2024-12-17T23:43:22.7429128Z Current runner version: '2.321.0' 2024-12-17T23:43:22.7437197Z Runner name: 'i-0c373a2e3f7bf6e7f' 2024-12-17T23:43:22.7438135Z Runner group name: 'Default' 2024-12-17T23:43:22.7439223Z Machine name: 'ip-10-0-50-59' 2024-12-17T23:43:22.7443971Z ##[group]GITHUB_TOKEN Permissions 2024-12-17T23:43:22.7446961Z Actions: read 2024-12-17T23:43:22.7447926Z Attestations: read 2024-12-17T23:43:22.7448596Z Checks: read 2024-12-17T23:43:22.7449154Z Contents: read 2024-12-17T23:43:22.7449788Z Deployments: read 2024-12-17T23:43:22.7450443Z Discussions: read 2024-12-17T23:43:22.7451212Z Issues: read 2024-12-17T23:43:22.7451882Z Metadata: read 2024-12-17T23:43:22.7452461Z Packages: read 2024-12-17T23:43:22.7453088Z Pages: read 2024-12-17T23:43:22.7453795Z PullRequests: read 2024-12-17T23:43:22.7454461Z RepositoryProjects: read 2024-12-17T23:43:22.7455138Z SecurityEvents: read 2024-12-17T23:43:22.7455793Z Statuses: read 2024-12-17T23:43:22.7456415Z ##[endgroup] 2024-12-17T23:43:22.7460238Z Secret source: Actions 2024-12-17T23:43:22.7461316Z Prepare workflow directory 2024-12-17T23:43:22.8233492Z Prepare all required actions 2024-12-17T23:43:22.8276415Z Getting action download info 2024-12-17T23:43:23.0218073Z Download action repository 'pytorch/test-infra@release/2.6' (SHA:eb0adf5a84668865394af69e26428b32c8105c1c) 2024-12-17T23:43:24.8240277Z Download action repository 'pytorch/pytorch@release/2.6' (SHA:0cdf8b1d09254cfda66191d1bd01e3041c3c76f7) 2024-12-17T23:43:37.5521548Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-12-17T23:43:37.7555325Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-12-17T23:43:38.0879848Z Getting action download info 2024-12-17T23:43:38.2218539Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-12-17T23:43:38.4408998Z Getting action download info 2024-12-17T23:43:38.5439254Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2024-12-17T23:43:38.7186932Z Getting action download info 2024-12-17T23:43:38.8276429Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-12-17T23:43:39.0844387Z Getting action download info 2024-12-17T23:43:39.2179207Z Download action repository 'pytorch/test-infra@main' (SHA:a07505a74641a4ff5123d635defac481ef28ef1e) 2024-12-17T23:43:40.6998981Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/release/2.6 (0cdf8b1d09254cfda66191d1bd01e3041c3c76f7) 2024-12-17T23:43:40.7001136Z ##[group] Inputs 2024-12-17T23:43:40.7001501Z build-environment: linux-focal-py3.12-clang10 2024-12-17T23:43:40.7003870Z 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"}]} 2024-12-17T23:43:40.7006536Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:43:40.7007281Z sync-tag: 2024-12-17T23:43:40.7008113Z timeout-minutes: 600 2024-12-17T23:43:40.7008374Z use-gha: 2024-12-17T23:43:40.7008609Z dashboard-tag: 2024-12-17T23:43:40.7008869Z s3-bucket: gha-artifacts 2024-12-17T23:43:40.7009155Z aws-role-to-assume: 2024-12-17T23:43:40.7009742Z disable-monitor: false 2024-12-17T23:43:40.7010276Z ##[endgroup] 2024-12-17T23:43:40.7010823Z Complete job name: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:43:40.7504280Z A job started hook has been configured by the self-hosted runner administrator 2024-12-17T23:43:40.7653183Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-12-17T23:43:40.7666327Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:43:40.7666975Z ##[endgroup] 2024-12-17T23:43:42.4976632Z Runner Type: linux.2xlarge 2024-12-17T23:43:42.4977170Z Instance Type: c5.2xlarge 2024-12-17T23:43:42.4977460Z AMI Name: unknown 2024-12-17T23:43:42.4999312Z AMI ID: ami-0fff1b9a61dec8a5f 2024-12-17T23:43:48.4422705Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@release/2.6 2024-12-17T23:43:48.4423205Z with: 2024-12-17T23:43:48.4423886Z github-secret: *** 2024-12-17T23:43:48.4424634Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-12-17T23:43:48.4425458Z activate-with-label: false 2024-12-17T23:43:48.4425762Z label: with-ssh 2024-12-17T23:43:48.4426036Z remove-existing-keys: true 2024-12-17T23:43:48.4426319Z fail-silently: true 2024-12-17T23:43:48.4426581Z env: 2024-12-17T23:43:48.4426813Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:43:48.4427101Z ##[endgroup] 2024-12-17T23:43:48.5589793Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-12-17T23:43:48.5591437Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2024-12-17T23:43:48.5713203Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@release/2.6 2024-12-17T23:43:48.5713811Z with: 2024-12-17T23:43:48.5714033Z no-sudo: true 2024-12-17T23:43:48.5714306Z submodules: recursive 2024-12-17T23:43:48.5714583Z fetch-depth: 0 2024-12-17T23:43:48.5714829Z env: 2024-12-17T23:43:48.5715044Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:43:48.5715319Z ##[endgroup] 2024-12-17T23:43:48.5792754Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:43:48.5793761Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:43:48.5801157Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:43:48.5801570Z env: 2024-12-17T23:43:48.5801791Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:43:48.5802069Z ##[endgroup] 2024-12-17T23:43:48.5887496Z ##[group]Run retry () { 2024-12-17T23:43:48.5887812Z retry () { 2024-12-17T23:43:48.5888174Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-12-17T23:43:48.5888622Z } 2024-12-17T23:43:48.5888873Z echo "${GITHUB_WORKSPACE}" 2024-12-17T23:43:48.5889213Z if [ -z "${NO_SUDO}" ]; then 2024-12-17T23:43:48.5889576Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-12-17T23:43:48.5889928Z else 2024-12-17T23:43:48.5890198Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-12-17T23:43:48.5890527Z fi 2024-12-17T23:43:48.5890772Z mkdir "${GITHUB_WORKSPACE}" 2024-12-17T23:43:48.5896277Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:43:48.5896675Z env: 2024-12-17T23:43:48.5896893Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:43:48.5897167Z NO_SUDO: true 2024-12-17T23:43:48.5897404Z ##[endgroup] 2024-12-17T23:43:48.5919577Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:43:51.8190795Z ##[group]Run malfet/checkout@silent-checkout 2024-12-17T23:43:51.8191153Z with: 2024-12-17T23:43:51.8191422Z ref: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:43:51.8191826Z fetch-depth: 0 2024-12-17T23:43:51.8192082Z submodules: recursive 2024-12-17T23:43:51.8192347Z quiet-checkout: true 2024-12-17T23:43:51.8192627Z repository: pytorch/pytorch 2024-12-17T23:43:51.8193049Z token: *** 2024-12-17T23:43:51.8193501Z ssh-strict: true 2024-12-17T23:43:51.8193761Z persist-credentials: true 2024-12-17T23:43:51.8194044Z clean: true 2024-12-17T23:43:51.8194282Z sparse-checkout-cone-mode: true 2024-12-17T23:43:51.8194590Z lfs: false 2024-12-17T23:43:51.8194827Z set-safe-directory: true 2024-12-17T23:43:51.8195099Z env: 2024-12-17T23:43:51.8195315Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:43:51.8195588Z ##[endgroup] 2024-12-17T23:43:51.9118937Z Syncing repository: pytorch/pytorch 2024-12-17T23:43:51.9121173Z ##[group]Getting Git version info 2024-12-17T23:43:51.9122064Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-12-17T23:43:51.9123243Z [command]/usr/bin/git version 2024-12-17T23:43:51.9123719Z git version 2.40.1 2024-12-17T23:43:51.9138502Z ##[endgroup] 2024-12-17T23:43:51.9154278Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/a1b2541c-e631-4265-b123-5807456c0d6c' before making global git config changes 2024-12-17T23:43:51.9156105Z Adding repository directory to the temporary git global config as a safe directory 2024-12-17T23:43:51.9159721Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:43:51.9187412Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-12-17T23:43:51.9191025Z ##[group]Initializing the repository 2024-12-17T23:43:51.9194210Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:43:51.9218036Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-12-17T23:43:51.9218809Z hint: is subject to change. To configure the initial branch name to use in all 2024-12-17T23:43:51.9219437Z hint: of your new repositories, which will suppress this warning, call: 2024-12-17T23:43:51.9219878Z hint: 2024-12-17T23:43:51.9220188Z hint: git config --global init.defaultBranch 2024-12-17T23:43:51.9220735Z hint: 2024-12-17T23:43:51.9221288Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-12-17T23:43:51.9222305Z hint: 'development'. The just-created branch can be renamed via this command: 2024-12-17T23:43:51.9223091Z hint: 2024-12-17T23:43:51.9223473Z hint: git branch -m 2024-12-17T23:43:51.9224383Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-12-17T23:43:51.9230495Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-12-17T23:43:51.9254674Z ##[endgroup] 2024-12-17T23:43:51.9255173Z ##[group]Disabling automatic garbage collection 2024-12-17T23:43:51.9256916Z [command]/usr/bin/git config --local gc.auto 0 2024-12-17T23:43:51.9279200Z ##[endgroup] 2024-12-17T23:43:51.9279890Z ##[group]Setting up auth 2024-12-17T23:43:51.9284935Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-12-17T23:43:51.9307897Z [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' || :" 2024-12-17T23:43:51.9562641Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-12-17T23:43:51.9586097Z [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' || :" 2024-12-17T23:43:51.9837424Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-12-17T23:43:51.9869421Z ##[endgroup] 2024-12-17T23:43:51.9869870Z ##[group]Fetching the repository 2024-12-17T23:43:51.9875543Z [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/* 2024-12-17T23:43:55.1766170Z remote: Enumerating objects: 1056579 2024-12-17T23:43:55.1766631Z remote: Enumerating objects: 1057229, done. 2024-12-17T23:43:55.1769698Z remote: 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0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:44:46.9507394Z ##[endgroup] 2024-12-17T23:44:46.9507864Z ##[group]Setting up auth for fetching submodules 2024-12-17T23:44:46.9512264Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-12-17T23:44:46.9546674Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-12-17T23:44:46.9566826Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-12-17T23:44:46.9590562Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-12-17T23:44:46.9612685Z ##[endgroup] 2024-12-17T23:44:46.9613426Z ##[group]Fetching submodules 2024-12-17T23:44:46.9614701Z [command]/usr/bin/git submodule sync --recursive 2024-12-17T23:44:46.9899677Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-12-17T23:44:47.0170996Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-12-17T23:44:47.0172377Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-12-17T23:44:47.0173713Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-12-17T23:44:47.0176060Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-12-17T23:44:47.0178390Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2024-12-17T23:44:47.0181272Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-12-17T23:44:47.0183546Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-12-17T23:44:47.0186274Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-12-17T23:44:47.0189336Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2024-12-17T23:44:47.0192176Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-12-17T23:44:47.0195103Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-12-17T23:44:47.0198356Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-12-17T23:44:47.0201419Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-12-17T23:44:47.0204961Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-12-17T23:44:47.0208786Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-12-17T23:44:47.0212425Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-12-17T23:44:47.0217369Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-12-17T23:44:47.0221290Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:44:47.0224909Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-12-17T23:44:47.0229046Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-12-17T23:44:47.0232901Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-12-17T23:44:47.0237298Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-12-17T23:44:47.0241465Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-12-17T23:44:47.0245700Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-12-17T23:44:47.0249896Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-12-17T23:44:47.0254376Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-12-17T23:44:47.0258776Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-12-17T23:44:47.0263582Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-12-17T23:44:47.0268238Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-12-17T23:44:47.0273296Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-12-17T23:44:47.0279830Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-12-17T23:44:47.0285541Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-12-17T23:44:47.0291707Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-12-17T23:44:47.0299672Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-12-17T23:44:47.0306158Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-12-17T23:44:47.0313107Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-12-17T23:44:47.0342430Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-12-17T23:44:47.3252218Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-12-17T23:44:47.5101791Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-12-17T23:44:47.6935366Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-12-17T23:44:47.9390208Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2024-12-17T23:44:48.2488151Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-12-17T23:44:50.4373973Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-12-17T23:45:04.6479495Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-12-17T23:45:05.0719735Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2024-12-17T23:45:07.2402149Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-12-17T23:45:07.8104412Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-12-17T23:45:08.4374085Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-12-17T23:45:09.7534519Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-12-17T23:45:12.0492595Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-12-17T23:45:17.3685762Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-12-17T23:45:19.3547874Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-12-17T23:45:21.1593941Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-12-17T23:45:22.5193234Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-12-17T23:45:22.9446176Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-12-17T23:45:23.2875616Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-12-17T23:45:24.4580388Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-12-17T23:45:24.8171511Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-12-17T23:45:25.0730742Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-12-17T23:45:26.6115599Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-12-17T23:45:27.5003307Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-12-17T23:45:27.8531129Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-12-17T23:45:36.7676044Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-12-17T23:45:39.1690255Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-12-17T23:45:46.7177840Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-12-17T23:45:46.9504971Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-12-17T23:45:57.1001952Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-12-17T23:45:57.3502938Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-12-17T23:45:57.5507774Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-12-17T23:45:58.6751541Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-12-17T23:45:58.9891106Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-12-17T23:45:59.6511943Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-12-17T23:46:00.0826373Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-12-17T23:46:00.0945381Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-12-17T23:46:00.1033945Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-12-17T23:46:00.1271497Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-12-17T23:46:00.1604689Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2024-12-17T23:46:00.1983270Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-12-17T23:46:00.8940309Z Submodule path 'third_party/XNNPACK': checked out '4ea82e595b36106653175dcb04b2aa532660d0d8' 2024-12-17T23:46:00.9170749Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-12-17T23:46:01.1436222Z Submodule path 'third_party/composable_kernel': checked out '50ee4267e27b875d149e642f4cebd47be1dc3b57' 2024-12-17T23:46:01.1915250Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-12-17T23:46:01.2879938Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2024-12-17T23:46:01.3217841Z Submodule path 'third_party/cudnn_frontend': checked out '936021bfed8c91dc416af1588b2c4eca631a9e45' 2024-12-17T23:46:01.8110305Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-12-17T23:46:02.0585261Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-12-17T23:46:02.1522075Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-12-17T23:46:02.1541108Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:46:02.1542828Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:46:02.1545080Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:46:02.1547565Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:46:02.1550249Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:46:02.1575625Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-12-17T23:46:03.0557531Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-12-17T23:46:03.6750941Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-12-17T23:46:05.7739682Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-12-17T23:46:06.9421069Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-12-17T23:46:07.3112907Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-12-17T23:46:07.4053789Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-12-17T23:46:07.7995933Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-12-17T23:46:07.8617659Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-12-17T23:46:07.8744157Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-12-17T23:46:08.0033959Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-12-17T23:46:08.0426353Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-12-17T23:46:08.0826623Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-12-17T23:46:08.1086459Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-12-17T23:46:08.1517730Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2024-12-17T23:46:08.1647561Z Submodule path 'third_party/ideep': checked out 'c7ccd5bdbe5434ba156f4e856dcef0601637334b' 2024-12-17T23:46:08.1661780Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-12-17T23:46:08.1686661Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-12-17T23:46:23.4870586Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2024-12-17T23:46:23.5049859Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-12-17T23:46:23.5901138Z Submodule path 'third_party/kineto': checked out '338140f58a28d599da3434ced4fd2d75dd1a213d' 2024-12-17T23:46:23.5919356Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:46:23.5920810Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:46:23.5923354Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:46:23.5952148Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-12-17T23:46:24.5148323Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-12-17T23:46:25.8706009Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-12-17T23:46:27.1253187Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-12-17T23:46:27.1269958Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:46:27.1272836Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:46:27.1274891Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:46:27.1277622Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:46:27.1280398Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:46:27.1283354Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:46:27.1286885Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:46:27.1289954Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:46:27.1317043Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-12-17T23:46:28.1588059Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-12-17T23:46:28.5338351Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-12-17T23:46:29.9213106Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-12-17T23:46:30.2156548Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-12-17T23:46:30.7449672Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-12-17T23:46:31.9458640Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-12-17T23:46:39.3983822Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-12-17T23:46:39.7858212Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-12-17T23:46:39.8038513Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-12-17T23:46:39.8397958Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-12-17T23:46:39.8532899Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-12-17T23:46:39.8547109Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:46:39.8572995Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-12-17T23:46:40.1643075Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-12-17T23:46:40.1823928Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-12-17T23:46:40.2225260Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-12-17T23:46:40.3303243Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-12-17T23:46:40.3478682Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-12-17T23:46:40.3876483Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-12-17T23:46:40.4460457Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-12-17T23:46:40.4834828Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-12-17T23:46:40.5133690Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-12-17T23:46:40.6234046Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-12-17T23:46:40.9748806Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c' 2024-12-17T23:46:40.9783460Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-12-17T23:46:40.9809817Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-12-17T23:46:42.1497227Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b' 2024-12-17T23:46:42.2171618Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-12-17T23:46:42.2190892Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:46:42.2193266Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:46:42.2195868Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:46:42.2198700Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:46:42.2201762Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:46:42.2204685Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:46:42.2207712Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for 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2024-12-17T23:47:11.9194970Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2024-12-17T23:47:11.9468843Z Entering 'android/libs/fbjni' 2024-12-17T23:47:11.9516395Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2024-12-17T23:47:11.9531182Z Entering 'third_party/FP16' 2024-12-17T23:47:11.9578324Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2024-12-17T23:47:11.9592636Z Entering 'third_party/FXdiv' 2024-12-17T23:47:11.9640183Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2024-12-17T23:47:11.9655592Z Entering 'third_party/NNPACK' 2024-12-17T23:47:11.9702476Z 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'third_party/pocketfft' 2024-12-17T23:47:13.0770012Z Entering 'third_party/protobuf' 2024-12-17T23:47:13.0811287Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:13.0849668Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:13.0889537Z Entering 'third_party/psimd' 2024-12-17T23:47:13.0928451Z Entering 'third_party/pthreadpool' 2024-12-17T23:47:13.0967822Z Entering 'third_party/pybind11' 2024-12-17T23:47:13.1006424Z Entering 'third_party/python-peachpy' 2024-12-17T23:47:13.1044935Z Entering 'third_party/sleef' 2024-12-17T23:47:13.1083752Z Entering 'third_party/tensorpipe' 2024-12-17T23:47:13.1122676Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:13.1160805Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:13.1198762Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:13.1235937Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:13.1272667Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:13.1323993Z ##[endgroup] 2024-12-17T23:47:13.1352875Z [command]/usr/bin/git log -1 --format='%H' 2024-12-17T23:47:13.1371195Z '0cdf8b1d09254cfda66191d1bd01e3041c3c76f7' 2024-12-17T23:47:13.1522055Z Prepare all required actions 2024-12-17T23:47:13.1522632Z Getting action download info 2024-12-17T23:47:13.3126141Z ##[group]Run ./.github/actions/setup-linux 2024-12-17T23:47:13.3126491Z env: 2024-12-17T23:47:13.3126707Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:13.3126985Z ##[endgroup] 2024-12-17T23:47:13.3176258Z ##[group]Run set -euo pipefail 2024-12-17T23:47:13.3176638Z set -euo pipefail 2024-12-17T23:47:13.3176938Z function get_ec2_metadata() { 2024-12-17T23:47:13.3177338Z  # Pulled from instance metadata endpoint for EC2 2024-12-17T23:47:13.3178017Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-12-17T23:47:13.3178596Z  category=$1 2024-12-17T23:47:13.3178975Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-12-17T23:47:13.3179434Z  runner_name_str=i-0c373a2e3f7bf6e7f 2024-12-17T23:47:13.3179831Z  if [[ -f /.inarc ]]; then 2024-12-17T23:47:13.3180194Z  echo "ARC Runner, no info on ec2 metadata" 2024-12-17T23:47:13.3180590Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-12-17T23:47:13.3181078Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-12-17T23:47:13.3181528Z  else 2024-12-17T23:47:13.3182410Z  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}" 2024-12-17T23:47:13.3183348Z  fi 2024-12-17T23:47:13.3183580Z } 2024-12-17T23:47:13.3183845Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-12-17T23:47:13.3184285Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-12-17T23:47:13.3184780Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-12-17T23:47:13.3185210Z echo "system info $(uname -a)" 2024-12-17T23:47:13.3193469Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:13.3193869Z env: 2024-12-17T23:47:13.3194086Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:13.3194365Z ##[endgroup] 2024-12-17T23:47:13.3329881Z ami-id: ami-0fff1b9a61dec8a5f 2024-12-17T23:47:13.3440459Z instance-id: i-0c373a2e3f7bf6e7f 2024-12-17T23:47:13.3535132Z instance-type: c5.2xlarge 2024-12-17T23:47:13.3544687Z system info Linux ip-10-0-50-59.ec2.internal 6.1.109-118.189.