 2024-12-18T02:13:30.4164167Z 2024-12-18T02:13:33.1891608Z Deleted Images: 2024-12-18T02:13:33.1892416Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-18T02:13:33.1893744Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10@sha256:061c030ced34ec2531cb2d0841ec9a10875e6b82f7a9a932b0a62f77f1c9910a 2024-12-18T02:13:33.1894842Z deleted: sha256:bca7896c0150bd62bb439228906ce331a02b4d3cd9ef140e4946794ede2ac12d 2024-12-18T02:13:33.1895519Z deleted: sha256:b37cb25b49b213a74963f9e9acec4cb57db2d0a6b6bd4fe46f1fb7448c2f485b 2024-12-18T02:13:33.1896192Z deleted: sha256:0684fc485e9f2dba4d5119f3e50d4f5f4d614268138293188d9de1162d0f862f 2024-12-18T02:13:33.1896843Z deleted: sha256:e2fa7e8a0f5c853bc371c599dfdf7c3c4eb45d56465095c920bf0bd6764ce98b 2024-12-18T02:13:33.1897515Z deleted: sha256:5f66e21b786f4832bc27c816f2908df50c288ea31161474c330f3383e1dc876a 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overriding HOME='/home/ec2-user/actions-runner/_work/_temp/71df7778-5166-4257-a4e0-2d22550e0b37' before making global git config changes 2024-12-18T02:13:33.2940839Z Adding repository directory to the temporary git global config as a safe directory 2024-12-18T02:13:33.2943863Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-18T02:13:33.2971128Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-12-18T02:13:33.2999442Z [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' || :" 2024-12-18T02:13:33.3290629Z Entering 'android/libs/fbjni' 2024-12-18T02:13:33.3341628Z Entering 'third_party/FP16' 2024-12-18T02:13:33.3391995Z Entering 'third_party/FXdiv' 2024-12-18T02:13:33.3442393Z Entering 'third_party/NNPACK' 2024-12-18T02:13:33.3492527Z Entering 'third_party/NVTX' 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2024-12-18T02:13:33.7978510Z Entering 'third_party/composable_kernel' 2024-12-18T02:13:33.8014728Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8053997Z Entering 'third_party/cpp-httplib' 2024-12-18T02:13:33.8091127Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8123402Z Entering 'third_party/cpuinfo' 2024-12-18T02:13:33.8160777Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8193936Z Entering 'third_party/cudnn_frontend' 2024-12-18T02:13:33.8229199Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8261896Z Entering 'third_party/cutlass' 2024-12-18T02:13:33.8297559Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8337886Z Entering 'third_party/eigen' 2024-12-18T02:13:33.8373246Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8407905Z Entering 'third_party/fbgemm' 2024-12-18T02:13:33.8444100Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8477745Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-18T02:13:33.8511621Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8543876Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-18T02:13:33.8578231Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8609642Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-18T02:13:33.8644364Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8684078Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-18T02:13:33.8716887Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8747782Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-18T02:13:33.8781895Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8813273Z Entering 'third_party/flatbuffers' 2024-12-18T02:13:33.8848284Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8883536Z Entering 'third_party/fmt' 2024-12-18T02:13:33.8917462Z http.https://github.com/.extraheader 2024-12-18T02:13:33.8947819Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-18T02:13:33.8983305Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9013466Z Entering 'third_party/gloo' 2024-12-18T02:13:33.9048068Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9078720Z Entering 'third_party/googletest' 2024-12-18T02:13:33.9112836Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9143380Z Entering 'third_party/ideep' 2024-12-18T02:13:33.9178154Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9208701Z Entering 'third_party/ideep/mkl-dnn' 2024-12-18T02:13:33.9241988Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9280392Z Entering 'third_party/ittapi' 2024-12-18T02:13:33.9314450Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9345160Z Entering 'third_party/kineto' 2024-12-18T02:13:33.9379745Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9410685Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-18T02:13:33.9443974Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9475317Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-18T02:13:33.9508311Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9540862Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-18T02:13:33.9574476Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9605065Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-18T02:13:33.9638637Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9671291Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-18T02:13:33.9704337Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9734230Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-18T02:13:33.9767968Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9800965Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-18T02:13:33.9834362Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9865962Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-18T02:13:33.9898935Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9929854Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-18T02:13:33.9964877Z http.https://github.com/.extraheader 2024-12-18T02:13:33.9997271Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-18T02:13:34.0030937Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0064664Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-18T02:13:34.0098291Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0128526Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-18T02:13:34.0162536Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0196781Z Entering 'third_party/mimalloc' 2024-12-18T02:13:34.0230917Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0262220Z Entering 'third_party/nccl/nccl' 2024-12-18T02:13:34.0297572Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0328899Z Entering 'third_party/nlohmann' 2024-12-18T02:13:34.0364262Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0397107Z Entering 'third_party/onnx' 2024-12-18T02:13:34.0430803Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0477942Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-18T02:13:34.0511566Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0543733Z Entering 'third_party/opentelemetry-cpp' 2024-12-18T02:13:34.0578873Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0611711Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-18T02:13:34.0645800Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0676170Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-18T02:13:34.0709454Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0739515Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-18T02:13:34.0773478Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0803725Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-18T02:13:34.0837088Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0869680Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-18T02:13:34.0902899Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0933450Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-18T02:13:34.0967442Z http.https://github.com/.extraheader 2024-12-18T02:13:34.0997726Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-18T02:13:34.1030910Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1061565Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-18T02:13:34.1097541Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1130446Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-18T02:13:34.1164159Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1198048Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-18T02:13:34.1230837Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1283233Z Entering 'third_party/pocketfft' 2024-12-18T02:13:34.1318145Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1349510Z Entering 'third_party/protobuf' 2024-12-18T02:13:34.1384624Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1419448Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-18T02:13:34.1454673Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1485980Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-18T02:13:34.1519133Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1552111Z Entering 'third_party/psimd' 2024-12-18T02:13:34.1586858Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1617926Z Entering 'third_party/pthreadpool' 2024-12-18T02:13:34.1652792Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1684541Z Entering 'third_party/pybind11' 2024-12-18T02:13:34.1718255Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1749200Z Entering 'third_party/python-peachpy' 2024-12-18T02:13:34.1785754Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1815717Z Entering 'third_party/sleef' 2024-12-18T02:13:34.1851040Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1882750Z Entering 'third_party/tensorpipe' 2024-12-18T02:13:34.1917410Z http.https://github.com/.extraheader 2024-12-18T02:13:34.1948654Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-18T02:13:34.1983600Z http.https://github.com/.extraheader 2024-12-18T02:13:34.2014202Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-18T02:13:34.2048909Z http.https://github.com/.extraheader 2024-12-18T02:13:34.2080277Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-18T02:13:34.2113339Z http.https://github.com/.extraheader 2024-12-18T02:13:34.2144385Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-18T02:13:34.2179112Z http.https://github.com/.extraheader 2024-12-18T02:13:34.2208701Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-18T02:13:34.2243266Z http.https://github.com/.extraheader 2024-12-18T02:13:34.2354499Z A job completed hook has been configured by the self-hosted runner administrator 2024-12-18T02:13:34.2373329Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-12-18T02:13:34.2378736Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T02:13:34.2379163Z ##[endgroup] 2024-12-18T02:13:40.8730447Z Cleaning up orphan processes