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Sep 10 08:59:12 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-12-17T23:47:13.3575571Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:47:13.3576553Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:47:13.3583120Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:13.3583534Z env: 2024-12-17T23:47:13.3583749Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:13.3584024Z ##[endgroup] 2024-12-17T23:47:13.3643011Z ##[group]Run if systemctl is-active --quiet docker; then 2024-12-17T23:47:13.3643482Z if systemctl is-active --quiet docker; then 2024-12-17T23:47:13.3643920Z  echo "Docker daemon is running..."; 2024-12-17T23:47:13.3644320Z else 2024-12-17T23:47:13.3644701Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-12-17T23:47:13.3645135Z fi 2024-12-17T23:47:13.3650495Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:13.3651040Z env: 2024-12-17T23:47:13.3651281Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:13.3651563Z ##[endgroup] 2024-12-17T23:47:13.3721174Z Docker daemon is running... 2024-12-17T23:47:13.3768980Z ##[group]Run nick-fields/retry@v3.0.0 2024-12-17T23:47:13.3769307Z with: 2024-12-17T23:47:13.3769525Z shell: bash 2024-12-17T23:47:13.3769903Z timeout_minutes: 5 2024-12-17T23:47:13.3770166Z max_attempts: 3 2024-12-17T23:47:13.3770420Z retry_wait_seconds: 30 2024-12-17T23:47:13.3772753Z command: AWS_ACCOUNT_ID=$(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 2024-12-17T23:47:13.3775099Z polling_interval_seconds: 1 2024-12-17T23:47:13.3775397Z warning_on_retry: true 2024-12-17T23:47:13.3775676Z continue_on_error: false 2024-12-17T23:47:13.3775934Z env: 2024-12-17T23:47:13.3776164Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:13.3776443Z AWS_RETRY_MODE: standard 2024-12-17T23:47:13.3776720Z AWS_MAX_ATTEMPTS: 5 2024-12-17T23:47:13.3776990Z AWS_DEFAULT_REGION: us-east-1 2024-12-17T23:47:13.3777265Z ##[endgroup] 2024-12-17T23:47:14.5849017Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:14.5850047Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:14.5851043Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:14.5851737Z 2024-12-17T23:47:14.5851889Z Login Succeeded 2024-12-17T23:47:15.4599511Z Command completed after 1 attempt(s). 2024-12-17T23:47:15.4662622Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:47:15.4663195Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:47:15.4663684Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:47:15.4670167Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:15.4670578Z env: 2024-12-17T23:47:15.4670816Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:15.4671086Z ##[endgroup] 2024-12-17T23:47:15.4752779Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-12-17T23:47:15.4753379Z # ignore expansion of "docker ps -q" since it could be empty 2024-12-17T23:47:15.4753828Z # shellcheck disable=SC2046 2024-12-17T23:47:15.4754172Z docker stop $(docker ps -q) || true 2024-12-17T23:47:15.4754537Z # Prune all of the docker images 2024-12-17T23:47:15.4754880Z docker system prune -af 2024-12-17T23:47:15.4760453Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:15.4760857Z env: 2024-12-17T23:47:15.4777777Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:15.4778090Z ##[endgroup] 2024-12-17T23:47:15.5038123Z "docker stop" requires at least 1 argument. 2024-12-17T23:47:15.5038532Z See 'docker stop --help'. 2024-12-17T23:47:15.5038727Z 2024-12-17T23:47:15.5038927Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-12-17T23:47:15.5039219Z 2024-12-17T23:47:15.5039333Z Stop one or more running containers 2024-12-17T23:47:15.5192587Z Total reclaimed space: 0B 2024-12-17T23:47:15.5233708Z ##[group]Run set +e 2024-12-17T23:47:15.5234023Z set +e 2024-12-17T23:47:15.5234271Z set -x 2024-12-17T23:47:15.5234513Z  2024-12-17T23:47:15.5234772Z PT_DOMAIN=download.pytorch.org 2024-12-17T23:47:15.5235357Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-12-17T23:47:15.5236472Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-12-17T23:47:15.5237033Z # one is returned at random 2024-12-17T23:47:15.5237451Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-12-17T23:47:15.5237854Z  2024-12-17T23:47:15.5238229Z if [ -z "${RESOLVED_IP}" ]; then 2024-12-17T23:47:15.5238675Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-12-17T23:47:15.5239224Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-12-17T23:47:15.5239634Z  2024-12-17T23:47:15.5239885Z  if [ -z "${RESOLVED_IP}" ]; then 2024-12-17T23:47:15.5240289Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-12-17T23:47:15.5240664Z  exit 1 2024-12-17T23:47:15.5240904Z  fi 2024-12-17T23:47:15.5241132Z fi 2024-12-17T23:47:15.5241356Z  2024-12-17T23:47:15.5241636Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-12-17T23:47:15.5242023Z  # Clean up any old records first 2024-12-17T23:47:15.5242389Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-12-17T23:47:15.5242732Z fi 2024-12-17T23:47:15.5242952Z  2024-12-17T23:47:15.5243284Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-12-17T23:47:15.5243706Z cat /etc/hosts 2024-12-17T23:47:15.5249383Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:15.5249782Z env: 2024-12-17T23:47:15.5250014Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:15.5250299Z ##[endgroup] 2024-12-17T23:47:15.5273197Z + PT_DOMAIN=download.pytorch.org 2024-12-17T23:47:15.5279026Z ++ dig -4 +short download.pytorch.org 2024-12-17T23:47:15.5279682Z ++ tail -n1 2024-12-17T23:47:15.5990484Z + RESOLVED_IP=3.167.99.82 2024-12-17T23:47:15.5990942Z + '[' -z 3.167.99.82 ']' 2024-12-17T23:47:15.5991255Z + grep -r download.pytorch.org /etc/hosts 2024-12-17T23:47:15.6001273Z 3.167.99.89 download.pytorch.org 2024-12-17T23:47:15.6002379Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-12-17T23:47:15.8455598Z + echo '3.167.99.82 download.pytorch.org' 2024-12-17T23:47:15.8456151Z + sudo tee -a /etc/hosts 2024-12-17T23:47:15.8860692Z 3.167.99.82 download.pytorch.org 2024-12-17T23:47:15.8876128Z + cat /etc/hosts 2024-12-17T23:47:15.8884095Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-12-17T23:47:15.8890694Z ::1 localhost6 localhost6.localdomain6 2024-12-17T23:47:15.8891096Z 3.167.99.82 download.pytorch.org 2024-12-17T23:47:15.9054258Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@release/2.6 2024-12-17T23:47:15.9054768Z with: 2024-12-17T23:47:15.9055456Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9056239Z docker-build-dir: .ci/docker 2024-12-17T23:47:15.9056553Z working-directory: . 2024-12-17T23:47:15.9056919Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:15.9057320Z force-push: false 2024-12-17T23:47:15.9057566Z env: 2024-12-17T23:47:15.9057790Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:15.9058062Z ##[endgroup] 2024-12-17T23:47:15.9081141Z ##[group]Run set -ex 2024-12-17T23:47:15.9081469Z set -ex 2024-12-17T23:47:15.9081714Z  2024-12-17T23:47:15.9082130Z # If the docker build directory or the build script doesn't exist, the action will 2024-12-17T23:47:15.9082877Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-12-17T23:47:15.9083453Z # job could then download the pre-built image as usual 2024-12-17T23:47:15.9083987Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-12-17T23:47:15.9084485Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9085100Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9085530Z  2024-12-17T23:47:15.9085901Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-12-17T23:47:15.9086347Z  exit 0 2024-12-17T23:47:15.9086586Z else 2024-12-17T23:47:15.9086867Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9087208Z fi 2024-12-17T23:47:15.9087431Z  2024-12-17T23:47:15.9087768Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-12-17T23:47:15.9088394Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-12-17T23:47:15.9088942Z  # use it as it is, but first let's extract the tag 2024-12-17T23:47:15.9089442Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-12-17T23:47:15.9089968Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9090476Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9090900Z else 2024-12-17T23:47:15.9091215Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-12-17T23:47:15.9091691Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9092353Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9092936Z fi 2024-12-17T23:47:15.9099500Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:15.9099896Z env: 2024-12-17T23:47:15.9100110Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:15.9100389Z REPO_NAME: pytorch 2024-12-17T23:47:15.9101099Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9101863Z DOCKER_BUILD_DIR: .ci/docker 2024-12-17T23:47:15.9102257Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:15.9102646Z ##[endgroup] 2024-12-17T23:47:15.9130360Z + [[ ! -d .ci/docker ]] 2024-12-17T23:47:15.9130894Z + [[ ! -f .ci/docker/build.sh ]] 2024-12-17T23:47:15.9131383Z + echo skip=false 2024-12-17T23:47:15.9132522Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-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* ]] 2024-12-17T23:47:15.9137244Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9138070Z ++ awk -F '[:,]' '{print $2}' 2024-12-17T23:47:15.9156594Z + DOCKER_TAG=45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9157056Z + echo docker-tag=45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9157901Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9185290Z ##[group]Run set +e 2024-12-17T23:47:15.9185640Z set +e 2024-12-17T23:47:15.9185886Z set -x 2024-12-17T23:47:15.9186123Z  2024-12-17T23:47:15.9186338Z login() { 2024-12-17T23:47:15.9186837Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-12-17T23:47:15.9187374Z } 2024-12-17T23:47:15.9187593Z  2024-12-17T23:47:15.9187815Z retry () { 2024-12-17T23:47:15.9188100Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-12-17T23:47:15.9188530Z } 2024-12-17T23:47:15.9188750Z  2024-12-17T23:47:15.9188995Z retry login "${DOCKER_REGISTRY}" 2024-12-17T23:47:15.9189318Z  2024-12-17T23:47:15.9189536Z START_TIME=$(date +%s) 2024-12-17T23:47:15.9189843Z # Wait up to 90 minutes 2024-12-17T23:47:15.9190349Z while [[ $(( $(date +%s) - 5400 )) -lt $START_TIME ]]; do 2024-12-17T23:47:15.9190867Z  # Check if image already exists, if it does then skip building it 2024-12-17T23:47:15.9191377Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-12-17T23:47:15.9191762Z  exit 0 2024-12-17T23:47:15.9191998Z  fi 2024-12-17T23:47:15.9192228Z  2024-12-17T23:47:15.9192628Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2024-12-17T23:47:15.9193316Z  # use this to differentiate between the Docker build and regular build jobs. For the 2024-12-17T23:47:15.9193997Z  # latter, it will wait for the Docker images to become available before continuing 2024-12-17T23:47:15.9194534Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2024-12-17T23:47:15.9194939Z  # It's a Docker build job, let's build the image 2024-12-17T23:47:15.9195306Z  break 2024-12-17T23:47:15.9195556Z  else 2024-12-17T23:47:15.9195912Z  # It's a regular build job, wait for the image to become available 2024-12-17T23:47:15.9196343Z  sleep 300 2024-12-17T23:47:15.9196591Z  fi 2024-12-17T23:47:15.9196822Z done 2024-12-17T23:47:15.9197048Z  2024-12-17T23:47:15.9197412Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-12-17T23:47:15.9198000Z # be empty. The default action would be to continue rebuild the image 2024-12-17T23:47:15.9198520Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-12-17T23:47:15.9198995Z  # if we're on the base branch then use the parent commit 2024-12-17T23:47:15.9199416Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-12-17T23:47:15.9199749Z else 2024-12-17T23:47:15.9200091Z  # otherwise we're on a PR, so use the most recent base commit 2024-12-17T23:47:15.9200589Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-12-17T23:47:15.9200954Z fi 2024-12-17T23:47:15.9201175Z  2024-12-17T23:47:15.9201422Z if [[ -z "${MERGE_BASE}" ]]; then 2024-12-17T23:47:15.9201791Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9202134Z  2024-12-17T23:47:15.9202599Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-12-17T23:47:15.9203166Z  exit 0 2024-12-17T23:47:15.9203402Z fi 2024-12-17T23:47:15.9203717Z  2024-12-17T23:47:15.9204042Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-12-17T23:47:15.9204749Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-12-17T23:47:15.9205343Z  exit 1 2024-12-17T23:47:15.9205581Z fi 2024-12-17T23:47:15.9205809Z  2024-12-17T23:47:15.9206184Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-12-17T23:47:15.9206854Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-12-17T23:47:15.9207449Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-12-17T23:47:15.9208151Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-12-17T23:47:15.9208943Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-12-17T23:47:15.9209416Z fi 2024-12-17T23:47:15.9209639Z  2024-12-17T23:47:15.9209916Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:15.9215629Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:15.9216020Z env: 2024-12-17T23:47:15.9216248Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:15.9216621Z DOCKER_BUILD_DIR: .ci/docker 2024-12-17T23:47:15.9217011Z BASE_REVISION: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:47:15.9217825Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9218619Z DOCKER_TAG: 45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:15.9219072Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:15.9219476Z DOCKER_PUSH: 2024-12-17T23:47:15.9219706Z ##[endgroup] 2024-12-17T23:47:15.9242988Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:15.9243448Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:15.9246171Z + aws ecr get-login-password --region us-east-1 2024-12-17T23:47:15.9247184Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:16.4764685Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:16.4765370Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:16.4766201Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:16.4766948Z 2024-12-17T23:47:16.4767095Z Login Succeeded 2024-12-17T23:47:16.4780803Z ++ date +%s 2024-12-17T23:47:16.4789394Z + START_TIME=1734479236 2024-12-17T23:47:16.4792499Z ++ date +%s 2024-12-17T23:47:16.4800563Z + [[ 1734473836 -lt 1734479236 ]] 2024-12-17T23:47:16.4801751Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:16.7102805Z { 2024-12-17T23:47:16.7103218Z "schemaVersion": 2, 2024-12-17T23:47:16.7103842Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-12-17T23:47:16.7104513Z "config": { 2024-12-17T23:47:16.7105043Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-12-17T23:47:16.7105710Z "size": 41578, 2024-12-17T23:47:16.7106474Z "digest": "sha256:59a4df73988ee309dbf7bbeebf91f7b6258b2da2804e245afce1c5d776a178fb" 2024-12-17T23:47:16.7107310Z }, 2024-12-17T23:47:16.7107631Z "layers": [ 2024-12-17T23:47:16.7107972Z { 2024-12-17T23:47:16.7108558Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7109180Z "size": 28583948, 2024-12-17T23:47:16.7109849Z "digest": "sha256:86e5016c269355b382c9cabab4f6646d56d75914f20d545289970436dae431b1" 2024-12-17T23:47:16.7110613Z }, 2024-12-17T23:47:16.7110905Z { 2024-12-17T23:47:16.7111785Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7112497Z "size": 1825, 2024-12-17T23:47:16.7113131Z "digest": "sha256:7d35dd005d9e53df675bcdf68f09e0c31962bce9d4460868804cd7447886a662" 2024-12-17T23:47:16.7113931Z }, 2024-12-17T23:47:16.7114219Z { 2024-12-17T23:47:16.7114766Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7115491Z "size": 313453552, 2024-12-17T23:47:16.7116184Z "digest": "sha256:a42b4bec9b5da4744bbd87e7918c7a4c007bdb60ac07be9a26c2b5c199f859c2" 2024-12-17T23:47:16.7116990Z }, 2024-12-17T23:47:16.7117260Z { 2024-12-17T23:47:16.7117787Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7118378Z "size": 864, 2024-12-17T23:47:16.7119112Z "digest": "sha256:685c62b4e7aa9afe9cde9dd72aee053fa1ea76493176f85911db071928b0ab0a" 2024-12-17T23:47:16.7119814Z }, 2024-12-17T23:47:16.7120076Z { 2024-12-17T23:47:16.7120549Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7121095Z "size": 79405133, 2024-12-17T23:47:16.7121529Z "digest": "sha256:a31cb67fce0c181664e4591b61367b8ccb0626a6721d089cb024e823677632ed" 2024-12-17T23:47:16.7122016Z }, 2024-12-17T23:47:16.7122220Z { 2024-12-17T23:47:16.7122540Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7122968Z "size": 704, 2024-12-17T23:47:16.7123740Z "digest": "sha256:443dc476b9747272f1701a613c22a475d4bf60d5975e78a4bb391129ba15e376" 2024-12-17T23:47:16.7124217Z }, 2024-12-17T23:47:16.7124417Z { 2024-12-17T23:47:16.7124738Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7125167Z "size": 1261, 2024-12-17T23:47:16.7125583Z "digest": "sha256:e45ebb0a20c4d86d45e589fb9496886950c5df27bf77b591e4774539f4b8343d" 2024-12-17T23:47:16.7126061Z }, 2024-12-17T23:47:16.7126261Z { 2024-12-17T23:47:16.7126585Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7127011Z "size": 484, 2024-12-17T23:47:16.7127428Z "digest": "sha256:fc2ca34e133a39c92308e15441070e8400230049713fb2d8129b35352dcf9564" 2024-12-17T23:47:16.7127900Z }, 2024-12-17T23:47:16.7128106Z { 2024-12-17T23:47:16.7128425Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7128849Z "size": 110, 2024-12-17T23:47:16.7129260Z "digest": "sha256:0b860640e7657cea5adc30868a80fd359d80ce4ebe75862b2e03e33b3a6e5308" 2024-12-17T23:47:16.7129747Z }, 2024-12-17T23:47:16.7129949Z { 2024-12-17T23:47:16.7130270Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7130699Z "size": 4152, 2024-12-17T23:47:16.7131126Z "digest": "sha256:b77b2d9def9e96b3850b0fd7ce077fee5e838fe54f2ce6e7b1d338c910153b42" 2024-12-17T23:47:16.7131616Z }, 2024-12-17T23:47:16.7131806Z { 2024-12-17T23:47:16.7132138Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7132568Z "size": 1860, 2024-12-17T23:47:16.7132985Z "digest": "sha256:3998865a45586d75c09588746941ba15835dfa83d29362d5a69e7c54ed3b64ee" 2024-12-17T23:47:16.7133456Z }, 2024-12-17T23:47:16.7133645Z { 2024-12-17T23:47:16.7133979Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7134407Z "size": 700, 2024-12-17T23:47:16.7134830Z "digest": "sha256:a97b321f18977fb74ff632dca9041e91fdb329ee2efdf320a53dacb7d2f02495" 2024-12-17T23:47:16.7135430Z + exit 0 2024-12-17T23:47:16.7135639Z }, 2024-12-17T23:47:16.7135824Z { 2024-12-17T23:47:16.7136378Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7136809Z "size": 477, 2024-12-17T23:47:16.7137229Z "digest": "sha256:3323984ef3d0db5d18e6aa1f053c841de7141721bac6d7f8fb1c1366847c9ff5" 2024-12-17T23:47:16.7137708Z }, 2024-12-17T23:47:16.7137896Z { 2024-12-17T23:47:16.7138230Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7138662Z "size": 2687732196, 2024-12-17T23:47:16.7139247Z "digest": "sha256:bc220d400fc17203b80a0d3d9f9026ccef0184961ad3e34546b2a61fcdae8c00" 2024-12-17T23:47:16.7139736Z }, 2024-12-17T23:47:16.7139925Z { 2024-12-17T23:47:16.7140258Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7140684Z "size": 32, 2024-12-17T23:47:16.7141098Z "digest": 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"sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7267197Z }, 2024-12-17T23:47:16.7267382Z { 2024-12-17T23:47:16.7267707Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7268125Z "size": 161, 2024-12-17T23:47:16.7268627Z "digest": "sha256:c8024304b2839eef704fe195c1909508616cbb13e25d4a3e676a9fdf36684755" 2024-12-17T23:47:16.7269190Z }, 2024-12-17T23:47:16.7269375Z { 2024-12-17T23:47:16.7269704Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7270124Z "size": 683, 2024-12-17T23:47:16.7270539Z "digest": "sha256:8444d3a64f1d209c6b2c314b8cdcfc0a3e1841cb4a5c01ac1bc933d0acf8bd8e" 2024-12-17T23:47:16.7271021Z }, 2024-12-17T23:47:16.7271207Z { 2024-12-17T23:47:16.7271536Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7271964Z "size": 700, 2024-12-17T23:47:16.7272389Z "digest": "sha256:a97b321f18977fb74ff632dca9041e91fdb329ee2efdf320a53dacb7d2f02495" 2024-12-17T23:47:16.7272879Z }, 2024-12-17T23:47:16.7273071Z { 2024-12-17T23:47:16.7273414Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7273852Z "size": 138, 2024-12-17T23:47:16.7274266Z "digest": "sha256:fb87d5a72395572b932ad801997d402f2e5780fbf2b503e269c6485d97e2d4d6" 2024-12-17T23:47:16.7274748Z }, 2024-12-17T23:47:16.7274944Z { 2024-12-17T23:47:16.7275279Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7275709Z "size": 32, 2024-12-17T23:47:16.7276126Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7276615Z }, 2024-12-17T23:47:16.7276805Z { 2024-12-17T23:47:16.7277138Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7277632Z "size": 160, 2024-12-17T23:47:16.7278051Z "digest": "sha256:40f7893b090dd7aa03877d64bce6904bb06524f81a34e01de8ebc4c22857e0c8" 2024-12-17T23:47:16.7278528Z }, 2024-12-17T23:47:16.7278718Z { 2024-12-17T23:47:16.7279055Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7279481Z "size": 907, 2024-12-17T23:47:16.7279911Z "digest": "sha256:b5cda50ebeb5dbea3b12f25f4e2bdc3eb39efac73195030563c7bf230ecabb74" 2024-12-17T23:47:16.7280404Z }, 2024-12-17T23:47:16.7280591Z { 2024-12-17T23:47:16.7280926Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7281350Z "size": 700, 2024-12-17T23:47:16.7281772Z "digest": "sha256:a97b321f18977fb74ff632dca9041e91fdb329ee2efdf320a53dacb7d2f02495" 2024-12-17T23:47:16.7282258Z }, 2024-12-17T23:47:16.7282447Z { 2024-12-17T23:47:16.7282778Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7283214Z "size": 135, 2024-12-17T23:47:16.7283634Z "digest": "sha256:85bc880833f6f7ee208c72ea12310619f39cfd83efc1455ac3edeab03fbcfdf6" 2024-12-17T23:47:16.7284121Z }, 2024-12-17T23:47:16.7284309Z { 2024-12-17T23:47:16.7284642Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7285069Z "size": 32, 2024-12-17T23:47:16.7285487Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7285968Z }, 2024-12-17T23:47:16.7286155Z { 2024-12-17T23:47:16.7286494Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7286925Z "size": 158, 2024-12-17T23:47:16.7287338Z "digest": "sha256:ae2d3bac1079b27e53f500723421a2836f648a83846c8cbd4252f9980ebe9f2e" 2024-12-17T23:47:16.7287812Z }, 2024-12-17T23:47:16.7288001Z { 2024-12-17T23:47:16.7288338Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7288770Z "size": 1521, 2024-12-17T23:47:16.7289194Z "digest": "sha256:16c0054b16e2a08c9403cc96dc841b32e91e932968cb0fc9aaf5d28384ce7be6" 2024-12-17T23:47:16.7289671Z }, 2024-12-17T23:47:16.7289861Z { 2024-12-17T23:47:16.7290195Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7290617Z "size": 32, 2024-12-17T23:47:16.7291035Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7291518Z }, 2024-12-17T23:47:16.7291708Z { 2024-12-17T23:47:16.7292044Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7292554Z "size": 136, 2024-12-17T23:47:16.7292987Z "digest": "sha256:3f9a54d0d31eaff71f08abb2523da7d8e2cfe4e4c8b19ce805c070880bf7c2cc" 2024-12-17T23:47:16.7293480Z }, 2024-12-17T23:47:16.7293669Z { 2024-12-17T23:47:16.7294004Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7294432Z "size": 379, 2024-12-17T23:47:16.7294851Z "digest": "sha256:046cb5046fc73c9fc89b78c6860b8a5e5975563adb9d0c6eb833ae4da84be49e" 2024-12-17T23:47:16.7295339Z }, 2024-12-17T23:47:16.7295526Z { 2024-12-17T23:47:16.7295857Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7296280Z "size": 32, 2024-12-17T23:47:16.7296692Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7297173Z }, 2024-12-17T23:47:16.7297360Z { 2024-12-17T23:47:16.7297693Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7298117Z "size": 104, 2024-12-17T23:47:16.7298531Z "digest": "sha256:6f89b73663e962e621154a70aadc28deacc008cd96f180f1296a3ad995ec3e8d" 2024-12-17T23:47:16.7298999Z }, 2024-12-17T23:47:16.7299199Z { 2024-12-17T23:47:16.7299531Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7299958Z "size": 1932, 2024-12-17T23:47:16.7300390Z "digest": "sha256:d68f1afba5fa41f71ed4a73cf94b8e806148abe0e692a455ed3d231f7bda7317" 2024-12-17T23:47:16.7300930Z }, 2024-12-17T23:47:16.7301141Z { 2024-12-17T23:47:16.7301482Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7301917Z "size": 234173055, 2024-12-17T23:47:16.7302365Z "digest": "sha256:56aaeadbe1af86251a75b3f0768af4dfa29ec7314e2c2e87363bde721aa94369" 2024-12-17T23:47:16.7302862Z }, 2024-12-17T23:47:16.7303057Z { 2024-12-17T23:47:16.7303382Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7303815Z "size": 106, 2024-12-17T23:47:16.7304249Z "digest": "sha256:bd90a525e7bb19a447fabff4d87b25b4fcceb7c5f03c1e49a8802dd22a93a92b" 2024-12-17T23:47:16.7304748Z }, 2024-12-17T23:47:16.7304954Z { 2024-12-17T23:47:16.7305282Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7305713Z "size": 165, 2024-12-17T23:47:16.7306131Z "digest": "sha256:0895a1ba2fdb9e7275dd7651ac9a52d0f98e2f8a5865f268f569346515a3dde0" 2024-12-17T23:47:16.7306622Z }, 2024-12-17T23:47:16.7306830Z { 2024-12-17T23:47:16.7307154Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7307590Z "size": 7943, 2024-12-17T23:47:16.7308011Z "digest": "sha256:57d5003c8ea23785943ba9be592f47316ca1007ca20a03ae0f5bbbd4233f1891" 2024-12-17T23:47:16.7308579Z }, 2024-12-17T23:47:16.7308785Z { 2024-12-17T23:47:16.7309107Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7309537Z "size": 8071, 2024-12-17T23:47:16.7309962Z "digest": "sha256:8c175b92c272619268504669e620b0a855f31dfdafd62e582358554124970d6b" 2024-12-17T23:47:16.7310433Z }, 2024-12-17T23:47:16.7310639Z { 2024-12-17T23:47:16.7310960Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7311384Z "size": 304, 2024-12-17T23:47:16.7311803Z "digest": "sha256:4c1758e11fc4388fc835fb6d7c09c0cc5483f89cc6b38fe7765d01418c7ca8fa" 2024-12-17T23:47:16.7312280Z }, 2024-12-17T23:47:16.7312484Z { 2024-12-17T23:47:16.7312805Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7313227Z "size": 32, 2024-12-17T23:47:16.7313641Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7314125Z }, 2024-12-17T23:47:16.7314317Z { 2024-12-17T23:47:16.7314629Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7315057Z "size": 108, 2024-12-17T23:47:16.7315479Z "digest": "sha256:1f48c41df7ceaf9b5cb42c03333af66102ad8729352a030d9d9dac48f2542dab" 2024-12-17T23:47:16.7315963Z }, 2024-12-17T23:47:16.7316242Z { 2024-12-17T23:47:16.7316564Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7316984Z "size": 54145661, 2024-12-17T23:47:16.7317404Z "digest": "sha256:c58640635e769decd88d1b69ac3689d623a4014cfc2f26b23087cdd796b2cf70" 2024-12-17T23:47:16.7317870Z }, 2024-12-17T23:47:16.7318059Z { 2024-12-17T23:47:16.7318373Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:16.7318795Z "size": 32, 2024-12-17T23:47:16.7319213Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:16.7319696Z } 2024-12-17T23:47:16.7319899Z ] 2024-12-17T23:47:16.7320083Z } 2024-12-17T23:47:16.7352372Z ##[group]Run set -eux 2024-12-17T23:47:16.7352681Z set -eux 2024-12-17T23:47:16.7353595Z 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 2024-12-17T23:47:16.7361218Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:16.7361616Z env: 2024-12-17T23:47:16.7361833Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:16.7362106Z ##[endgroup] 2024-12-17T23:47:16.7387896Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2024-12-17T23:47:16.7389154Z + jq --raw-output .SecretString 2024-12-17T23:47:16.7390174Z + jq -r .docker_hub_readonly_token 2024-12-17T23:47:16.7393682Z + docker login --username pytorchbot --password-stdin 2024-12-17T23:47:17.3950976Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:17.3951968Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:17.3952796Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:17.3953401Z 2024-12-17T23:47:17.3953548Z Login Succeeded 2024-12-17T23:47:17.4039452Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-12-17T23:47:17.4039847Z tag=${ECR_DOCKER_IMAGE##*/} 2024-12-17T23:47:17.4040269Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-12-17T23:47:17.4046069Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:17.4046471Z env: 2024-12-17T23:47:17.4046701Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:17.4047425Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:17.4048180Z ##[endgroup] 2024-12-17T23:47:17.4072726Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:17.4125068Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@release/2.6 2024-12-17T23:47:17.4125559Z with: 2024-12-17T23:47:17.4126228Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:17.4127078Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:17.4127490Z env: 2024-12-17T23:47:17.4127716Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:17.4127977Z ##[endgroup] 2024-12-17T23:47:17.4152614Z ##[group]Run set -x 2024-12-17T23:47:17.4152911Z set -x 2024-12-17T23:47:17.4153164Z set +e 2024-12-17T23:47:17.4153406Z  2024-12-17T23:47:17.4153656Z login() { 2024-12-17T23:47:17.4154161Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-12-17T23:47:17.4154705Z } 2024-12-17T23:47:17.4154922Z  2024-12-17T23:47:17.4155207Z retry () { 2024-12-17T23:47:17.4155501Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-12-17T23:47:17.4155830Z } 2024-12-17T23:47:17.4156057Z  2024-12-17T23:47:17.4156305Z retry login "${DOCKER_REGISTRY}" 2024-12-17T23:47:17.4156628Z  2024-12-17T23:47:17.4156850Z set -e 2024-12-17T23:47:17.4157198Z # ignore output since only exit code is used for conditional 2024-12-17T23:47:17.4157715Z # only pull docker image if it's not available locally 2024-12-17T23:47:17.4158280Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-12-17T23:47:17.4158801Z  retry docker pull "${DOCKER_IMAGE}" 2024-12-17T23:47:17.4159147Z fi 2024-12-17T23:47:17.4164901Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:17.4165302Z env: 2024-12-17T23:47:17.4165536Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:17.4166264Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:17.4167107Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:17.4167514Z ##[endgroup] 2024-12-17T23:47:17.4189527Z + set +e 2024-12-17T23:47:17.4190207Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:17.4190686Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:17.4195347Z + aws ecr get-login-password --region us-east-1 2024-12-17T23:47:17.4196069Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:17.9637144Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:17.9638161Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:17.9638904Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:17.9639302Z 2024-12-17T23:47:17.9639436Z Login Succeeded 2024-12-17T23:47:17.9650118Z + set -e 2024-12-17T23:47:17.9650909Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:17.9779807Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:17.9781041Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:18.2152284Z 45e1356b47a284893081276eff3000b7b534f3b1: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-12-17T23:47:18.2159421Z 86e5016c2693: Pulling fs layer 2024-12-17T23:47:18.2160205Z 7d35dd005d9e: Pulling fs layer 2024-12-17T23:47:18.2162442Z a42b4bec9b5d: Pulling fs layer 2024-12-17T23:47:18.2163119Z 685c62b4e7aa: Pulling fs layer 2024-12-17T23:47:18.2163587Z a31cb67fce0c: Pulling fs layer 2024-12-17T23:47:18.2164435Z 443dc476b974: Pulling fs layer 2024-12-17T23:47:18.2164892Z e45ebb0a20c4: Pulling fs layer 2024-12-17T23:47:18.2165395Z fc2ca34e133a: Pulling fs layer 2024-12-17T23:47:18.2165822Z 0b860640e765: Pulling fs layer 2024-12-17T23:47:18.2166245Z b77b2d9def9e: Pulling fs layer 2024-12-17T23:47:18.2166702Z 3998865a4558: Pulling fs layer 2024-12-17T23:47:18.2167153Z a97b321f1897: Pulling fs layer 2024-12-17T23:47:18.2167596Z 3323984ef3d0: Pulling fs layer 2024-12-17T23:47:18.2168041Z a31cb67fce0c: Waiting 2024-12-17T23:47:18.2168476Z bc220d400fc1: Pulling fs layer 2024-12-17T23:47:18.2168905Z e45ebb0a20c4: Waiting 2024-12-17T23:47:18.2169345Z 4f4fb700ef54: Pulling fs layer 2024-12-17T23:47:18.2169837Z 443dc476b974: Waiting 2024-12-17T23:47:18.2170269Z fc2ca34e133a: Waiting 2024-12-17T23:47:18.2170719Z 4597f957f51e: Pulling fs layer 2024-12-17T23:47:18.2171183Z 0b860640e765: Waiting 2024-12-17T23:47:18.2171532Z 3998865a4558: Waiting 2024-12-17T23:47:18.2171846Z 06b3659165cc: Pulling fs layer 2024-12-17T23:47:18.2172148Z a97b321f1897: Waiting 2024-12-17T23:47:18.2172476Z 832d1727bc5d: Pulling fs layer 2024-12-17T23:47:18.2172762Z b77b2d9def9e: Waiting 2024-12-17T23:47:18.2173045Z 6d799691bec4: Pulling fs layer 2024-12-17T23:47:18.2173327Z 5cdb6e2777e2: Pulling fs layer 2024-12-17T23:47:18.2173627Z 6f71cfa1fdc7: Pulling fs layer 2024-12-17T23:47:18.2174133Z 3323984ef3d0: Waiting 2024-12-17T23:47:18.2174431Z 073117d82573: Pulling fs layer 2024-12-17T23:47:18.2174728Z ac6c785c6efc: Pulling fs layer 2024-12-17T23:47:18.2175012Z 0559c3302578: Pulling fs layer 2024-12-17T23:47:18.2175298Z 685c62b4e7aa: Waiting 2024-12-17T23:47:18.2175617Z f7b86d17c395: Pulling fs layer 2024-12-17T23:47:18.2175946Z da7628384d14: Pulling fs layer 2024-12-17T23:47:18.2176239Z 9ffe206157f6: Pulling fs layer 2024-12-17T23:47:18.2176519Z 4597f957f51e: Waiting 2024-12-17T23:47:18.2176779Z 8ef62406be30: Pulling fs layer 2024-12-17T23:47:18.2177068Z 39d65501dedd: Pulling fs layer 2024-12-17T23:47:18.2177360Z fc717a79495f: Pulling fs layer 2024-12-17T23:47:18.2177699Z a1f505ee82fa: Pulling fs layer 2024-12-17T23:47:18.2177997Z 1f5a706b22b7: Pulling fs layer 2024-12-17T23:47:18.2178275Z 06b3659165cc: Waiting 2024-12-17T23:47:18.2178570Z 6f71cfa1fdc7: Waiting 2024-12-17T23:47:18.2178835Z e7b892fbe708: Pulling fs layer 2024-12-17T23:47:18.2179171Z 073117d82573: Waiting 2024-12-17T23:47:18.2179487Z 5f4a19cb068d: Pulling fs layer 2024-12-17T23:47:18.2179782Z ac6c785c6efc: Waiting 2024-12-17T23:47:18.2180065Z 2506b19110aa: Pulling fs layer 2024-12-17T23:47:18.2180342Z 832d1727bc5d: Waiting 2024-12-17T23:47:18.2180586Z 0559c3302578: Waiting 2024-12-17T23:47:18.2180832Z bf298afd8feb: Pulling fs layer 2024-12-17T23:47:18.2181129Z b6f26b3bf34a: Pulling fs layer 2024-12-17T23:47:18.2181604Z 6d799691bec4: Waiting 2024-12-17T23:47:18.2181861Z 923309a5702b: Pulling fs layer 2024-12-17T23:47:18.2182143Z f7b86d17c395: Waiting 2024-12-17T23:47:18.2182490Z 5cdb6e2777e2: Waiting 2024-12-17T23:47:18.2182748Z 05770cd73324: Pulling fs layer 2024-12-17T23:47:18.2183040Z da7628384d14: Waiting 2024-12-17T23:47:18.2183304Z ed70779f9e58: Pulling fs layer 2024-12-17T23:47:18.2183750Z 21ff7b3f102d: Pulling fs layer 2024-12-17T23:47:18.2184064Z 4f4fb700ef54: Waiting 2024-12-17T23:47:18.2184328Z bc220d400fc1: Waiting 2024-12-17T23:47:18.2184600Z a1f505ee82fa: Waiting 2024-12-17T23:47:18.2184864Z 54d4d01c4064: Pulling fs layer 2024-12-17T23:47:18.2185156Z 6630a9991ff0: Pulling fs layer 2024-12-17T23:47:18.2185426Z 1f5a706b22b7: Waiting 2024-12-17T23:47:18.2185685Z 23e1e14e321a: Pulling fs layer 2024-12-17T23:47:18.2185982Z f3cf2a094570: Pulling fs layer 2024-12-17T23:47:18.2186268Z 39d65501dedd: Waiting 2024-12-17T23:47:18.2186521Z bf298afd8feb: Waiting 2024-12-17T23:47:18.2187040Z e7b892fbe708: Waiting 2024-12-17T23:47:18.2187407Z b6f26b3bf34a: Waiting 2024-12-17T23:47:18.2187798Z 63ef1f16a20c: Pulling fs layer 2024-12-17T23:47:18.2188289Z 5f4a19cb068d: Waiting 2024-12-17T23:47:18.2188673Z 923309a5702b: Waiting 2024-12-17T23:47:18.2189026Z c5ab4a6866b6: Pulling fs layer 2024-12-17T23:47:18.2189557Z 05770cd73324: Waiting 2024-12-17T23:47:18.2189991Z ed70779f9e58: Waiting 2024-12-17T23:47:18.2190355Z 8ef62406be30: Waiting 2024-12-17T23:47:18.2190741Z 2506b19110aa: Waiting 2024-12-17T23:47:18.2191312Z d286081aaf3d: Pulling fs layer 2024-12-17T23:47:18.2191861Z fc717a79495f: Waiting 2024-12-17T23:47:18.2192508Z 54d4d01c4064: Waiting 2024-12-17T23:47:18.2192885Z dff332de7f89: Pulling fs layer 2024-12-17T23:47:18.2193287Z 23e1e14e321a: Waiting 2024-12-17T23:47:18.2193827Z 6630a9991ff0: Waiting 2024-12-17T23:47:18.2194201Z f3cf2a094570: Waiting 2024-12-17T23:47:18.2194484Z 63ef1f16a20c: Waiting 2024-12-17T23:47:18.2194993Z ce994207386b: Pulling fs layer 2024-12-17T23:47:18.2195563Z dff332de7f89: Waiting 2024-12-17T23:47:18.2196145Z 4d16d8f30102: Pulling fs layer 2024-12-17T23:47:18.2196544Z 21ff7b3f102d: Waiting 2024-12-17T23:47:18.2196862Z 5f204d7b058b: Pulling fs layer 2024-12-17T23:47:18.2197288Z c8024304b283: Pulling fs layer 2024-12-17T23:47:18.2197683Z 4d16d8f30102: Waiting 2024-12-17T23:47:18.2198053Z 8444d3a64f1d: Pulling fs layer 2024-12-17T23:47:18.2198544Z ce994207386b: Waiting 2024-12-17T23:47:18.2199025Z 5f204d7b058b: Waiting 2024-12-17T23:47:18.2199503Z 8444d3a64f1d: Waiting 2024-12-17T23:47:18.2200160Z fb87d5a72395: Pulling fs layer 2024-12-17T23:47:18.2200770Z 40f7893b090d: Pulling fs layer 2024-12-17T23:47:18.2201521Z b5cda50ebeb5: Pulling fs layer 2024-12-17T23:47:18.2202205Z 40f7893b090d: Waiting 2024-12-17T23:47:18.2202828Z 85bc880833f6: Pulling fs layer 2024-12-17T23:47:18.2203415Z b5cda50ebeb5: Waiting 2024-12-17T23:47:18.2203955Z 85bc880833f6: Waiting 2024-12-17T23:47:18.2204439Z ae2d3bac1079: Pulling fs layer 2024-12-17T23:47:18.2204990Z 16c0054b16e2: Pulling fs layer 2024-12-17T23:47:18.2205512Z fb87d5a72395: Waiting 2024-12-17T23:47:18.2206019Z ae2d3bac1079: Waiting 2024-12-17T23:47:18.2206566Z 3f9a54d0d31e: Pulling fs layer 2024-12-17T23:47:18.2207076Z 16c0054b16e2: Waiting 2024-12-17T23:47:18.2207630Z c8024304b283: Waiting 2024-12-17T23:47:18.2208406Z 046cb5046fc7: Pulling fs layer 2024-12-17T23:47:18.2209310Z 9ffe206157f6: Waiting 2024-12-17T23:47:18.2209802Z 6f89b73663e9: Pulling fs layer 2024-12-17T23:47:18.2210441Z d68f1afba5fa: Pulling fs layer 2024-12-17T23:47:18.2210976Z 56aaeadbe1af: Pulling fs layer 2024-12-17T23:47:18.2211487Z bd90a525e7bb: Pulling fs layer 2024-12-17T23:47:18.2212143Z 3f9a54d0d31e: Waiting 2024-12-17T23:47:18.2212677Z 046cb5046fc7: Waiting 2024-12-17T23:47:18.2213157Z 56aaeadbe1af: Waiting 2024-12-17T23:47:18.2213477Z 0895a1ba2fdb: Pulling fs layer 2024-12-17T23:47:18.2213865Z d68f1afba5fa: Waiting 2024-12-17T23:47:18.2214272Z 57d5003c8ea2: Pulling fs layer 2024-12-17T23:47:18.2214648Z bd90a525e7bb: Waiting 2024-12-17T23:47:18.2214975Z 0895a1ba2fdb: Waiting 2024-12-17T23:47:18.2215473Z 8c175b92c272: Pulling fs layer 2024-12-17T23:47:18.2215868Z 4c1758e11fc4: Pulling fs layer 2024-12-17T23:47:18.2216232Z 6f89b73663e9: Waiting 2024-12-17T23:47:18.2216677Z 57d5003c8ea2: Waiting 2024-12-17T23:47:18.2217048Z 1f48c41df7ce: Pulling fs layer 2024-12-17T23:47:18.2217367Z 8c175b92c272: Waiting 2024-12-17T23:47:18.2217765Z c58640635e76: Pulling fs layer 2024-12-17T23:47:18.2218149Z 1f48c41df7ce: Waiting 2024-12-17T23:47:18.2218498Z c58640635e76: Waiting 2024-12-17T23:47:18.2218854Z 4c1758e11fc4: Waiting 2024-12-17T23:47:18.3080622Z 7d35dd005d9e: Download complete 2024-12-17T23:47:18.3810209Z 685c62b4e7aa: Verifying Checksum 2024-12-17T23:47:18.3822754Z 685c62b4e7aa: Download complete 2024-12-17T23:47:18.5700261Z 86e5016c2693: Verifying Checksum 2024-12-17T23:47:18.5700816Z 86e5016c2693: Download complete 2024-12-17T23:47:18.6648652Z 443dc476b974: Verifying Checksum 2024-12-17T23:47:18.6649201Z 443dc476b974: Download complete 2024-12-17T23:47:18.7566275Z e45ebb0a20c4: Verifying Checksum 2024-12-17T23:47:18.7566901Z e45ebb0a20c4: Download complete 2024-12-17T23:47:18.8482670Z fc2ca34e133a: Download complete 2024-12-17T23:47:18.9164586Z 0b860640e765: Download complete 2024-12-17T23:47:19.0073851Z b77b2d9def9e: Verifying Checksum 2024-12-17T23:47:19.0074792Z b77b2d9def9e: Download complete 2024-12-17T23:47:19.0862734Z 3998865a4558: Verifying Checksum 2024-12-17T23:47:19.0863481Z 3998865a4558: Download complete 2024-12-17T23:47:19.1667675Z a97b321f1897: Verifying Checksum 2024-12-17T23:47:19.1668073Z a97b321f1897: Download complete 2024-12-17T23:47:19.2414462Z a31cb67fce0c: Verifying Checksum 2024-12-17T23:47:19.2414997Z a31cb67fce0c: Download complete 2024-12-17T23:47:19.2522482Z 3323984ef3d0: Verifying Checksum 2024-12-17T23:47:19.2523043Z 3323984ef3d0: Download complete 2024-12-17T23:47:19.2606370Z 4f4fb700ef54: Verifying Checksum 2024-12-17T23:47:19.2606947Z 4f4fb700ef54: Download complete 2024-12-17T23:47:19.3294641Z 4597f957f51e: Download complete 2024-12-17T23:47:19.4048024Z 06b3659165cc: Verifying Checksum 2024-12-17T23:47:19.4048672Z 06b3659165cc: Download complete 2024-12-17T23:47:19.4322082Z 86e5016c2693: Pull complete 2024-12-17T23:47:19.4543051Z 7d35dd005d9e: Pull complete 2024-12-17T23:47:19.4701947Z 832d1727bc5d: Verifying Checksum 2024-12-17T23:47:19.4702516Z 832d1727bc5d: Download complete 2024-12-17T23:47:19.5520401Z 6d799691bec4: Verifying Checksum 2024-12-17T23:47:19.5520864Z 6d799691bec4: Download complete 2024-12-17T23:47:19.6378660Z 5cdb6e2777e2: Verifying Checksum 2024-12-17T23:47:19.6379096Z 5cdb6e2777e2: Download complete 2024-12-17T23:47:19.7115572Z 6f71cfa1fdc7: Verifying Checksum 2024-12-17T23:47:19.7116193Z 6f71cfa1fdc7: Download complete 2024-12-17T23:47:19.7829348Z 073117d82573: Download complete 2024-12-17T23:47:19.8687241Z ac6c785c6efc: Verifying Checksum 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308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:48:52.0754864Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:48:52.0755844Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:48:52.0764117Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:48:52.0764499Z env: 2024-12-17T23:48:52.0764737Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:48:52.0765026Z ##[endgroup] 2024-12-17T23:48:52.0952430Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-12-17T23:48:52.0953036Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-12-17T23:48:52.0953560Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-12-17T23:48:52.0954063Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:48:52.0959518Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:48:52.0959910Z env: 2024-12-17T23:48:52.0960142Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:48:52.0960415Z ##[endgroup] 2024-12-17T23:48:52.4556661Z Defaulting to user installation because normal site-packages is not writeable 2024-12-17T23:48:52.4719812Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1) 2024-12-17T23:48:52.4725331Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84) 2024-12-17T23:48:52.6042685Z Prepare all required actions 2024-12-17T23:48:52.6043447Z Getting action download info 2024-12-17T23:48:52.7604667Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-12-17T23:48:52.9598613Z Download action repository 'actions/download-artifact@v4' (SHA:fa0a91b85d4f404e444e00e005971372dc801d16) 2024-12-17T23:48:53.1833331Z ##[group]Run ./.github/actions/download-build-artifacts 2024-12-17T23:48:53.1833712Z with: 2024-12-17T23:48:53.1833966Z name: linux-focal-py3.12-clang10 2024-12-17T23:48:53.1834296Z s3-bucket: gha-artifacts 2024-12-17T23:48:53.1834572Z env: 2024-12-17T23:48:53.1834804Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:48:53.1835068Z ##[endgroup] 2024-12-17T23:48:53.1893933Z ##[group]Run seemethere/download-artifact-s3@v4 2024-12-17T23:48:53.1894292Z with: 2024-12-17T23:48:53.1894545Z name: linux-focal-py3.12-clang10 2024-12-17T23:48:53.1894851Z s3-bucket: gha-artifacts 2024-12-17T23:48:53.1895197Z region: us-east-1 2024-12-17T23:48:53.1895441Z env: 2024-12-17T23:48:53.1895650Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:48:53.1895923Z ##[endgroup] 2024-12-17T23:48:53.6503913Z (node:316749) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-12-17T23:48:53.6504452Z 2024-12-17T23:48:53.6504648Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-12-17T23:48:53.6505203Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-12-17T23:48:53.6505779Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-12-17T23:48:53.7803246Z Found 1 objects with prefix pytorch/pytorch/12383255652/linux-focal-py3.12-clang10/ 2024-12-17T23:48:53.7804193Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-12-17T23:48:57.7761608Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-12-17T23:48:57.7768595Z Artifact download has finished successfully 2024-12-17T23:48:57.7935431Z ##[group]Run unzip -o artifacts.zip 2024-12-17T23:48:57.7935806Z unzip -o artifacts.zip 2024-12-17T23:48:57.7942337Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:48:57.7942759Z env: 2024-12-17T23:48:57.7943002Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:48:57.7943296Z ##[endgroup] 2024-12-17T23:48:57.8106500Z Archive: artifacts.zip 2024-12-17T23:48:57.8107400Z creating: dist/ 2024-12-17T23:48:58.7782721Z inflating: dist/torch-2.6.0a0+git0cdf8b1-cp312-cp312-linux_x86_64.whl 2024-12-17T23:48:58.7783510Z creating: build/custom_test_artifacts/ 2024-12-17T23:48:58.7784144Z creating: build/custom_test_artifacts/custom-op-build/ 2024-12-17T23:48:58.7784998Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2024-12-17T23:48:58.7785979Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeOutput.log 2024-12-17T23:48:58.7787017Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/ 2024-12-17T23:48:58.7788079Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-12-17T23:48:58.7789280Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-12-17T23:48:58.7790092Z creating: 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inflating: build/bin/lazy_tensor_test 2024-12-17T23:49:04.0310461Z inflating: build/bin/ivalue_test 2024-12-17T23:49:04.0358502Z inflating: build/bin/CppSignature_test 2024-12-17T23:49:04.0409034Z inflating: build/bin/mobile_memory_cleanup 2024-12-17T23:49:04.0462764Z inflating: build/bin/scalar_tensor_test 2024-12-17T23:49:04.0769714Z inflating: build/bin/op_registration_test 2024-12-17T23:49:04.0820148Z inflating: build/bin/math_kernel_test 2024-12-17T23:49:04.0870561Z inflating: build/bin/memory_format_test 2024-12-17T23:49:04.0924544Z inflating: build/bin/native_test 2024-12-17T23:49:04.0978025Z inflating: build/bin/reduce_ops_test 2024-12-17T23:49:04.1027440Z inflating: build/bin/packedtensoraccessor_test 2024-12-17T23:49:04.1081477Z inflating: build/bin/quantized_test 2024-12-17T23:49:04.1148886Z inflating: build/bin/pow_test 2024-12-17T23:49:04.1197362Z inflating: build/bin/reportMemoryUsage_test 2024-12-17T23:49:04.1247909Z inflating: build/bin/test_edge_op_registration 2024-12-17T23:49:04.1251904Z inflating: build/bin/torch_shm_manager 2024-12-17T23:49:04.1267301Z inflating: build/bin/tutorial_tensorexpr 2024-12-17T23:49:04.2257878Z inflating: build/bin/test_tensorexpr 2024-12-17T23:49:04.2819568Z inflating: build/bin/test_jit 2024-12-17T23:49:04.2819948Z creating: .additional_ci_files/ 2024-12-17T23:49:04.2906110Z inflating: .additional_ci_files/test-times.json 2024-12-17T23:49:04.3249798Z inflating: .additional_ci_files/test-class-times.json 2024-12-17T23:49:04.3275289Z ##[group]Run rm artifacts.zip 2024-12-17T23:49:04.3275614Z rm artifacts.zip 2024-12-17T23:49:04.3281282Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:04.3281704Z env: 2024-12-17T23:49:04.3281935Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:04.3282210Z ##[endgroup] 2024-12-17T23:49:04.4359411Z ##[group]Run df -H 2024-12-17T23:49:04.4359689Z df -H 2024-12-17T23:49:04.4368855Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:04.4369263Z env: 2024-12-17T23:49:04.4369497Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:04.4369759Z ##[endgroup] 2024-12-17T23:49:04.4674168Z Filesystem Size Used Avail Use% Mounted on 2024-12-17T23:49:04.4674823Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-12-17T23:49:04.4675180Z tmpfs 8.2G 132k 8.2G 1% /dev/shm 2024-12-17T23:49:04.4675520Z tmpfs 3.3G 488k 3.3G 1% /run 2024-12-17T23:49:04.4675970Z /dev/nvme0n1p1 161G 25G 137G 16% / 2024-12-17T23:49:04.4676470Z tmpfs 8.2G 33k 8.2G 1% /tmp 2024-12-17T23:49:04.4676845Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-12-17T23:49:04.4738283Z Prepare all required actions 2024-12-17T23:49:04.4738783Z Getting action download info 2024-12-17T23:49:04.6418797Z ##[group]Run ./.github/actions/download-td-artifacts 2024-12-17T23:49:04.6419167Z with: 2024-12-17T23:49:04.6419367Z env: 2024-12-17T23:49:04.6419587Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:04.6419857Z ##[endgroup] 2024-12-17T23:49:04.6511640Z ##[group]Run seemethere/download-artifact-s3@v4 2024-12-17T23:49:04.6511998Z with: 2024-12-17T23:49:04.6512215Z name: td_results 2024-12-17T23:49:04.6512468Z s3-bucket: gha-artifacts 2024-12-17T23:49:04.6512731Z region: us-east-1 2024-12-17T23:49:04.6512969Z env: 2024-12-17T23:49:04.6513190Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:04.6513458Z ##[endgroup] 2024-12-17T23:49:05.1011816Z (node:316767) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-12-17T23:49:05.1012352Z 2024-12-17T23:49:05.1012634Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-12-17T23:49:05.1013212Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-12-17T23:49:05.1013776Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-12-17T23:49:05.1885901Z Found 1 objects with prefix pytorch/pytorch/12383255652/td_results/ 2024-12-17T23:49:05.1886571Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-12-17T23:49:05.2310381Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-12-17T23:49:05.2315571Z Artifact download has finished successfully 2024-12-17T23:49:05.2511369Z ##[group]Run mkdir -p .additional_ci_files 2024-12-17T23:49:05.2511783Z mkdir -p .additional_ci_files 2024-12-17T23:49:05.2512253Z mv td_results.json .additional_ci_files/td_results.json || true 2024-12-17T23:49:05.2518105Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:05.2518503Z env: 2024-12-17T23:49:05.2518753Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:05.2519046Z ##[endgroup] 2024-12-17T23:49:05.2926371Z ##[group]Run .github/scripts/parse_ref.py 2024-12-17T23:49:05.2926775Z .github/scripts/parse_ref.py 2024-12-17T23:49:05.2932349Z shell: /usr/bin/bash -e {0} 2024-12-17T23:49:05.2932642Z env: 2024-12-17T23:49:05.2932871Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:05.2933133Z ##[endgroup] 2024-12-17T23:49:05.3306136Z Prepare all required actions 2024-12-17T23:49:05.3419112Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-12-17T23:49:05.3419479Z with: 2024-12-17T23:49:05.3419886Z github-token: *** 2024-12-17T23:49:05.3420136Z env: 2024-12-17T23:49:05.3420361Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:05.3420641Z ##[endgroup] 2024-12-17T23:49:05.3490487Z ##[group]Run set -eux 2024-12-17T23:49:05.3490766Z set -eux 2024-12-17T23:49:05.3491220Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-12-17T23:49:05.3496857Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:05.3497277Z env: 2024-12-17T23:49:05.3497505Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:05.3498006Z GITHUB_TOKEN: *** 2024-12-17T23:49:05.3498265Z ##[endgroup] 2024-12-17T23:49:05.3519467Z + python3 .github/scripts/get_workflow_job_id.py 12383255652 i-0c373a2e3f7bf6e7f 2024-12-17T23:49:07.1179704Z setting job-id=34566046822 2024-12-17T23:49:07.1180336Z setting job-name=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:07.1374412Z Prepare all required actions 2024-12-17T23:49:07.1374834Z Getting action download info 2024-12-17T23:49:07.2857047Z ##[group]Run ./.github/actions/filter-test-configs 2024-12-17T23:49:07.2857426Z with: 2024-12-17T23:49:07.2857853Z github-token: *** 2024-12-17T23:49:07.2859999Z 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"}]} 2024-12-17T23:49:07.2862594Z job-name: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:07.2863079Z env: 2024-12-17T23:49:07.2863295Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:07.2863569Z ##[endgroup] 2024-12-17T23:49:07.2938555Z ##[group]Run nick-fields/retry@v3.0.0 2024-12-17T23:49:07.2938876Z with: 2024-12-17T23:49:07.2939103Z shell: bash 2024-12-17T23:49:07.2939346Z timeout_minutes: 10 2024-12-17T23:49:07.2939611Z max_attempts: 5 2024-12-17T23:49:07.2939856Z retry_wait_seconds: 30 2024-12-17T23:49:07.2940674Z 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 2024-12-17T23:49:07.2941547Z polling_interval_seconds: 1 2024-12-17T23:49:07.2941849Z warning_on_retry: true 2024-12-17T23:49:07.2942127Z continue_on_error: false 2024-12-17T23:49:07.2942401Z env: 2024-12-17T23:49:07.2942608Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:07.2943140Z GITHUB_TOKEN: *** 2024-12-17T23:49:07.2943390Z ##[endgroup] 2024-12-17T23:49:07.3850810Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-12-17T23:49:07.6354730Z Defaulting to user installation because normal site-packages is not writeable 2024-12-17T23:49:07.6511912Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1) 2024-12-17T23:49:07.6516100Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1) 2024-12-17T23:49:07.6635627Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12) 2024-12-17T23:49:07.6644866Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-12-17T23:49:07.6648934Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-12-17T23:49:07.6654980Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2024.12.14) 2024-12-17T23:49:08.3687889Z Command completed after 1 attempt(s). 2024-12-17T23:49:08.3885993Z ##[group]Run set -x 2024-12-17T23:49:08.3886424Z set -x 2024-12-17T23:49:08.3886780Z  2024-12-17T23:49:08.3887429Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-12-17T23:49:08.3888278Z # in runner workspace 2024-12-17T23:49:08.3888952Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-12-17T23:49:08.3896129Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:08.3896537Z env: 2024-12-17T23:49:08.3896779Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:08.3897062Z ##[endgroup] 2024-12-17T23:49:08.3921866Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-12-17T23:49:08.4272479Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-12-17T23:49:08.4272905Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-12-17T23:49:08.4273261Z echo "Job name: ${JOB_NAME}" 2024-12-17T23:49:08.4273572Z  2024-12-17T23:49:08.4273952Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-12-17T23:49:08.4274444Z # in runner workspace 2024-12-17T23:49:08.4274883Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-12-17T23:49:08.4275481Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-12-17T23:49:08.4275835Z  --job-name "${JOB_NAME}" \ 2024-12-17T23:49:08.4278071Z  --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"}]}" \ 2024-12-17T23:49:08.4280323Z  --selected-test-configs "" \ 2024-12-17T23:49:08.4280681Z  --pr-number "${PR_NUMBER}" \ 2024-12-17T23:49:08.4281013Z  --tag "${TAG}" \ 2024-12-17T23:49:08.4281313Z  --event-name "${EVENT_NAME}" \ 2024-12-17T23:49:08.4281650Z  --schedule "${SCHEDULE}" \ 2024-12-17T23:49:08.4281976Z  --branch "${HEAD_BRANCH}" 2024-12-17T23:49:08.4287297Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:08.4287686Z env: 2024-12-17T23:49:08.4287911Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:08.4288383Z GITHUB_TOKEN: *** 2024-12-17T23:49:08.4288811Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:08.4289292Z PR_NUMBER: 2024-12-17T23:49:08.4289526Z TAG: 2024-12-17T23:49:08.4289745Z EVENT_NAME: push 2024-12-17T23:49:08.4289977Z SCHEDULE: 2024-12-17T23:49:08.4290202Z HEAD_BRANCH: 2024-12-17T23:49:08.4290435Z ##[endgroup] 2024-12-17T23:49:08.4311704Z Workflow: pull 2024-12-17T23:49:08.4312128Z Job name: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:08.8801646Z ##[group]Run echo "Filtered matrix:" 2024-12-17T23:49:08.8802038Z echo "Filtered matrix:" 2024-12-17T23:49:08.8804335Z 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"}]}" 2024-12-17T23:49:08.8806935Z  2024-12-17T23:49:08.8807268Z echo 2024-12-17T23:49:08.8807668Z echo "Is the current job unstable? False" 2024-12-17T23:49:08.8808029Z  2024-12-17T23:49:08.8808246Z echo 2024-12-17T23:49:08.8808526Z echo "Is keep-going label set? False" 2024-12-17T23:49:08.8808861Z  2024-12-17T23:49:08.8809076Z echo 2024-12-17T23:49:08.8809322Z echo "Renabled issues? " 2024-12-17T23:49:08.8815063Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:08.8815483Z env: 2024-12-17T23:49:08.8815719Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:08.8816145Z ##[endgroup] 2024-12-17T23:49:08.8838907Z Filtered matrix: 2024-12-17T23:49:08.8841794Z {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}]} 2024-12-17T23:49:08.8843972Z 2024-12-17T23:49:08.8844095Z Is the current job unstable? False 2024-12-17T23:49:08.8844314Z 2024-12-17T23:49:08.8844426Z Is keep-going label set? False 2024-12-17T23:49:08.8844617Z 2024-12-17T23:49:08.8844724Z Renabled issues? 2024-12-17T23:49:08.8959415Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:08.8959937Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:08.8965346Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:08.8965738Z env: 2024-12-17T23:49:08.8968034Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:08.8968319Z JOB_TIMEOUT: 600 2024-12-17T23:49:08.8968556Z ##[endgroup] 2024-12-17T23:49:08.9068794Z ##[group]Run set -x 2024-12-17T23:49:08.9069138Z set -x 2024-12-17T23:49:08.9069379Z  2024-12-17T23:49:08.9069647Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-12-17T23:49:08.9070071Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-12-17T23:49:08.9070473Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-12-17T23:49:08.9070853Z  TEST_COMMAND=.ci/onnx/test.sh 2024-12-17T23:49:08.9071173Z else 2024-12-17T23:49:08.9071443Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-12-17T23:49:08.9071769Z fi 2024-12-17T23:49:08.9071979Z  2024-12-17T23:49:08.9072336Z # detached container should get cleaned up by teardown_ec2_linux 2024-12-17T23:49:08.9072890Z # TODO: Stop building test binaries as part of the build phase 2024-12-17T23:49:08.9073376Z # Used for GPU_FLAG since that doesn't play nice 2024-12-17T23:49:08.9073814Z # shellcheck disable=SC2086,SC2090 2024-12-17T23:49:08.9074169Z container_name=$(docker run \ 2024-12-17T23:49:08.9074495Z  ${GPU_FLAG:-} \ 2024-12-17T23:49:08.9074811Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2024-12-17T23:49:08.9075179Z  -e BUILD_ENVIRONMENT \ 2024-12-17T23:49:08.9075481Z  -e PR_NUMBER \ 2024-12-17T23:49:08.9075769Z  -e GITHUB_ACTIONS \ 2024-12-17T23:49:08.9076077Z  -e GITHUB_REPOSITORY \ 2024-12-17T23:49:08.9076392Z  -e GITHUB_WORKFLOW \ 2024-12-17T23:49:08.9076699Z  -e GITHUB_JOB \ 2024-12-17T23:49:08.9076976Z  -e GITHUB_RUN_ID \ 2024-12-17T23:49:08.9077272Z  -e GITHUB_RUN_NUMBER \ 2024-12-17T23:49:08.9077588Z  -e GITHUB_RUN_ATTEMPT \ 2024-12-17T23:49:08.9077892Z  -e JOB_ID \ 2024-12-17T23:49:08.9078168Z  -e JOB_NAME \ 2024-12-17T23:49:08.9078443Z  -e BASE_SHA \ 2024-12-17T23:49:08.9078717Z  -e BRANCH \ 2024-12-17T23:49:08.9078991Z  -e SHA1 \ 2024-12-17T23:49:08.9079249Z  -e AWS_DEFAULT_REGION \ 2024-12-17T23:49:08.9079563Z  -e IN_WHEEL_TEST \ 2024-12-17T23:49:08.9079858Z  -e SHARD_NUMBER \ 2024-12-17T23:49:08.9080155Z  -e TEST_CONFIG \ 2024-12-17T23:49:08.9080451Z  -e NUM_TEST_SHARDS \ 2024-12-17T23:49:08.9080749Z  -e REENABLED_ISSUES \ 2024-12-17T23:49:08.9081071Z  -e CONTINUE_THROUGH_ERROR \ 2024-12-17T23:49:08.9081460Z  -e VERBOSE_TEST_LOGS \ 2024-12-17T23:49:08.9081784Z  -e TEST_SHOWLOCALS \ 2024-12-17T23:49:08.9082092Z  -e NO_TEST_TIMEOUT \ 2024-12-17T23:49:08.9082375Z  -e NO_TD \ 2024-12-17T23:49:08.9082647Z  -e TD_DISTRIBUTED \ 2024-12-17T23:49:08.9082949Z  -e PR_LABELS \ 2024-12-17T23:49:08.9083267Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-12-17T23:49:08.9083617Z  -e SCCACHE_BUCKET \ 2024-12-17T23:49:08.9083911Z  -e SCCACHE_REGION \ 2024-12-17T23:49:08.9084224Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-12-17T23:49:08.9084538Z  -e XLA_CUDA \ 2024-12-17T23:49:08.9084842Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-12-17T23:49:08.9085366Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-12-17T23:49:08.9085740Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-12-17T23:49:08.9086128Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-12-17T23:49:08.9086488Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-12-17T23:49:08.9086835Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2024-12-17T23:49:08.9087176Z  -e DASHBOARD_TAG \ 2024-12-17T23:49:08.9087470Z  -e IS_A100_RUNNER \ 2024-12-17T23:49:08.9087763Z  -e ARTIFACTS_FILE_SUFFIX \ 2024-12-17T23:49:08.9088144Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-12-17T23:49:08.9088558Z  --security-opt seccomp=unconfined \ 2024-12-17T23:49:08.9089015Z  --cap-add=SYS_PTRACE \ 2024-12-17T23:49:08.9089325Z  --ipc=host \ 2024-12-17T23:49:08.9089598Z  --shm-size="${SHM_SIZE}" \ 2024-12-17T23:49:08.9089907Z  --tty \ 2024-12-17T23:49:08.9090163Z  --detach \ 2024-12-17T23:49:08.9090442Z  --name="${container_name}" \ 2024-12-17T23:49:08.9090772Z  --user jenkins \ 2024-12-17T23:49:08.9091126Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-12-17T23:49:08.9091542Z  -w /var/lib/jenkins/workspace \ 2024-12-17T23:49:08.9091873Z  "${DOCKER_IMAGE}" 2024-12-17T23:49:08.9092147Z ) 2024-12-17T23:49:08.9092452Z # Propagate download.pytorch.org IP to container 2024-12-17T23:49:08.9093141Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-12-17T23:49:08.9093869Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-12-17T23:49:08.9094598Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-12-17T23:49:08.9099836Z shell: /usr/bin/bash -e {0} 2024-12-17T23:49:08.9100135Z env: 2024-12-17T23:49:08.9100365Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:08.9100694Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10 2024-12-17T23:49:08.9101035Z PR_NUMBER: 2024-12-17T23:49:08.9101293Z GITHUB_REPOSITORY: pytorch/pytorch 2024-12-17T23:49:08.9101614Z GITHUB_WORKFLOW: pull 2024-12-17T23:49:08.9101882Z GITHUB_JOB: test 2024-12-17T23:49:08.9102132Z GITHUB_RUN_ID: 12383255652 2024-12-17T23:49:08.9102403Z GITHUB_RUN_NUMBER: 276594 2024-12-17T23:49:08.9102684Z GITHUB_RUN_ATTEMPT: 1 2024-12-17T23:49:08.9102946Z JOB_ID: 34566046822 2024-12-17T23:49:08.9103365Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:08.9103846Z BRANCH: release/2.6 2024-12-17T23:49:08.9104130Z SHA1: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:08.9104530Z BASE_SHA: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:08.9104893Z TEST_CONFIG: dynamo_wrapped 2024-12-17T23:49:08.9105179Z SHARD_NUMBER: 1 2024-12-17T23:49:08.9105428Z NUM_TEST_SHARDS: 3 2024-12-17T23:49:08.9105670Z REENABLED_ISSUES: 2024-12-17T23:49:08.9105934Z CONTINUE_THROUGH_ERROR: False 2024-12-17T23:49:08.9106231Z VERBOSE_TEST_LOGS: False 2024-12-17T23:49:08.9106510Z TEST_SHOWLOCALS: False 2024-12-17T23:49:08.9106782Z NO_TEST_TIMEOUT: False 2024-12-17T23:49:08.9107034Z NO_TD: False 2024-12-17T23:49:08.9107272Z TD_DISTRIBUTED: False 2024-12-17T23:49:08.9107594Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-12-17T23:49:08.9107967Z SCCACHE_REGION: us-east-1 2024-12-17T23:49:08.9108255Z SCCACHE_S3_KEY_PREFIX: pull 2024-12-17T23:49:08.9108623Z SHM_SIZE: 1g 2024-12-17T23:49:08.9109294Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:08.9110040Z XLA_CUDA: 2024-12-17T23:49:08.9110416Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:49:08.9110887Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-12-17T23:49:08.9111319Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-12-17T23:49:08.9111624Z DASHBOARD_TAG: 2024-12-17T23:49:08.9112070Z HUGGING_FACE_HUB_TOKEN: *** 2024-12-17T23:49:08.9112497Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2024-12-17T23:49:08.9112808Z IS_A100_RUNNER: 0 2024-12-17T23:49:08.9113196Z ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-17T23:49:08.9113651Z ##[endgroup] 2024-12-17T23:49:08.9134812Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]] 2024-12-17T23:49:08.9135183Z + [[ linux-focal-py3.12-clang10 == *onnx* ]] 2024-12-17T23:49:08.9135535Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-12-17T23:49:08.9142902Z +++ nproc --ignore=2 2024-12-17T23:49:08.9215214Z ++ 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=6 -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-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:15.6449296Z + container_name=d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-17T23:49:15.6451131Z + grep download.pytorch.org /etc/hosts 2024-12-17T23:49:15.6452967Z + docker exec -i d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 sudo bash -c '/bin/cat >> /etc/hosts' 2024-12-17T23:49:15.7536989Z + echo DOCKER_CONTAINER_ID=d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-17T23:49:15.7540386Z ++ echo dist/torch-2.6.0a0+git0cdf8b1-cp312-cp312-linux_x86_64.whl 2024-12-17T23:49:15.7542600Z + docker exec -t d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 sh -c 'python3 -m pip install dist/torch-2.6.0a0+git0cdf8b1-cp312-cp312-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-12-17T23:49:16.4570798Z Processing ./dist/torch-2.6.0a0+git0cdf8b1-cp312-cp312-linux_x86_64.whl (from torch==2.6.0a0+git0cdf8b1) 2024-12-17T23:49:16.8915264Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.16.1) 2024-12-17T23:49:16.8917840Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (4.12.2) 2024-12-17T23:49:16.8920991Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2.8.8) 2024-12-17T23:49:16.8924195Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.1.4) 2024-12-17T23:49:16.8927792Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2024.10.0) 2024-12-17T23:49:16.8939892Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (75.1.0) 2024-12-17T23:49:16.8944823Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.13.1) 2024-12-17T23:49:16.8959669Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.3.0) 2024-12-17T23:49:16.8976019Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.3.0) 2024-12-17T23:49:16.8997260Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from opt-einsum>=3.3->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.26.2) 2024-12-17T23:49:16.9105381Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.0.2) 2024-12-17T23:49:17.1628152Z Installing collected packages: torch 2024-12-17T23:49:26.8849743Z Successfully installed torch-2.6.0a0+git0cdf8b1 2024-12-17T23:49:26.9593348Z + export TERM=vt100 2024-12-17T23:49:26.9593832Z + TERM=vt100 2024-12-17T23:49:26.9596089Z ++ dirname .ci/pytorch/test.sh 2024-12-17T23:49:26.9616512Z + source .ci/pytorch/common.sh 2024-12-17T23:49:26.9626742Z +++ dirname .ci/pytorch/common.sh 2024-12-17T23:49:26.9633117Z ++ source .ci/pytorch/common_utils.sh 2024-12-17T23:49:26.9641819Z +++ declare -f -t trap_add 2024-12-17T23:49:26.9647330Z ++ set -ex 2024-12-17T23:49:26.9647723Z ++ [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-12-17T23:49:26.9648119Z ++ BUILD_TEST_LIBTORCH=0 2024-12-17T23:49:26.9648533Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-12-17T23:49:26.9648879Z + [[ -d /var/lib/jenkins/workspace ]] 2024-12-17T23:49:26.9651464Z ++ stat -c %u /var/lib/jenkins/workspace 2024-12-17T23:49:26.9680998Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-12-17T23:49:26.9681372Z + trap_add cleanup_workspace EXIT 2024-12-17T23:49:26.9681702Z + trap_add_cmd=cleanup_workspace 2024-12-17T23:49:26.9682013Z + shift 2024-12-17T23:49:26.9682230Z + for trap_add_name in "$@" 2024-12-17T23:49:26.9687766Z +++ trap -p EXIT 2024-12-17T23:49:26.9690302Z ++ eval 'extract_trap_cmd ' 2024-12-17T23:49:26.9690777Z +++ extract_trap_cmd 2024-12-17T23:49:26.9691035Z +++ printf '%s\n' '' 2024-12-17T23:49:26.9691297Z ++ printf '%s\n' cleanup_workspace 2024-12-17T23:49:26.9692918Z + trap -- ' 2024-12-17T23:49:26.9693273Z cleanup_workspace' EXIT 2024-12-17T23:49:26.9693613Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-12-17T23:49:27.4207433Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-12-17T23:49:27.4222686Z + echo 'Environment variables:' 2024-12-17T23:49:27.4223054Z Environment variables: 2024-12-17T23:49:27.4223319Z + env 2024-12-17T23:49:27.4242849Z INSTALLED_DB=yes 2024-12-17T23:49:27.4243523Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:49:27.4244293Z CONTINUE_THROUGH_ERROR=False 2024-12-17T23:49:27.4244866Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-12-17T23:49:27.4245445Z HOSTNAME=d5aa3dabf956 2024-12-17T23:49:27.4246444Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4247520Z GITHUB_ACTION=__self 2024-12-17T23:49:27.4247926Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-12-17T23:49:27.4248492Z GITHUB_RUN_NUMBER=276594 2024-12-17T23:49:27.4248907Z TEST_CONFIG=dynamo_wrapped 2024-12-17T23:49:27.4249220Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-12-17T23:49:27.4249627Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-12-17T23:49:27.4249989Z IS_A100_RUNNER=0 2024-12-17T23:49:27.4250479Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2024-12-17T23:49:27.4250794Z GITHUB_TRIGGERING_ACTOR=malfet 2024-12-17T23:49:27.4251107Z GITHUB_REF_TYPE=branch 2024-12-17T23:49:27.4251365Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-12-17T23:49:27.4251691Z BASE_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4252035Z XLA_CUDA= 2024-12-17T23:49:27.4252584Z HUGGING_FACE_HUB_TOKEN=*** 2024-12-17T23:49:27.4254005Z *** 2024-12-17T23:49:27.4254241Z GITHUB_REPOSITORY_ID=65600975 2024-12-17T23:49:27.4254525Z GITHUB_ACTIONS=true 2024-12-17T23:49:27.4254812Z SHA1=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4255202Z GITHUB_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4255759Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/release/2.6 2024-12-17T23:49:27.4256277Z UCC_HOME=/usr 2024-12-17T23:49:27.4256508Z VERBOSE_TEST_LOGS=False 2024-12-17T23:49:27.4256793Z GITHUB_REF=refs/heads/release/2.6 2024-12-17T23:49:27.4257093Z SHARD_NUMBER=1 2024-12-17T23:49:27.4257337Z GITHUB_REF_PROTECTED=true 2024-12-17T23:49:27.4257617Z HOME=/var/lib/jenkins 2024-12-17T23:49:27.4258088Z GITHUB_API_URL=https://api.github.com 2024-12-17T23:49:27.4258439Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-12-17T23:49:27.4258746Z UCX_COMMIT= 2024-12-17T23:49:27.4258986Z SCCACHE_S3_KEY_PREFIX=pull 2024-12-17T23:49:27.4259275Z NUM_TEST_SHARDS=3 2024-12-17T23:49:27.4259541Z UCX_HOME=/usr 2024-12-17T23:49:27.4260107Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4260930Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:27.4261726Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4262554Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-12-17T23:49:27.4263077Z GITHUB_EVENT_NAME=push 2024-12-17T23:49:27.4263331Z DASHBOARD_TAG= 2024-12-17T23:49:27.4263580Z GITHUB_RUN_ID=12383255652 2024-12-17T23:49:27.4264239Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4264935Z GITHUB_ACTOR=malfet 2024-12-17T23:49:27.4265186Z PR_NUMBER= 2024-12-17T23:49:27.4265420Z DESIRED_CUDA= 2024-12-17T23:49:27.4265652Z GITHUB_RUN_ATTEMPT=1 2024-12-17T23:49:27.4265928Z ANACONDA_PYTHON_VERSION=3.12 2024-12-17T23:49:27.4266309Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-12-17T23:49:27.4266680Z TERM=vt100 2024-12-17T23:49:27.4266897Z INSTALLED_VISION=yes 2024-12-17T23:49:27.4267158Z BRANCH=release/2.6 2024-12-17T23:49:27.4267419Z SCCACHE_REGION=us-east-1 2024-12-17T23:49:27.4267703Z OPENSSL_ROOT_DIR=/opt/openssl 2024-12-17T23:49:27.4268002Z CUDA_PATH=/usr/local/cuda 2024-12-17T23:49:27.4268633Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-12-17T23:49:27.4269231Z GITHUB_SERVER_URL=https://github.com 2024-12-17T23:49:27.4269547Z UCC_COMMIT= 2024-12-17T23:49:27.4269777Z REENABLED_ISSUES= 2024-12-17T23:49:27.4270013Z DOCS= 2024-12-17T23:49:27.4270212Z SHLVL=1 2024-12-17T23:49:27.4270421Z MAX_JOBS=6 2024-12-17T23:49:27.4270644Z GITHUB_ACTOR_ID=2453524 2024-12-17T23:49:27.4270999Z GITHUB_WORKFLOW_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4271413Z GITHUB_REF_NAME=release/2.6 2024-12-17T23:49:27.4271821Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:49:27.4272261Z GITHUB_JOB=test 2024-12-17T23:49:27.4272506Z NO_TEST_TIMEOUT=False 2024-12-17T23:49:27.4272765Z TD_DISTRIBUTED=False 2024-12-17T23:49:27.4273044Z GITHUB_REPOSITORY=pytorch/pytorch 2024-12-17T23:49:27.4273347Z GITHUB_RETENTION_DAYS=90 2024-12-17T23:49:27.4273622Z OPENSSL_DIR=/opt/openssl 2024-12-17T23:49:27.4273901Z GITHUB_ACTION_REPOSITORY= 2024-12-17T23:49:27.4274706Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:49:27.4275531Z GITHUB_BASE_REF= 2024-12-17T23:49:27.4275765Z INSTALLED_ACL= 2024-12-17T23:49:27.4276141Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-17T23:49:27.4276582Z CI=true 2024-12-17T23:49:27.4276815Z GITHUB_REPOSITORY_OWNER=pytorch 2024-12-17T23:49:27.4277207Z JOB_ID=34566046822 2024-12-17T23:49:27.4277446Z INSTALLED_PROTOBUF=yes 2024-12-17T23:49:27.4277715Z GITHUB_HEAD_REF= 2024-12-17T23:49:27.4277961Z GITHUB_ACTION_REF= 2024-12-17T23:49:27.4278271Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-12-17T23:49:27.4278642Z TEST_SHOWLOCALS=False 2024-12-17T23:49:27.4278896Z GITHUB_WORKFLOW=pull 2024-12-17T23:49:27.4279168Z DEBIAN_FRONTEND=noninteractive 2024-12-17T23:49:27.4279815Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4280466Z NO_TD=False 2024-12-17T23:49:27.4280707Z SKIP_SCCACHE_INITIALIZATION=1 2024-12-17T23:49:27.4280982Z _=/usr/bin/env 2024-12-17T23:49:27.4281384Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-12-17T23:49:27.4378092Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-12-17T23:49:27.4378828Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-12-17T23:49:27.4379613Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-12-17T23:49:27.4380514Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-12-17T23:49:27.4380978Z + BUILD_DIR=build 2024-12-17T23:49:27.4381282Z + BUILD_RENAMED_DIR=build_renamed 2024-12-17T23:49:27.4381590Z + BUILD_BIN_DIR=build/bin 2024-12-17T23:49:27.4381916Z + SHARD_NUMBER=1 2024-12-17T23:49:27.4382161Z + NUM_TEST_SHARDS=3 2024-12-17T23:49:27.4382397Z + export VALGRIND=ON 2024-12-17T23:49:27.4382702Z + VALGRIND=ON 2024-12-17T23:49:27.4382971Z + [[ linux-focal-py3.12-clang10 == *clang9* ]] 2024-12-17T23:49:27.4383390Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-12-17T23:49:27.4383809Z + [[ 0 == \1 ]] 2024-12-17T23:49:27.4384092Z + [[ False == \1 ]] 2024-12-17T23:49:27.4384456Z + [[ linux-focal-py3.12-clang10 != *bazel* ]] 2024-12-17T23:49:27.4384882Z ++ realpath build/custom_test_artifacts 2024-12-17T23:49:27.4407863Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-12-17T23:49:27.4408520Z + [[ -n '' ]] 2024-12-17T23:49:27.4408759Z + echo 'Environment variables' 2024-12-17T23:49:27.4409057Z Environment variables 2024-12-17T23:49:27.4409307Z + env 2024-12-17T23:49:27.4415178Z INSTALLED_DB=yes 2024-12-17T23:49:27.4415871Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:49:27.4416605Z CONTINUE_THROUGH_ERROR=False 2024-12-17T23:49:27.4417020Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-12-17T23:49:27.4417415Z HOSTNAME=d5aa3dabf956 2024-12-17T23:49:27.4418166Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4418877Z GITHUB_ACTION=__self 2024-12-17T23:49:27.4419168Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-12-17T23:49:27.4419469Z GITHUB_RUN_NUMBER=276594 2024-12-17T23:49:27.4419748Z TEST_CONFIG=dynamo_wrapped 2024-12-17T23:49:27.4420042Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-12-17T23:49:27.4420391Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-12-17T23:49:27.4420731Z IS_A100_RUNNER=0 2024-12-17T23:49:27.4421205Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2024-12-17T23:49:27.4421504Z GITHUB_TRIGGERING_ACTOR=malfet 2024-12-17T23:49:27.4421803Z GITHUB_REF_TYPE=branch 2024-12-17T23:49:27.4422076Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-12-17T23:49:27.4422409Z BASE_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4422755Z XLA_CUDA= 2024-12-17T23:49:27.4423080Z HUGGING_FACE_HUB_TOKEN=*** 2024-12-17T23:49:27.4423551Z *** 2024-12-17T23:49:27.4423782Z GITHUB_REPOSITORY_ID=65600975 2024-12-17T23:49:27.4424077Z GITHUB_ACTIONS=true 2024-12-17T23:49:27.4424367Z SHA1=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4424743Z GITHUB_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4425311Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/release/2.6 2024-12-17T23:49:27.4425822Z UCC_HOME=/usr 2024-12-17T23:49:27.4426063Z VERBOSE_TEST_LOGS=False 2024-12-17T23:49:27.4426524Z GITHUB_REF=refs/heads/release/2.6 2024-12-17T23:49:27.4426810Z SHARD_NUMBER=1 2024-12-17T23:49:27.4427059Z GITHUB_REF_PROTECTED=true 2024-12-17T23:49:27.4427339Z HOME=/var/lib/jenkins 2024-12-17T23:49:27.4427629Z GITHUB_API_URL=https://api.github.com 2024-12-17T23:49:27.4427967Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-12-17T23:49:27.4428259Z UCX_COMMIT= 2024-12-17T23:49:27.4428570Z SCCACHE_S3_KEY_PREFIX=pull 2024-12-17T23:49:27.4428847Z NUM_TEST_SHARDS=3 2024-12-17T23:49:27.4429085Z UCX_HOME=/usr 2024-12-17T23:49:27.4429662Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4430465Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:27.4431350Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4432172Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-12-17T23:49:27.4432695Z GITHUB_EVENT_NAME=push 2024-12-17T23:49:27.4432957Z DASHBOARD_TAG= 2024-12-17T23:49:27.4433199Z GITHUB_RUN_ID=12383255652 2024-12-17T23:49:27.4433837Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4434531Z GITHUB_ACTOR=malfet 2024-12-17T23:49:27.4434775Z PR_NUMBER= 2024-12-17T23:49:27.4434999Z DESIRED_CUDA= 2024-12-17T23:49:27.4435233Z GITHUB_RUN_ATTEMPT=1 2024-12-17T23:49:27.4435475Z VALGRIND=ON 2024-12-17T23:49:27.4435713Z ANACONDA_PYTHON_VERSION=3.12 2024-12-17T23:49:27.4436250Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-12-17T23:49:27.4436620Z TERM=vt100 2024-12-17T23:49:27.4436840Z INSTALLED_VISION=yes 2024-12-17T23:49:27.4437103Z BRANCH=release/2.6 2024-12-17T23:49:27.4437358Z SCCACHE_REGION=us-east-1 2024-12-17T23:49:27.4437642Z OPENSSL_ROOT_DIR=/opt/openssl 2024-12-17T23:49:27.4437956Z CUDA_PATH=/usr/local/cuda 2024-12-17T23:49:27.4438495Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-12-17T23:49:27.4439082Z GITHUB_SERVER_URL=https://github.com 2024-12-17T23:49:27.4439400Z UCC_COMMIT= 2024-12-17T23:49:27.4439630Z REENABLED_ISSUES= 2024-12-17T23:49:27.4439864Z DOCS= 2024-12-17T23:49:27.4440068Z SHLVL=1 2024-12-17T23:49:27.4440264Z MAX_JOBS=6 2024-12-17T23:49:27.4440488Z GITHUB_ACTOR_ID=2453524 2024-12-17T23:49:27.4440838Z GITHUB_WORKFLOW_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:49:27.4441239Z GITHUB_REF_NAME=release/2.6 2024-12-17T23:49:27.4441649Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:49:27.4442077Z GITHUB_JOB=test 2024-12-17T23:49:27.4442317Z NO_TEST_TIMEOUT=False 2024-12-17T23:49:27.4442580Z TD_DISTRIBUTED=False 2024-12-17T23:49:27.4442854Z GITHUB_REPOSITORY=pytorch/pytorch 2024-12-17T23:49:27.4443166Z GITHUB_RETENTION_DAYS=90 2024-12-17T23:49:27.4443430Z OPENSSL_DIR=/opt/openssl 2024-12-17T23:49:27.4443712Z GITHUB_ACTION_REPOSITORY= 2024-12-17T23:49:27.4444507Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:49:27.4445325Z GITHUB_BASE_REF= 2024-12-17T23:49:27.4445565Z INSTALLED_ACL= 2024-12-17T23:49:27.4445928Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-17T23:49:27.4446365Z CI=true 2024-12-17T23:49:27.4446594Z GITHUB_REPOSITORY_OWNER=pytorch 2024-12-17T23:49:27.4446883Z JOB_ID=34566046822 2024-12-17T23:49:27.4447127Z INSTALLED_PROTOBUF=yes 2024-12-17T23:49:27.4447374Z GITHUB_HEAD_REF= 2024-12-17T23:49:27.4447616Z GITHUB_ACTION_REF= 2024-12-17T23:49:27.4447916Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-12-17T23:49:27.4448285Z TEST_SHOWLOCALS=False 2024-12-17T23:49:27.4448535Z GITHUB_WORKFLOW=pull 2024-12-17T23:49:27.4448806Z DEBIAN_FRONTEND=noninteractive 2024-12-17T23:49:27.4449446Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_7b4293db-0822-4b6f-a568-d0b5841cbfbb 2024-12-17T23:49:27.4450235Z NO_TD=False 2024-12-17T23:49:27.4450481Z SKIP_SCCACHE_INITIALIZATION=1 2024-12-17T23:49:27.4450770Z _=/usr/bin/env 2024-12-17T23:49:27.4450997Z + echo 'Testing pytorch' 2024-12-17T23:49:27.4451268Z Testing pytorch 2024-12-17T23:49:27.4462368Z + export LANG=C.UTF-8 2024-12-17T23:49:27.4462703Z + LANG=C.UTF-8 2024-12-17T23:49:27.4502481Z + PR_NUMBER= 2024-12-17T23:49:27.4502869Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]] 2024-12-17T23:49:27.4503446Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-12-17T23:49:27.4503961Z + [[ dynamo_wrapped == \s\l\o\w ]] 2024-12-17T23:49:27.4504510Z + [[ linux-focal-py3.12-clang10 == *slow-gradcheck* ]] 2024-12-17T23:49:27.4505245Z + [[ linux-focal-py3.12-clang10 == *cuda* ]] 2024-12-17T23:49:27.4505744Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-12-17T23:49:27.4506112Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-12-17T23:49:27.4506463Z + [[ dynamo_wrapped == *crossref* ]] 2024-12-17T23:49:27.4506813Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-12-17T23:49:27.4507155Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-12-17T23:49:27.4507526Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-12-17T23:49:27.4507888Z + pip_install --user ninja==1.10.2 2024-12-17T23:49:27.4508296Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:49:27.4508879Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2024-12-17T23:49:27.8805447Z Collecting ninja==1.10.2 2024-12-17T23:49:27.8965746Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-12-17T23:49:27.9047100Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-12-17T23:49:28.0772545Z Installing collected packages: ninja 2024-12-17T23:49:28.0850179Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-12-17T23:49:28.0851205Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-12-17T23:49:28.0885520Z Successfully installed ninja-1.10.2 2024-12-17T23:49:28.1609204Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:49:28.1610838Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:49:28.1611832Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-12-17T23:49:28.1612186Z + install_tlparse 2024-12-17T23:49:28.1612481Z + pip_install --user tlparse==0.3.25 2024-12-17T23:49:28.1612897Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:49:28.1613421Z + python3 -m pip install --progress-bar off --user tlparse==0.3.25 2024-12-17T23:49:28.5394257Z Collecting tlparse==0.3.25 2024-12-17T23:49:28.5563284Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB) 2024-12-17T23:49:28.5665810Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-12-17T23:49:28.7577618Z Installing collected packages: tlparse 2024-12-17T23:49:28.7910646Z Successfully installed tlparse-0.3.25 2024-12-17T23:49:28.8594585Z ++ python -m site --user-base 2024-12-17T23:49:28.8732691Z + 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_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:49:28.8734015Z + [[ linux-focal-py3.12-clang10 == *asan* ]] 2024-12-17T23:49:28.8734755Z + [[ linux-focal-py3.12-clang10 == *-debug* ]] 2024-12-17T23:49:28.8735169Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-12-17T23:49:28.8735983Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass' 2024-12-17T23:49:28.8736806Z We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass 2024-12-17T23:49:28.8739013Z + cd test 2024-12-17T23:49:28.8739649Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-12-17T23:49:30.6580572Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-12-17T23:49:30.6581037Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-12-17T23:49:30.6585210Z + DYNAMO_BENCHMARK_FLAGS=() 2024-12-17T23:49:30.6586537Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]] 2024-12-17T23:49:30.6587050Z + [[ dynamo_wrapped == *dynamo_eager* ]] 2024-12-17T23:49:30.6587429Z + [[ dynamo_wrapped == *aot_eager* ]] 2024-12-17T23:49:30.6588086Z + [[ dynamo_wrapped == *aot_inductor* ]] 2024-12-17T23:49:30.6588497Z + [[ dynamo_wrapped == *inductor* ]] 2024-12-17T23:49:30.6588830Z + [[ dynamo_wrapped == *dynamic* ]] 2024-12-17T23:49:30.6589135Z + [[ dynamo_wrapped == *cpu* ]] 2024-12-17T23:49:30.6589500Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-12-17T23:49:30.6618562Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-12-17T23:49:30.6618966Z + [[ linux-focal-py3.12-clang10 == *-bazel-* ]] 2024-12-17T23:49:30.6621564Z + cd test 2024-12-17T23:49:30.6622225Z + python -c 'import torch; print(torch.__config__.show())' 2024-12-17T23:49:31.8998415Z PyTorch built with: 2024-12-17T23:49:31.8998792Z - GCC 4.2 2024-12-17T23:49:31.8999032Z - C++ Version: 201703 2024-12-17T23:49:31.8999304Z - clang 10.0.0 2024-12-17T23:49:31.8999858Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-12-17T23:49:31.9000609Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2024-12-17T23:49:31.9001101Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-12-17T23:49:31.9001460Z - LAPACK is enabled (usually provided by MKL) 2024-12-17T23:49:31.9001812Z - NNPACK is enabled 2024-12-17T23:49:31.9002091Z - CPU capability usage: AVX512 2024-12-17T23:49:31.9007683Z - 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, 2024-12-17T23:49:31.9013321Z 2024-12-17T23:49:32.1475583Z + cd test 2024-12-17T23:49:32.1476120Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-12-17T23:49:33.4131303Z ATen/Parallel: 2024-12-17T23:49:33.4131679Z at::get_num_threads() : 4 2024-12-17T23:49:33.4132002Z at::get_num_interop_threads() : 4 2024-12-17T23:49:33.4132316Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-12-17T23:49:33.4132654Z omp_get_max_threads() : 4 2024-12-17T23:49:33.4133241Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-12-17T23:49:33.4133863Z mkl_get_max_threads() : 4 2024-12-17T23:49:33.4134575Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2024-12-17T23:49:33.4135031Z std::thread::hardware_concurrency() : 8 2024-12-17T23:49:33.4135371Z Environment variables: 2024-12-17T23:49:33.4135650Z OMP_NUM_THREADS : [not set] 2024-12-17T23:49:33.4135944Z MKL_NUM_THREADS : [not set] 2024-12-17T23:49:33.4136417Z ATen parallel backend: OpenMP 2024-12-17T23:49:33.4136614Z 2024-12-17T23:49:33.6743606Z + [[ dynamo_wrapped == *numpy_2* ]] 2024-12-17T23:49:33.6744046Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-12-17T23:49:33.6744423Z + [[ dynamo_wrapped == *backward* ]] 2024-12-17T23:49:33.6744747Z + [[ dynamo_wrapped == *xla* ]] 2024-12-17T23:49:33.6745046Z + [[ dynamo_wrapped == *executorch* ]] 2024-12-17T23:49:33.6745692Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]] 2024-12-17T23:49:33.6746078Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-12-17T23:49:33.6746451Z + [[ dynamo_wrapped == distributed ]] 2024-12-17T23:49:33.6746823Z + [[ dynamo_wrapped == *inductor_distributed* ]] 2024-12-17T23:49:33.6747293Z + [[ dynamo_wrapped == *inductor-halide* ]] 2024-12-17T23:49:33.6747766Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]] 2024-12-17T23:49:33.6748171Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]] 2024-12-17T23:49:33.6748632Z + [[ dynamo_wrapped == *huggingface* ]] 2024-12-17T23:49:33.6748980Z + [[ dynamo_wrapped == *timm* ]] 2024-12-17T23:49:33.6749294Z + [[ dynamo_wrapped == *torchbench* ]] 2024-12-17T23:49:33.6749638Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]] 2024-12-17T23:49:33.6749998Z + [[ dynamo_wrapped == *inductor* ]] 2024-12-17T23:49:33.6750331Z + [[ dynamo_wrapped == *dynamo_wrapped* ]] 2024-12-17T23:49:33.6750661Z + install_torchvision 2024-12-17T23:49:33.6750924Z + local orig_preload 2024-12-17T23:49:33.6751177Z + local commit 2024-12-17T23:49:33.6751425Z ++ get_pinned_commit vision 2024-12-17T23:49:33.6751729Z ++ cat .github/ci_commit_pins/vision.txt 2024-12-17T23:49:33.6779085Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:33.6779720Z + orig_preload= 2024-12-17T23:49:33.6780151Z + '[' -n '' ']' 2024-12-17T23:49:33.6780818Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:33.6781546Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:49:33.6782375Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:34.0184864Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:34.0189681Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-7x_vtk8x 2024-12-17T23:49:34.0209316Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-7x_vtk8x 2024-12-17T23:49:35.4168374Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-12-17T23:49:35.4187627Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:36.7865858Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:37.0909891Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:49:39.3779367Z Preparing metadata (setup.py) ... [?25l- \ done 2024-12-17T23:49:39.3811145Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.2) 2024-12-17T23:49:39.3814671Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.6.0a0+git0cdf8b1) 2024-12-17T23:49:39.3819130Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0) 2024-12-17T23:49:39.3889260Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1) 2024-12-17T23:49:39.3892982Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-12-17T23:49:39.3896022Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2024-12-17T23:49:39.3899045Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-12-17T23:49:39.3903195Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0) 2024-12-17T23:49:39.3914208Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.1.0) 2024-12-17T23:49:39.3919228Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2024-12-17T23:49:39.3932451Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2024-12-17T23:49:39.4042643Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2) 2024-12-17T23:49:39.4142017Z Building wheels for collected packages: torchvision 2024-12-17T23:50:43.3228807Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2024-12-17T23:50:43.3264844Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1117896 sha256=e7c64cc9044b2d28371dc956455ed1c78e63c106594fabdd0f1414091f7cdc4f 2024-12-17T23:50:43.3267106Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-12-17T23:50:43.3302815Z Successfully built torchvision 2024-12-17T23:50:43.4682138Z Installing collected packages: torchvision 2024-12-17T23:50:43.9162938Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-12-17T23:50:44.0144391Z + '[' -n '' ']' 2024-12-17T23:50:44.0144793Z + test_dynamo_wrapped_shard 1 2024-12-17T23:50:44.0145101Z + [[ -z 3 ]] 2024-12-17T23:50:44.0145367Z + python tools/dynamo/verify_dynamo.py 2024-12-17T23:50:45.2644382Z Python version: 3.12.7 2024-12-17T23:50:45.2644761Z `torch` version: 2.6.0a0+git0cdf8b1 2024-12-17T23:50:45.2645083Z CUDA version: None 2024-12-17T23:50:45.2645338Z ROCM version: None 2024-12-17T23:50:45.2645504Z 2024-12-17T23:50:46.6133381Z CUDA not available -- skipping CUDA check on eager backend 2024-12-17T23:50:46.6133709Z 2024-12-17T23:50:47.7510435Z CUDA not available -- skipping CUDA check on aot_eager backend 2024-12-17T23:50:47.7511006Z 2024-12-17T23:50:56.9005307Z CUDA not available -- skipping CUDA check on inductor backend 2024-12-17T23:50:56.9005670Z 2024-12-17T23:50:56.9005783Z All required checks passed 2024-12-17T23:50:57.5555280Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 1 3 --verbose --upload-artifacts-while-running 2024-12-17T23:50:57.6590102Z /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 2024-12-17T23:50:57.6590994Z import pkg_resources 2024-12-17T23:51:01.6693514Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-17T23:51:01.7179246Z Ignoring disabled issues: [''] 2024-12-17T23:51:01.7346215Z Found test times from artifacts 2024-12-17T23:51:01.7980965Z Found test times from artifacts 2024-12-17T23:51:01.8000160Z Running all tests 2024-12-17T23:51:01.8127895Z Running parallel tests on 3 processes 2024-12-17T23:51:01.8131461Z Name: tests to run (est. time: 80.24min) 2024-12-17T23:51:01.8131911Z Serial tests (30): 2024-12-17T23:51:01.8132184Z test_reductions 1/3 2024-12-17T23:51:01.8132544Z test_reductions 3/3 2024-12-17T23:51:01.8132969Z test_cuda_nvml_based_avail 1/1 2024-12-17T23:51:01.8133376Z test_cuda_primary_ctx 1/1 2024-12-17T23:51:01.8133687Z test_cpp_extensions_aot_ninja 1/1 2024-12-17T23:51:01.8134487Z test_spectral_ops 1/1 2024-12-17T23:51:01.8134790Z test_cpp_extensions_aot_no_ninja 1/1 2024-12-17T23:51:01.8135128Z test_show_pickle 1/1 2024-12-17T23:51:01.8135422Z test_namedtuple_return_api 1/1 2024-12-17T23:51:01.8135755Z test_jit_disabled 1/1 2024-12-17T23:51:01.8136019Z test_autocast 1/1 2024-12-17T23:51:01.8136501Z test_tensorexpr 1/1 2024-12-17T23:51:01.8136771Z test_fake_tensor 1/1 2024-12-17T23:51:01.8137062Z test_fx 1/1 2024-12-17T23:51:01.8137314Z test_multiprocessing 1/1 2024-12-17T23:51:01.8137604Z test_native_mha 1/1 2024-12-17T23:51:01.8137861Z test_sort_and_select 1/1 2024-12-17T23:51:01.8138149Z nn/test_pooling 1/1 2024-12-17T23:51:01.8138422Z test_python_dispatch 1/1 2024-12-17T23:51:01.8138716Z test_mobile_optimizer 1/1 2024-12-17T23:51:01.8139012Z nn/test_convolution 1/1 2024-12-17T23:51:01.8139273Z test_nn 1/2 2024-12-17T23:51:01.8139509Z test_nn 2/2 2024-12-17T23:51:01.8139769Z test_multiprocessing_spawn 1/1 2024-12-17T23:51:01.8140087Z test_overrides 1/1 2024-12-17T23:51:01.8140376Z distributions/test_distributions 1/2 2024-12-17T23:51:01.8140719Z distributions/test_distributions 2/2 2024-12-17T23:51:01.8141063Z test_autoload_disable 1/1 2024-12-17T23:51:01.8141348Z doctests 1/1 2024-12-17T23:51:01.8141596Z test_autoload_enable 1/1 2024-12-17T23:51:01.8141870Z Parallel tests (29): 2024-12-17T23:51:01.8142155Z test_cuda_expandable_segments 1/1 2024-12-17T23:51:01.8142491Z dynamo/test_higher_order_ops 1/1 2024-12-17T23:51:01.8142805Z dynamo/test_misc 1/1 2024-12-17T23:51:01.8143080Z dynamo/test_frame_init 1/1 2024-12-17T23:51:01.8143359Z dynamo/test_nops 1/1 2024-12-17T23:51:01.8143644Z dynamo/test_fx_passes_pre_grad 1/1 2024-12-17T23:51:01.8143972Z dynamo/test_skip_non_tensor 1/1 2024-12-17T23:51:01.8144291Z dynamo/test_reconstruct 1/1 2024-12-17T23:51:01.8144595Z dynamo/test_sdpa 1/1 2024-12-17T23:51:01.8144868Z dynamo/test_recompiles 1/1 2024-12-17T23:51:01.8145180Z dynamo/test_pre_dispatch 1/1 2024-12-17T23:51:01.8145487Z dynamo/test_cudagraphs 1/1 2024-12-17T23:51:01.8145799Z dynamo/test_graph_region_tracker 1/1 2024-12-17T23:51:01.8146144Z dynamo/test_deviceguard 1/1 2024-12-17T23:51:01.8146429Z dynamo/test_sources 1/1 2024-12-17T23:51:01.8146723Z dynamo/test_structured_trace 1/1 2024-12-17T23:51:01.8147043Z dynamo/test_modes 1/1 2024-12-17T23:51:01.8147340Z dynamo/test_graph_deduplication 1/1 2024-12-17T23:51:01.8147671Z dynamo/test_ctx_manager 1/1 2024-12-17T23:51:01.8147981Z dynamo/test_activation_checkpointing 1/1 2024-12-17T23:51:01.8148415Z dynamo/test_trace_rules 1/1 2024-12-17T23:51:01.8148721Z dynamo/test_debug_utils 1/1 2024-12-17T23:51:01.8149031Z dynamo/test_bytecode_utils 1/1 2024-12-17T23:51:01.8149352Z dynamo/test_recompile_ux 1/1 2024-12-17T23:51:01.8149648Z dynamo/test_minifier 1/1 2024-12-17T23:51:01.8149944Z dynamo/test_comptime 1/1 2024-12-17T23:51:01.8150232Z test_hub 1/1 2024-12-17T23:51:01.8150485Z optim/test_swa_utils 1/1 2024-12-17T23:51:01.8150777Z test_quantization 3/4 2024-12-17T23:51:01.8151055Z Name: excluded (est. time: 0.0min) 2024-12-17T23:51:01.8151533Z Serial tests (0): 2024-12-17T23:51:01.8151795Z Parallel tests (0): 2024-12-17T23:51:01.8239172Z Running test_reductions 1/3 ... [2024-12-17 23:51:01.823582] 2024-12-17T23:51:01.8239881Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-17T23:51:01.8244787Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-17 23:51:01.824051] 2024-12-18T00:13:07.5939657Z 2024-12-18T00:13:07.5941011Z test_reductions 1/3 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.3_3d3a075aa7d77442_.log 2024-12-18T00:13:07.6505818Z Running 1531 items in this shard: test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float64, 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test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, 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 2024-12-18T00:13:07.7055017Z 2024-12-18T00:13:07.7055219Z Running test_reductions 3/3 ... [2024-12-18 00:13:07.596139] 2024-12-18T00:13:07.7055637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:13:07.7056695Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:13:07.596490] 2024-12-18T00:15:19.8412994Z 2024-12-18T00:15:19.8413931Z test_reductions 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_3.3_5bb44456a4cdfdbf_.log 2024-12-18T00:15:19.8993003Z Running 1541 items in this shard: 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_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, 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_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, 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_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_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_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64 2024-12-18T00:15:19.9563020Z 2024-12-18T00:15:19.9563272Z Running test_cuda_nvml_based_avail 1/1 ... [2024-12-18 00:15:19.843541] 2024-12-18T00:15:19.9563741Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:15:19.9564874Z Executing ['/opt/conda/envs/py_3.12/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 00:15:19.843931] 2024-12-18T00:15:23.0901107Z 2024-12-18T00:15:23.0902137Z 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_5876ab19b7ef0294_.log 2024-12-18T00:15:23.0903081Z Running 0 items in this shard: 2024-12-18T00:15:23.0903302Z 2024-12-18T00:15:23.0903847Z Running test_cuda_primary_ctx 1/1 ... [2024-12-18 00:15:23.090229] 2024-12-18T00:15:23.0904335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:15:23.0907354Z Executing ['/opt/conda/envs/py_3.12/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 00:15:23.090517] 2024-12-18T00:15:26.3180409Z 2024-12-18T00:15:26.3181346Z 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_b9f5e0e3295495cf_.log 2024-12-18T00:15:26.3182129Z Running 0 items in this shard: 2024-12-18T00:15:26.3182326Z 2024-12-18T00:15:26.3183847Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-12-18 00:15:26.318214] 2024-12-18T00:15:29.0516943Z running install 2024-12-18T00:15:29.0517942Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:15:29.0518776Z !! 2024-12-18T00:15:29.0518894Z 2024-12-18T00:15:29.0519048Z ******************************************************************************** 2024-12-18T00:15:29.0519445Z Please avoid running ``setup.py`` directly. 2024-12-18T00:15:29.0519855Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:15:29.0520218Z standards-based tools. 2024-12-18T00:15:29.0520417Z 2024-12-18T00:15:29.0520723Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:15:29.0521262Z ******************************************************************************** 2024-12-18T00:15:29.0521517Z 2024-12-18T00:15:29.0521613Z !! 2024-12-18T00:15:29.0521839Z self.initialize_options() 2024-12-18T00:15:29.0646624Z running build 2024-12-18T00:15:29.0646927Z running build_py 2024-12-18T00:15:29.0722075Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T00:15:29.0724805Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T00:15:29.0733940Z running build_ext 2024-12-18T00:15:29.1826074Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:15:29.1827278Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312 2024-12-18T00:15:29.2137358Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-12-18T00:15:29.2138105Z Compiling objects... 2024-12-18T00:15:29.2138430Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:15:30.2403542Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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 2024-12-18T00:15:30.2503206Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-12-18T00:15:30.5195030Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:15:30.5500764Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-12-18T00:15:30.5514239Z Compiling objects... 2024-12-18T00:15:30.5514602Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:15:31.3177935Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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 2024-12-18T00:15:31.3228842Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-12-18T00:15:31.5488315Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:15:31.5795241Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-12-18T00:15:31.5796035Z Compiling objects... 2024-12-18T00:15:31.5796372Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:15:32.5552372Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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 2024-12-18T00:15:32.5604170Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-12-18T00:15:32.8198941Z running install_lib 2024-12-18T00:15:32.8277396Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-12-18T00:15:32.8281768Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:15:32.8283443Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:15:32.8285833Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:15:32.8321079Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:15:32.8355342Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:15:32.8394919Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-312.pyc 2024-12-18T00:15:32.8397524Z running install_egg_info 2024-12-18T00:15:32.8565529Z running egg_info 2024-12-18T00:15:32.8566999Z creating torch_test_cpp_extension.egg-info 2024-12-18T00:15:32.8640659Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:15:32.8643561Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:15:32.8645277Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:15:32.8647079Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:15:32.8648233Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:15:32.8722586Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:15:32.8730043Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:15:32.8731455Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-12-18T00:15:32.8737652Z running install_scripts 2024-12-18T00:15:35.0654534Z running install 2024-12-18T00:15:35.0655559Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:15:35.0656358Z !! 2024-12-18T00:15:35.0656498Z 2024-12-18T00:15:35.0656626Z ******************************************************************************** 2024-12-18T00:15:35.0657034Z Please avoid running ``setup.py`` directly. 2024-12-18T00:15:35.0657443Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:15:35.0657806Z standards-based tools. 2024-12-18T00:15:35.0658008Z 2024-12-18T00:15:35.0658317Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:15:35.0658853Z ******************************************************************************** 2024-12-18T00:15:35.0659109Z 2024-12-18T00:15:35.0659194Z !! 2024-12-18T00:15:35.0659420Z self.initialize_options() 2024-12-18T00:15:35.0785148Z running build 2024-12-18T00:15:35.0785402Z running build_ext 2024-12-18T00:15:35.1866471Z building 'no_python_abi_suffix_test' extension 2024-12-18T00:15:35.1868733Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312 2024-12-18T00:15:35.2168598Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-12-18T00:15:35.2169481Z Compiling objects... 2024-12-18T00:15:35.2169803Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:15:35.2956499Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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 2024-12-18T00:15:35.2996102Z creating build/lib.linux-x86_64-cpython-312 2024-12-18T00:15:35.3001239Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2024-12-18T00:15:35.3575094Z running install_lib 2024-12-18T00:15:35.3651881Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-12-18T00:15:35.3656015Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-12-18T00:15:35.3660676Z running install_egg_info 2024-12-18T00:15:35.3836812Z running egg_info 2024-12-18T00:15:35.3837419Z creating no_python_abi_suffix_test.egg-info 2024-12-18T00:15:35.3907703Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-12-18T00:15:35.3911307Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-12-18T00:15:35.3913091Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-12-18T00:15:35.3914398Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:15:35.3986793Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:15:35.3994110Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:15:35.3995567Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2024-12-18T00:15:35.4000382Z running install_scripts 2024-12-18T00:15:35.7667176Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:15:35.7670138Z Executing ['/opt/conda/envs/py_3.12/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 00:15:35.766722] 2024-12-18T00:15:41.9178780Z 2024-12-18T00:15:41.9180136Z 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_3c4e9f3ee6693490_.log 2024-12-18T00:15:41.9187624Z 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 2024-12-18T00:15:41.9194133Z 2024-12-18T00:15:41.9194434Z Running test_spectral_ops 1/1 ... [2024-12-18 00:15:41.917931] 2024-12-18T00:15:41.9194855Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:15:41.9195893Z Executing ['/opt/conda/envs/py_3.12/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 00:15:41.918254] 2024-12-18T00:16:14.5268785Z 2024-12-18T00:16:14.5270156Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_d348834830966a74_.log 2024-12-18T00:16:14.5364866Z 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 2024-12-18T00:16:14.5453604Z 2024-12-18T00:16:14.5453854Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-12-18 00:16:14.527593] 2024-12-18T00:16:17.2442878Z running install 2024-12-18T00:16:17.2444246Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:16:17.2445692Z !! 2024-12-18T00:16:17.2445882Z 2024-12-18T00:16:17.2446068Z ******************************************************************************** 2024-12-18T00:16:17.2447380Z Please avoid running ``setup.py`` directly. 2024-12-18T00:16:17.2448179Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:16:17.2448857Z standards-based tools. 2024-12-18T00:16:17.2449214Z 2024-12-18T00:16:17.2449767Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:16:17.2450754Z ******************************************************************************** 2024-12-18T00:16:17.2451218Z 2024-12-18T00:16:17.2451364Z !! 2024-12-18T00:16:17.2451756Z self.initialize_options() 2024-12-18T00:16:17.2577629Z running build 2024-12-18T00:16:17.2577933Z running build_py 2024-12-18T00:16:17.2654750Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T00:16:17.2657016Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T00:16:17.2660261Z running build_ext 2024-12-18T00:16:17.3443822Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:16:17.3445395Z creating build/temp.linux-x86_64-cpython-312 2024-12-18T00:16:17.3451316Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T00:16:18.5009358Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T00:16:18.5010385Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T00:16:18.5011282Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-12-18T00:16:18.5011835Z from extension.cpp:1: 2024-12-18T00:16:18.5014834Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T00:16:18.5015687Z extension.cpp:45:53: required from here 2024-12-18T00:16:18.5017290Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T00:16:18.5018643Z 1539 | class class_ : public detail::generic_type { 2024-12-18T00:16:18.5019292Z | ^~~~~~ 2024-12-18T00:16:18.5021267Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T00:16:18.5022878Z extension.cpp:45:53: required from here 2024-12-18T00:16:18.5026219Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2024-12-18T00:16:18.5028983Z 1599 | with_internals([&](internals &internals) { 2024-12-18T00:16:18.5029385Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:18.5029905Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T00:16:18.5030493Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:18.5030958Z 1601 | : internals.registered_types_cpp; 2024-12-18T00:16:18.5031381Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:18.5031814Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T00:16:18.5032241Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:18.5032647Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T00:16:18.5033035Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:16:18.5033373Z 1604 | }); 2024-12-18T00:16:18.5033633Z | ~ 2024-12-18T00:16:18.5037092Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-12-18T00:16:18.8955762Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:16:18.8960653Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T00:16:19.9575308Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-12-18T00:16:20.3281404Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:16:20.3286376Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T00:16:21.6083168Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:21.6084178Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:21.6085033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:21.6086016Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:21.6086754Z from rng_extension.cpp:6: 2024-12-18T00:16:21.6087658Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:21.6088512Z 1123 | # pragma unroll 2024-12-18T00:16:21.6088775Z | 2024-12-18T00:16:21.6089376Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T00:16:21.6090336Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:21.6091367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:21.6092264Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:21.6093221Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:21.6093953Z from rng_extension.cpp:6: 2024-12-18T00:16:21.6094796Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:21.6095621Z 59 | #pragma unroll 2024-12-18T00:16:21.6095919Z | 2024-12-18T00:16:21.6096627Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:21.6097446Z 72 | #pragma unroll 2024-12-18T00:16:21.6097701Z | 2024-12-18T00:16:21.6098427Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:21.6099470Z 87 | #pragma unroll 2024-12-18T00:16:21.6099774Z | 2024-12-18T00:16:21.6100305Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T00:16:21.6101327Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:16:21.6102194Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:16:21.6103035Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:16:21.6104109Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:16:21.6104847Z from rng_extension.cpp:6: 2024-12-18T00:16:21.6105722Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:16:21.6106586Z 153 | #pragma unroll 2024-12-18T00:16:21.6106835Z | 2024-12-18T00:16:21.6110230Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-12-18T00:16:22.0035569Z running install_lib 2024-12-18T00:16:22.0123106Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:16:22.0212465Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:16:22.0299125Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T00:16:22.0391451Z running install_egg_info 2024-12-18T00:16:22.0562693Z running egg_info 2024-12-18T00:16:22.0630271Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:16:22.0633613Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:16:22.0635552Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:16:22.0637691Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:16:22.0711607Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:16:22.0720341Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:16:22.0721791Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2024-12-18T00:16:22.0723463Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-12-18T00:16:22.0729339Z running install_scripts 2024-12-18T00:16:24.2387658Z running install 2024-12-18T00:16:24.2389011Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:16:24.2389840Z !! 2024-12-18T00:16:24.2390235Z 2024-12-18T00:16:24.2390378Z ******************************************************************************** 2024-12-18T00:16:24.2390776Z Please avoid running ``setup.py`` directly. 2024-12-18T00:16:24.2391179Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:16:24.2391556Z standards-based tools. 2024-12-18T00:16:24.2391756Z 2024-12-18T00:16:24.2392060Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:16:24.2392595Z ******************************************************************************** 2024-12-18T00:16:24.2392838Z 2024-12-18T00:16:24.2392932Z !! 2024-12-18T00:16:24.2393155Z self.initialize_options() 2024-12-18T00:16:24.2518021Z running build 2024-12-18T00:16:24.2518315Z running build_ext 2024-12-18T00:16:24.3612980Z building 'no_python_abi_suffix_test' extension 2024-12-18T00:16:24.3916797Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-12-18T00:16:24.3917719Z Compiling objects... 2024-12-18T00:16:24.3918106Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:16:24.4184992Z ninja: no work to do. 2024-12-18T00:16:24.4228513Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2024-12-18T00:16:24.4821714Z running install_lib 2024-12-18T00:16:24.4902947Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-12-18T00:16:24.4907189Z running install_egg_info 2024-12-18T00:16:24.5078350Z running egg_info 2024-12-18T00:16:24.5148131Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-12-18T00:16:24.5152746Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-12-18T00:16:24.5154911Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-12-18T00:16:24.5226698Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:16:24.5234844Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:16:24.5236779Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info' (and everything under it) 2024-12-18T00:16:24.5238491Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2024-12-18T00:16:24.5243286Z running install_scripts 2024-12-18T00:16:24.8898163Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:16:24.8900722Z Executing ['/opt/conda/envs/py_3.12/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 00:16:24.889782] 2024-12-18T00:16:31.0264962Z 2024-12-18T00:16:31.0266353Z 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_66532d962a562892_.log 2024-12-18T00:16:31.0273725Z 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 2024-12-18T00:16:31.0280132Z 2024-12-18T00:16:31.0280335Z Running test_show_pickle 1/1 ... [2024-12-18 00:16:31.026713] 2024-12-18T00:16:31.0280734Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:16:31.0281775Z Executing ['/opt/conda/envs/py_3.12/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 00:16:31.027055] 2024-12-18T00:16:34.5461336Z 2024-12-18T00:16:34.5462860Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_c898faa15cd2e4c1_.log 2024-12-18T00:16:34.5464427Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2024-12-18T00:16:34.5465169Z 2024-12-18T00:16:34.5465531Z Running test_namedtuple_return_api 1/1 ... [2024-12-18 00:16:34.546327] 2024-12-18T00:16:34.5466239Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:16:34.5470600Z Executing ['/opt/conda/envs/py_3.12/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 00:16:34.546741] 2024-12-18T00:16:39.7181002Z 2024-12-18T00:16:39.7182400Z 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_d6e4989efce02b9e_.log 2024-12-18T00:16:39.7184563Z 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 2024-12-18T00:16:39.7186039Z 2024-12-18T00:16:39.7186274Z Running test_jit_disabled 1/1 ... [2024-12-18 00:16:39.718212] 2024-12-18T00:16:39.7186762Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:16:39.7187806Z Executing ['/opt/conda/envs/py_3.12/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 00:16:39.718487] 2024-12-18T00:16:43.2875277Z 2024-12-18T00:16:43.2876510Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_83b5018e58b8dba8_.log 2024-12-18T00:16:43.2878279Z 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 2024-12-18T00:16:43.2879692Z 2024-12-18T00:16:43.2879876Z Running test_autocast 1/1 ... [2024-12-18 00:16:43.287659] 2024-12-18T00:16:43.2880274Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:16:43.2882023Z Executing ['/opt/conda/envs/py_3.12/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 00:16:43.287976] 2024-12-18T00:17:19.6492439Z 2024-12-18T00:17:19.6493362Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_c603ae9486a96a8d_.log 2024-12-18T00:17:19.6500414Z 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 2024-12-18T00:17:19.6506372Z 2024-12-18T00:17:19.6506562Z Running test_tensorexpr 1/1 ... [2024-12-18 00:17:19.649364] 2024-12-18T00:17:19.6506966Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:19.6508003Z Executing ['/opt/conda/envs/py_3.12/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 00:17:19.649693] 2024-12-18T00:17:23.0185049Z 2024-12-18T00:17:23.0186021Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_de4e89f7cef51227_.log 2024-12-18T00:17:23.0206402Z 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 2024-12-18T00:17:23.0225967Z 2024-12-18T00:17:23.0226153Z Running test_fake_tensor 1/1 ... [2024-12-18 00:17:23.018715] 2024-12-18T00:17:23.0226562Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:23.0227602Z Executing ['/opt/conda/envs/py_3.12/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 00:17:23.019055] 2024-12-18T00:17:38.1560381Z 2024-12-18T00:17:38.1561509Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_9b79464802823692_.log 2024-12-18T00:17:38.1655997Z 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 2024-12-18T00:17:38.1749105Z 2024-12-18T00:17:38.1749295Z Running test_fx 1/1 ... [2024-12-18 00:17:38.156423] 2024-12-18T00:17:38.1749679Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:17:38.1750674Z Executing ['/opt/conda/envs/py_3.12/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 00:17:38.156748] 2024-12-18T00:23:58.2530302Z 2024-12-18T00:23:58.2531173Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_c74d7faab47c2eaa_.log 2024-12-18T00:23:58.3000966Z 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 2024-12-18T00:23:58.3455420Z 2024-12-18T00:23:58.3455661Z Running test_multiprocessing 1/1 ... [2024-12-18 00:23:58.254822] 2024-12-18T00:23:58.3456108Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:23:58.3457172Z Executing ['/opt/conda/envs/py_3.12/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 00:23:58.255143] 2024-12-18T00:24:38.1244831Z 2024-12-18T00:24:38.1245907Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_b07d4ec622f13fb1_.log 2024-12-18T00:24:38.1260826Z 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 2024-12-18T00:24:38.1274304Z 2024-12-18T00:24:38.1274494Z Running test_native_mha 1/1 ... [2024-12-18 00:24:38.124740] 2024-12-18T00:24:38.1274913Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:24:38.1275942Z Executing ['/opt/conda/envs/py_3.12/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 00:24:38.125042] 2024-12-18T00:25:06.7769574Z 2024-12-18T00:25:06.7770476Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_82b4575bc4828e55_.log 2024-12-18T00:25:06.7795008Z 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 2024-12-18T00:25:06.7815405Z 2024-12-18T00:25:06.7815613Z Running test_sort_and_select 1/1 ... [2024-12-18 00:25:06.777167] 2024-12-18T00:25:06.7816034Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:25:06.7817086Z Executing ['/opt/conda/envs/py_3.12/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 00:25:06.777520] 2024-12-18T00:25:57.4608806Z 2024-12-18T00:25:57.4609839Z 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_d3f12872bd44785c_.log 2024-12-18T00:25:57.4648414Z 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 2024-12-18T00:25:57.4685206Z 2024-12-18T00:25:57.4685394Z Running nn/test_pooling 1/1 ... [2024-12-18 00:25:57.461403] 2024-12-18T00:25:57.4685810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:25:57.4686850Z Executing ['/opt/conda/envs/py_3.12/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 00:25:57.461713] 2024-12-18T00:27:01.6761427Z 2024-12-18T00:27:01.6762533Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_72ffaffa70a5e324_.log 2024-12-18T00:27:01.6800959Z 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 2024-12-18T00:27:01.6849034Z 2024-12-18T00:27:01.6849253Z Running test_python_dispatch 1/1 ... [2024-12-18 00:27:01.676537] 2024-12-18T00:27:01.6849688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:27:01.6850756Z Executing ['/opt/conda/envs/py_3.12/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 00:27:01.676872] 2024-12-18T00:27:24.5493686Z 2024-12-18T00:27:24.5494657Z PRINTING LOG FILE of test_python_dispatch 1/1 (test/test-reports/test_python_dispatch_1.1_3235abbc3b780f40_.log) 2024-12-18T00:27:24.5495990Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-81ad8354948651df.xml 2024-12-18T00:27:24.5497055Z ============================= test session starts ============================== 2024-12-18T00:27:24.5497995Z platform linux -- Python 3.12.7, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.12/bin/python 2024-12-18T00:27:24.5498793Z cachedir: .pytest_cache 2024-12-18T00:27:24.5499785Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:27:24.5501562Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:27:24.5502072Z configfile: pytest.ini 2024-12-18T00:27:24.5503089Z 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 2024-12-18T00:27:24.5504330Z collecting ... collected 119 items 2024-12-18T00:27:24.5504860Z stepcurrent: Cannot find last run test, not skipping 2024-12-18T00:27:24.5552498Z 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 2024-12-18T00:27:24.5596343Z 2024-12-18T00:27:24.5596717Z test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed PASSED [0.3496s] [ 0%] 2024-12-18T00:27:24.5597497Z test_python_dispatch.py::TestPythonRegistration::test_alias_analysis PASSED [0.1539s] [ 1%] 2024-12-18T00:27:24.5599865Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library PASSED [0.0783s] [ 2%] 2024-12-18T00:27:24.5600742Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing PASSED [0.0412s] [ 3%] 2024-12-18T00:27:24.5601685Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing PASSED [0.0540s] [ 4%] 2024-12-18T00:27:24.5602613Z test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind PASSED [0.0170s] [ 5%] 2024-12-18T00:27:24.5603475Z test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable PASSED [0.0370s] [ 5%] 2024-12-18T00:27:24.5604360Z test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg PASSED [0.0484s] [ 6%] 2024-12-18T00:27:24.5605684Z test_python_dispatch.py::TestPythonRegistration::test_fallback SKIPPED [0.0983s] ( is not 2024-12-18T00:27:24.5606680Z 2024-12-18T00:27:24.5606685Z 2024-12-18T00:27:24.5606907Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:27:24.5607337Z import torch._dynamo 2024-12-18T00:27:24.5607644Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:27:24.5607906Z 2024-12-18T00:27:24.5607910Z 2024-12-18T00:27:24.5608104Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5608772Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_fallback 2024-12-18T00:27:24.5609255Z 2024-12-18T00:27:24.5609607Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 7%] 2024-12-18T00:27:24.5610347Z test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough PASSED [0.0794s] [ 8%] 2024-12-18T00:27:24.5611357Z test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset SKIPPED [0.0752s] (ValueError: not enough values to unpack (expected 2, got 1) 2024-12-18T00:27:24.5612012Z 2024-12-18T00:27:24.5612017Z 2024-12-18T00:27:24.5612241Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:27:24.5612658Z import torch._dynamo 2024-12-18T00:27:24.5612986Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:27:24.5613246Z 2024-12-18T00:27:24.5613250Z 2024-12-18T00:27:24.5613440Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5614121Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_fallback_keyset 2024-12-18T00:27:24.5614611Z 2024-12-18T00:27:24.5614888Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 9%] 2024-12-18T00:27:24.5615716Z test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls PASSED [0.0474s] [ 10%] 2024-12-18T00:27:24.5616638Z test_python_dispatch.py::TestPythonRegistration::test_finalizer SKIPPED [0.1679s] (Scalars are not equal! 2024-12-18T00:27:24.5617138Z 2024-12-18T00:27:24.5617241Z Expected 2 but got 8. 2024-12-18T00:27:24.5617516Z Absolute difference: 6 2024-12-18T00:27:24.5617794Z Relative difference: 3.0 2024-12-18T00:27:24.5617967Z 2024-12-18T00:27:24.5618162Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5618828Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_finalizer 2024-12-18T00:27:24.5619312Z 2024-12-18T00:27:24.5619574Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 10%] 2024-12-18T00:27:24.5621677Z test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries SKIPPED [0.0133s] (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%] 2024-12-18T00:27:24.5625150Z test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum SKIPPED [0.0129s] (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%] 2024-12-18T00:27:24.5628506Z test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator SKIPPED [0.0125s] (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%] 2024-12-18T00:27:24.5631817Z test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough SKIPPED [0.0126s] (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%] 2024-12-18T00:27:24.5633924Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_error_cases PASSED [0.0602s] [ 15%] 2024-12-18T00:27:24.5636426Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns SKIPPED [0.0126s] (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%] 2024-12-18T00:27:24.5639884Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns SKIPPED [0.0125s] (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%] 2024-12-18T00:27:24.5643328Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return SKIPPED [0.0125s] (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%] 2024-12-18T00:27:24.5646770Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional SKIPPED [0.0126s] (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%] 2024-12-18T00:27:24.5648925Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint ('RERUN', {'yellow': True}) [0.0541s] [ 19%] 2024-12-18T00:27:24.5649843Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint ('RERUN', {'yellow': True}) [0.0497s] [ 19%] 2024-12-18T00:27:24.5650699Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint FAILED [0.0501s] [ 19%] 2024-12-18T00:27:24.5651155Z 2024-12-18T00:27:24.5651296Z ==================================== RERUNS ==================================== 2024-12-18T00:27:24.5651795Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:27:24.5652270Z Traceback (most recent call last): 2024-12-18T00:27:24.5652844Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 573, in test_returning_symint 2024-12-18T00:27:24.5653438Z def test_returning_symint(self) -> None: 2024-12-18T00:27:24.5654138Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 574, in torch_dynamo_resume_in_test_returning_symint_at_574 2024-12-18T00:27:24.5654829Z shape_env = ShapeEnv() 2024-12-18T00:27:24.5655424Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1380, in __call__ 2024-12-18T00:27:24.5656076Z return self._torchdynamo_orig_callable( 2024-12-18T00:27:24.5656412Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5657013Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1164, in __call__ 2024-12-18T00:27:24.5657725Z result = self._inner_convert( 2024-12-18T00:27:24.5658030Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5658618Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 547, in __call__ 2024-12-18T00:27:24.5659236Z return _compile( 2024-12-18T00:27:24.5659478Z ^^^^^^^^^ 2024-12-18T00:27:24.5660035Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 986, in _compile 2024-12-18T00:27:24.5660756Z guarded_code = compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:27:24.5661189Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5661907Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 715, in compile_inner 2024-12-18T00:27:24.5662627Z return _compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:27:24.5663025Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5663664Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_utils_internal.py", line 95, in wrapper_function 2024-12-18T00:27:24.5664300Z return function(*args, **kwargs) 2024-12-18T00:27:24.5664613Z ^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5665245Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 750, in _compile_inner 2024-12-18T00:27:24.5665946Z out_code = transform_code_object(code, transform) 2024-12-18T00:27:24.5666311Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5667056Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object 2024-12-18T00:27:24.5667833Z transformations(instructions, code_options) 2024-12-18T00:27:24.5668548Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 231, in _fn 2024-12-18T00:27:24.5669165Z return fn(*args, **kwargs) 2024-12-18T00:27:24.5669442Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5670029Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 662, in transform 2024-12-18T00:27:24.5670650Z tracer.run() 2024-12-18T00:27:24.5671197Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2868, in run 2024-12-18T00:27:24.5671807Z super().run() 2024-12-18T00:27:24.5672351Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1052, in run 2024-12-18T00:27:24.5672953Z while self.step(): 2024-12-18T00:27:24.5673210Z ^^^^^^^^^^^ 2024-12-18T00:27:24.5673763Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 962, in step 2024-12-18T00:27:24.5674420Z self.dispatch_table[inst.opcode](self, inst) 2024-12-18T00:27:24.5675087Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 659, in wrapper 2024-12-18T00:27:24.5675722Z return inner_fn(self, inst) 2024-12-18T00:27:24.5676002Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5676583Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2341, in CALL 2024-12-18T00:27:24.5677196Z self._call(inst) 2024-12-18T00:27:24.5677755Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2335, in _call 2024-12-18T00:27:24.5678387Z self.call_function(fn, args, kwargs) 2024-12-18T00:27:24.5679045Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 897, in call_function 2024-12-18T00:27:24.5679833Z self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] 2024-12-18T00:27:24.5680287Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5681017Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function 2024-12-18T00:27:24.5681681Z tensor_variable = wrap_fx_proxy( 2024-12-18T00:27:24.5682000Z ^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5682637Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2153, in wrap_fx_proxy 2024-12-18T00:27:24.5683400Z return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs) 2024-12-18T00:27:24.5683831Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5684539Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2219, in wrap_fx_proxy_cls 2024-12-18T00:27:24.5685284Z return _wrap_fx_proxy( 2024-12-18T00:27:24.5685567Z ^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5686177Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2315, in _wrap_fx_proxy 2024-12-18T00:27:24.5686983Z example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True) 2024-12-18T00:27:24.5687458Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5688092Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2536, in get_fake_value 2024-12-18T00:27:24.5688851Z raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None 2024-12-18T00:27:24.5689609Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2471, in get_fake_value 2024-12-18T00:27:24.5690217Z ret_val = wrap_fake_exception( 2024-12-18T00:27:24.5690523Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5691137Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2017, in wrap_fake_exception 2024-12-18T00:27:24.5691769Z return fn() 2024-12-18T00:27:24.5692005Z ^^^^ 2024-12-18T00:27:24.5692508Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2472, in 2024-12-18T00:27:24.5693179Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:27:24.5693593Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5694192Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2604, in run_node 2024-12-18T00:27:24.5694869Z raise RuntimeError(make_error_message(e)).with_traceback( 2024-12-18T00:27:24.5695534Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2586, in run_node 2024-12-18T00:27:24.5696129Z return node.target(*args, **kwargs) 2024-12-18T00:27:24.5696455Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5697019Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_stats.py", line 21, in wrapper 2024-12-18T00:27:24.5697599Z return fn(*args, **kwargs) 2024-12-18T00:27:24.5697892Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5698528Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1276, in __torch_dispatch__ 2024-12-18T00:27:24.5699258Z return self.dispatch(func, types, args, kwargs) 2024-12-18T00:27:24.5699627Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5700265Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1816, in dispatch 2024-12-18T00:27:24.5700994Z return self._cached_dispatch_impl(func, types, args, kwargs) 2024-12-18T00:27:24.5701417Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5702119Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1377, in _cached_dispatch_impl 2024-12-18T00:27:24.5702884Z output = self._dispatch_impl(func, types, args, kwargs) 2024-12-18T00:27:24.5703282Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5704012Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 2354, in _dispatch_impl 2024-12-18T00:27:24.5704721Z op_impl_out = op_impl(self, func, *args, **kwargs) 2024-12-18T00:27:24.5705090Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5705724Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_impls.py", line 188, in constructors 2024-12-18T00:27:24.5706407Z with in_kernel_invocation_manager(fake_mode): 2024-12-18T00:27:24.5706766Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5707250Z File "/opt/conda/envs/py_3.12/lib/python3.12/contextlib.py", line 137, in __enter__ 2024-12-18T00:27:24.5707740Z return next(self.gen) 2024-12-18T00:27:24.5708083Z ^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5708814Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 509, in in_kernel_invocation_manager 2024-12-18T00:27:24.5709657Z assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" 2024-12-18T00:27:24.5710092Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5710790Z torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:27:24.5711488Z True, False 2024-12-18T00:27:24.5711619Z 2024-12-18T00:27:24.5711724Z from user code: 2024-12-18T00:27:24.5712331Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:27:24.5713069Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:27:24.5713341Z 2024-12-18T00:27:24.5713562Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:27:24.5713893Z 2024-12-18T00:27:24.5713908Z 2024-12-18T00:27:24.5714114Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:27:24.5714545Z import torch._dynamo 2024-12-18T00:27:24.5714862Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:27:24.5715107Z 2024-12-18T00:27:24.5715111Z 2024-12-18T00:27:24.5715302Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5715993Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:27:24.5716503Z 2024-12-18T00:27:24.5716739Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:27:24.5717340Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:27:24.5717820Z Traceback (most recent call last): 2024-12-18T00:27:24.5718400Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 573, in test_returning_symint 2024-12-18T00:27:24.5718994Z def test_returning_symint(self) -> None: 2024-12-18T00:27:24.5719697Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 574, in torch_dynamo_resume_in_test_returning_symint_at_574 2024-12-18T00:27:24.5720387Z shape_env = ShapeEnv() 2024-12-18T00:27:24.5720977Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1380, in __call__ 2024-12-18T00:27:24.5721626Z return self._torchdynamo_orig_callable( 2024-12-18T00:27:24.5721961Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5722560Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1164, in __call__ 2024-12-18T00:27:24.5723190Z result = self._inner_convert( 2024-12-18T00:27:24.5723489Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5724078Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 547, in __call__ 2024-12-18T00:27:24.5724695Z return _compile( 2024-12-18T00:27:24.5724935Z ^^^^^^^^^ 2024-12-18T00:27:24.5725484Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 986, in _compile 2024-12-18T00:27:24.5726279Z guarded_code = compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:27:24.5726716Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5727374Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 715, in compile_inner 2024-12-18T00:27:24.5728093Z return _compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:27:24.5728487Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5729120Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_utils_internal.py", line 95, in wrapper_function 2024-12-18T00:27:24.5729750Z return function(*args, **kwargs) 2024-12-18T00:27:24.5730111Z ^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5730735Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 750, in _compile_inner 2024-12-18T00:27:24.5731432Z out_code = transform_code_object(code, transform) 2024-12-18T00:27:24.5731793Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5732529Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object 2024-12-18T00:27:24.5733300Z transformations(instructions, code_options) 2024-12-18T00:27:24.5733928Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 231, in _fn 2024-12-18T00:27:24.5734526Z return fn(*args, **kwargs) 2024-12-18T00:27:24.5734810Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5735387Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 662, in transform 2024-12-18T00:27:24.5736005Z tracer.run() 2024-12-18T00:27:24.5736706Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2868, in run 2024-12-18T00:27:24.5737321Z super().run() 2024-12-18T00:27:24.5737872Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1052, in run 2024-12-18T00:27:24.5738477Z while self.step(): 2024-12-18T00:27:24.5738735Z ^^^^^^^^^^^ 2024-12-18T00:27:24.5739281Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 962, in step 2024-12-18T00:27:24.5739937Z self.dispatch_table[inst.opcode](self, inst) 2024-12-18T00:27:24.5740601Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 659, in wrapper 2024-12-18T00:27:24.5741235Z return inner_fn(self, inst) 2024-12-18T00:27:24.5741513Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5742105Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2341, in CALL 2024-12-18T00:27:24.5742723Z self._call(inst) 2024-12-18T00:27:24.5743288Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2335, in _call 2024-12-18T00:27:24.5743934Z self.call_function(fn, args, kwargs) 2024-12-18T00:27:24.5744589Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 897, in call_function 2024-12-18T00:27:24.5745382Z self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] 2024-12-18T00:27:24.5745843Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5746504Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function 2024-12-18T00:27:24.5747171Z tensor_variable = wrap_fx_proxy( 2024-12-18T00:27:24.5747489Z ^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5748126Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2153, in wrap_fx_proxy 2024-12-18T00:27:24.5748968Z return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs) 2024-12-18T00:27:24.5749508Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5750215Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2219, in wrap_fx_proxy_cls 2024-12-18T00:27:24.5750910Z return _wrap_fx_proxy( 2024-12-18T00:27:24.5751193Z ^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5751804Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2315, in _wrap_fx_proxy 2024-12-18T00:27:24.5752606Z example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True) 2024-12-18T00:27:24.5753084Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5753806Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2536, in get_fake_value 2024-12-18T00:27:24.5754564Z raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None 2024-12-18T00:27:24.5755325Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2471, in get_fake_value 2024-12-18T00:27:24.5755941Z ret_val = wrap_fake_exception( 2024-12-18T00:27:24.5756247Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5756855Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2017, in wrap_fake_exception 2024-12-18T00:27:24.5757480Z return fn() 2024-12-18T00:27:24.5757713Z ^^^^ 2024-12-18T00:27:24.5758206Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2472, in 2024-12-18T00:27:24.5758869Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:27:24.5759276Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5759882Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2604, in run_node 2024-12-18T00:27:24.5760557Z raise RuntimeError(make_error_message(e)).with_traceback( 2024-12-18T00:27:24.5761222Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2586, in run_node 2024-12-18T00:27:24.5761814Z return node.target(*args, **kwargs) 2024-12-18T00:27:24.5762140Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5762699Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_stats.py", line 21, in wrapper 2024-12-18T00:27:24.5763273Z return fn(*args, **kwargs) 2024-12-18T00:27:24.5763555Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5764193Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1276, in __torch_dispatch__ 2024-12-18T00:27:24.5764902Z return self.dispatch(func, types, args, kwargs) 2024-12-18T00:27:24.5765266Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5765905Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1816, in dispatch 2024-12-18T00:27:24.5766677Z return self._cached_dispatch_impl(func, types, args, kwargs) 2024-12-18T00:27:24.5767104Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5767813Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1377, in _cached_dispatch_impl 2024-12-18T00:27:24.5768566Z output = self._dispatch_impl(func, types, args, kwargs) 2024-12-18T00:27:24.5768973Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5769651Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 2354, in _dispatch_impl 2024-12-18T00:27:24.5770368Z op_impl_out = op_impl(self, func, *args, **kwargs) 2024-12-18T00:27:24.5770738Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5771396Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_impls.py", line 188, in constructors 2024-12-18T00:27:24.5772065Z with in_kernel_invocation_manager(fake_mode): 2024-12-18T00:27:24.5772501Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5772991Z File "/opt/conda/envs/py_3.12/lib/python3.12/contextlib.py", line 137, in __enter__ 2024-12-18T00:27:24.5773487Z return next(self.gen) 2024-12-18T00:27:24.5773761Z ^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5774417Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 509, in in_kernel_invocation_manager 2024-12-18T00:27:24.5775252Z assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" 2024-12-18T00:27:24.5775686Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5776383Z torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:27:24.5777140Z True, False 2024-12-18T00:27:24.5777266Z 2024-12-18T00:27:24.5777375Z from user code: 2024-12-18T00:27:24.5777980Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:27:24.5778718Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:27:24.5778987Z 2024-12-18T00:27:24.5779203Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:27:24.5779546Z 2024-12-18T00:27:24.5779550Z 2024-12-18T00:27:24.5779760Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:27:24.5780186Z import torch._dynamo 2024-12-18T00:27:24.5780502Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:27:24.5780747Z 2024-12-18T00:27:24.5780751Z 2024-12-18T00:27:24.5780939Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5781636Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:27:24.5782142Z 2024-12-18T00:27:24.5782377Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:27:24.5782871Z =================================== FAILURES =================================== 2024-12-18T00:27:24.5783373Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:27:24.5783845Z Traceback (most recent call last): 2024-12-18T00:27:24.5784415Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 573, in test_returning_symint 2024-12-18T00:27:24.5785017Z def test_returning_symint(self) -> None: 2024-12-18T00:27:24.5785718Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 574, in torch_dynamo_resume_in_test_returning_symint_at_574 2024-12-18T00:27:24.5786408Z shape_env = ShapeEnv() 2024-12-18T00:27:24.5787006Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1380, in __call__ 2024-12-18T00:27:24.5787664Z return self._torchdynamo_orig_callable( 2024-12-18T00:27:24.5787990Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5788683Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 1164, in __call__ 2024-12-18T00:27:24.5789321Z result = self._inner_convert( 2024-12-18T00:27:24.5789620Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5799749Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 547, in __call__ 2024-12-18T00:27:24.5800568Z return _compile( 2024-12-18T00:27:24.5800832Z ^^^^^^^^^ 2024-12-18T00:27:24.5801400Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 986, in _compile 2024-12-18T00:27:24.5802136Z guarded_code = compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:27:24.5802575Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5803255Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 715, in compile_inner 2024-12-18T00:27:24.5803965Z return _compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:27:24.5804527Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5805167Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_utils_internal.py", line 95, in wrapper_function 2024-12-18T00:27:24.5805812Z return function(*args, **kwargs) 2024-12-18T00:27:24.5806125Z ^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5806741Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 750, in _compile_inner 2024-12-18T00:27:24.5807440Z out_code = transform_code_object(code, transform) 2024-12-18T00:27:24.5807817Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5808626Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object 2024-12-18T00:27:24.5809403Z transformations(instructions, code_options) 2024-12-18T00:27:24.5810039Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 231, in _fn 2024-12-18T00:27:24.5810639Z return fn(*args, **kwargs) 2024-12-18T00:27:24.5810927Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5811518Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py", line 662, in transform 2024-12-18T00:27:24.5812144Z tracer.run() 2024-12-18T00:27:24.5812690Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2868, in run 2024-12-18T00:27:24.5813304Z super().run() 2024-12-18T00:27:24.5813838Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 1052, in run 2024-12-18T00:27:24.5814455Z while self.step(): 2024-12-18T00:27:24.5814715Z ^^^^^^^^^^^ 2024-12-18T00:27:24.5815272Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 962, in step 2024-12-18T00:27:24.5815935Z self.dispatch_table[inst.opcode](self, inst) 2024-12-18T00:27:24.5816592Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 659, in wrapper 2024-12-18T00:27:24.5817231Z return inner_fn(self, inst) 2024-12-18T00:27:24.5817516Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5818089Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2341, in CALL 2024-12-18T00:27:24.5818696Z self._call(inst) 2024-12-18T00:27:24.5819260Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 2335, in _call 2024-12-18T00:27:24.5819886Z self.call_function(fn, args, kwargs) 2024-12-18T00:27:24.5820556Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py", line 897, in call_function 2024-12-18T00:27:24.5821342Z self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] 2024-12-18T00:27:24.5821800Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5822456Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function 2024-12-18T00:27:24.5823120Z tensor_variable = wrap_fx_proxy( 2024-12-18T00:27:24.5823422Z ^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5824066Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2153, in wrap_fx_proxy 2024-12-18T00:27:24.5824832Z return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs) 2024-12-18T00:27:24.5825261Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5825968Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2219, in wrap_fx_proxy_cls 2024-12-18T00:27:24.5826669Z return _wrap_fx_proxy( 2024-12-18T00:27:24.5826935Z ^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5827558Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/builder.py", line 2315, in _wrap_fx_proxy 2024-12-18T00:27:24.5828504Z example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True) 2024-12-18T00:27:24.5828985Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5829625Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2536, in get_fake_value 2024-12-18T00:27:24.5830390Z raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None 2024-12-18T00:27:24.5831136Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2471, in get_fake_value 2024-12-18T00:27:24.5831763Z ret_val = wrap_fake_exception( 2024-12-18T00:27:24.5832075Z ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5832744Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2017, in wrap_fake_exception 2024-12-18T00:27:24.5833381Z return fn() 2024-12-18T00:27:24.5833606Z ^^^^ 2024-12-18T00:27:24.5834121Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2472, in 2024-12-18T00:27:24.5834793Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:27:24.5835207Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5835813Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2604, in run_node 2024-12-18T00:27:24.5836666Z raise RuntimeError(make_error_message(e)).with_traceback( 2024-12-18T00:27:24.5837323Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 2586, in run_node 2024-12-18T00:27:24.5837931Z return node.target(*args, **kwargs) 2024-12-18T00:27:24.5838256Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5838821Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_stats.py", line 21, in wrapper 2024-12-18T00:27:24.5839402Z return fn(*args, **kwargs) 2024-12-18T00:27:24.5839683Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5840324Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1276, in __torch_dispatch__ 2024-12-18T00:27:24.5841065Z return self.dispatch(func, types, args, kwargs) 2024-12-18T00:27:24.5841436Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5842079Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1816, in dispatch 2024-12-18T00:27:24.5842803Z return self._cached_dispatch_impl(func, types, args, kwargs) 2024-12-18T00:27:24.5843221Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5843935Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 1377, in _cached_dispatch_impl 2024-12-18T00:27:24.5844704Z output = self._dispatch_impl(func, types, args, kwargs) 2024-12-18T00:27:24.5845112Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5845794Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 2354, in _dispatch_impl 2024-12-18T00:27:24.5846513Z op_impl_out = op_impl(self, func, *args, **kwargs) 2024-12-18T00:27:24.5846868Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5847517Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_impls.py", line 188, in constructors 2024-12-18T00:27:24.5848204Z with in_kernel_invocation_manager(fake_mode): 2024-12-18T00:27:24.5848557Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5849043Z File "/opt/conda/envs/py_3.12/lib/python3.12/contextlib.py", line 137, in __enter__ 2024-12-18T00:27:24.5849532Z return next(self.gen) 2024-12-18T00:27:24.5849792Z ^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5850456Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py", line 509, in in_kernel_invocation_manager 2024-12-18T00:27:24.5851402Z assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" 2024-12-18T00:27:24.5851834Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5852532Z torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:27:24.5853219Z True, False 2024-12-18T00:27:24.5853361Z 2024-12-18T00:27:24.5853456Z from user code: 2024-12-18T00:27:24.5854080Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:27:24.5854815Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:27:24.5855075Z 2024-12-18T00:27:24.5855378Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:27:24.5855714Z 2024-12-18T00:27:24.5855718Z 2024-12-18T00:27:24.5855937Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:27:24.5856368Z import torch._dynamo 2024-12-18T00:27:24.5856675Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:27:24.5856938Z 2024-12-18T00:27:24.5856942Z 2024-12-18T00:27:24.5857135Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5857837Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:27:24.5858352Z 2024-12-18T00:27:24.5858587Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:27:24.5859523Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-81ad8354948651df.xml - 2024-12-18T00:27:24.5860387Z =========================== short test summary info ============================ 2024-12-18T00:27:24.5861558Z FAILED [0.0501s] test_python_dispatch.py::TestPythonRegistration::test_returning_symint - torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:27:24.5862621Z True, False 2024-12-18T00:27:24.5862762Z 2024-12-18T00:27:24.5862856Z from user code: 2024-12-18T00:27:24.5863481Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:27:24.5864223Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:27:24.5864481Z 2024-12-18T00:27:24.5864717Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:27:24.5865051Z 2024-12-18T00:27:24.5865055Z 2024-12-18T00:27:24.5865276Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:27:24.5865687Z import torch._dynamo 2024-12-18T00:27:24.5866007Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:27:24.5866265Z 2024-12-18T00:27:24.5866269Z 2024-12-18T00:27:24.5866460Z To execute this test, run the following from the base repo dir: 2024-12-18T00:27:24.5867151Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:27:24.5867651Z 2024-12-18T00:27:24.5867896Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:27:24.5868490Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2024-12-18T00:27:24.5868977Z ============== 1 failed, 11 passed, 11 skipped, 2 rerun in 1.63s =============== 2024-12-18T00:27:24.5869369Z Got exit code 1 2024-12-18T00:27:24.5869624Z Retrying single test... 2024-12-18T00:27:24.5870229Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-9807f4353ba10e73.xml 2024-12-18T00:27:24.5870930Z ============================= test session starts ============================== 2024-12-18T00:27:24.5871511Z platform linux -- Python 3.12.7, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.12/bin/python 2024-12-18T00:27:24.5872024Z cachedir: .pytest_cache 2024-12-18T00:27:24.5872716Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:27:24.5873394Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:27:24.5873716Z configfile: pytest.ini 2024-12-18T00:27:24.5874355Z 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 2024-12-18T00:27:24.5875139Z collecting ... collected 119 items / 118 deselected / 1 selected 2024-12-18T00:27:24.5875922Z stepcurrent: skipping 22 already run items. Running only test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint 2024-12-18T00:27:24.5876623Z Running 1 items in this shard 2024-12-18T00:27:24.5876825Z 2024-12-18T00:27:24.5877224Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint PASSED [0.4276s] [100%] 2024-12-18T00:27:24.5877682Z 2024-12-18T00:27:24.5878261Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-9807f4353ba10e73.xml - 2024-12-18T00:27:24.5879134Z ====================== 1 passed, 118 deselected in 0.46s ======================= 2024-12-18T00:27:24.5879524Z Got exit code 0 2024-12-18T00:27:24.5879879Z Test succeeeded in new process, continuing with the rest of the tests 2024-12-18T00:27:24.5880654Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-5b26bb002adfdcee.xml 2024-12-18T00:27:24.5881356Z ============================= test session starts ============================== 2024-12-18T00:27:24.5881936Z platform linux -- Python 3.12.7, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.12/bin/python 2024-12-18T00:27:24.5882463Z cachedir: .pytest_cache 2024-12-18T00:27:24.5883087Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:27:24.5883754Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:27:24.5884074Z configfile: pytest.ini 2024-12-18T00:27:24.5884706Z 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 2024-12-18T00:27:24.5885488Z collecting ... collected 119 items / 23 deselected / 96 selected 2024-12-18T00:27:24.5885934Z stepcurrent: skipping 23 already run items. 2024-12-18T00:27:24.5886287Z Running 96 items in this shard 2024-12-18T00:27:24.5886472Z 2024-12-18T00:27:24.5886767Z test_python_dispatch.py::TestPythonDispatch::test_all_same_mode PASSED [0.0554s] [ 1%] 2024-12-18T00:27:24.5887489Z test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr PASSED [0.1080s] [ 2%] 2024-12-18T00:27:24.5888180Z test_python_dispatch.py::TestPythonDispatch::test_basic PASSED [0.0545s] [ 3%] 2024-12-18T00:27:24.5888941Z test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode PASSED [0.0485s] [ 4%] 2024-12-18T00:27:24.5889770Z test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor PASSED [0.0176s] [ 5%] 2024-12-18T00:27:24.5890518Z test_python_dispatch.py::TestPythonDispatch::test_custom_autograd PASSED [0.2336s] [ 6%] 2024-12-18T00:27:24.5891295Z test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes PASSED [0.2044s] [ 7%] 2024-12-18T00:27:24.5892153Z test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path PASSED [0.0785s] [ 8%] 2024-12-18T00:27:24.5893006Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass PASSED [0.0420s] [ 9%] 2024-12-18T00:27:24.5893919Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass PASSED [0.1796s] [ 10%] 2024-12-18T00:27:24.5894882Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type PASSED [0.0700s] [ 11%] 2024-12-18T00:27:24.5895879Z test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once PASSED [0.0303s] [ 12%] 2024-12-18T00:27:24.5896748Z test_python_dispatch.py::TestPythonDispatch::test_device_slowpath PASSED [0.1207s] [ 13%] 2024-12-18T00:27:24.5897450Z test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath PASSED [0.1371s] [ 14%] 2024-12-18T00:27:24.5898188Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call PASSED [0.0400s] [ 15%] 2024-12-18T00:27:24.5898970Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg PASSED [0.0396s] [ 16%] 2024-12-18T00:27:24.5899786Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd PASSED [0.0731s] [ 17%] 2024-12-18T00:27:24.5900605Z test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode PASSED [0.0182s] [ 18%] 2024-12-18T00:27:24.5901506Z test_python_dispatch.py::TestPythonDispatch::test_exception_handling PASSED [0.0181s] [ 19%] 2024-12-18T00:27:24.5902232Z test_python_dispatch.py::TestPythonDispatch::test_fancy_strides PASSED [0.0497s] [ 20%] 2024-12-18T00:27:24.5902905Z test_python_dispatch.py::TestPythonDispatch::test_format PASSED [0.1250s] [ 21%] 2024-12-18T00:27:24.5903574Z test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode PASSED [0.0124s] [ 22%] 2024-12-18T00:27:24.5904266Z test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack PASSED [0.0121s] [ 23%] 2024-12-18T00:27:24.5905057Z test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass PASSED [0.0713s] [ 25%] 2024-12-18T00:27:24.5905824Z test_python_dispatch.py::TestPythonDispatch::test_invalid_ret PASSED [0.0775s] [ 26%] 2024-12-18T00:27:24.5906560Z test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path PASSED [0.2067s] [ 27%] 2024-12-18T00:27:24.5907282Z test_python_dispatch.py::TestPythonDispatch::test_kwarg_only PASSED [0.0359s] [ 28%] 2024-12-18T00:27:24.5908047Z test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default PASSED [0.0338s] [ 29%] 2024-12-18T00:27:24.5908911Z test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path PASSED [0.2133s] [ 30%] 2024-12-18T00:27:24.5909591Z test_python_dispatch.py::TestPythonDispatch::test_like PASSED [0.0269s] [ 31%] 2024-12-18T00:27:24.5910580Z test_python_dispatch.py::TestPythonDispatch::test_list_ret SKIPPED [0.0630s] (This test passed, maybe we can remove `test/dynamo_skips/TestPythonDispatch.test_list_ret`) [ 32%] 2024-12-18T00:27:24.5911645Z test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass PASSED [0.0828s] [ 33%] 2024-12-18T00:27:24.5912419Z test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes PASSED [0.0196s] [ 34%] 2024-12-18T00:27:24.5913222Z test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc PASSED [0.0254s] [ 35%] 2024-12-18T00:27:24.5914106Z test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata PASSED [0.0606s] [ 36%] 2024-12-18T00:27:24.5914925Z test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug PASSED [0.0221s] [ 37%] 2024-12-18T00:27:24.5915639Z test_python_dispatch.py::TestPythonDispatch::test_mode_detection PASSED [0.0117s] [ 38%] 2024-12-18T00:27:24.5916370Z test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass PASSED [0.0175s] [ 39%] 2024-12-18T00:27:24.5917139Z test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass PASSED [0.0499s] [ 40%] 2024-12-18T00:27:24.5917938Z test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode PASSED [0.0384s] [ 41%] 2024-12-18T00:27:24.5918720Z test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode PASSED [0.0323s] [ 42%] 2024-12-18T00:27:24.5919412Z test_python_dispatch.py::TestPythonDispatch::test_new_ones PASSED [0.0255s] [ 43%] 2024-12-18T00:27:24.5920489Z test_python_dispatch.py::TestPythonDispatch::test_none_wrapping W1218 00:27:20.134000 2117 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:27:24.5921891Z W1218 00:27:20.134000 2117 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] function: '__torch_dispatch__' (/var/lib/jenkins/workspace/test/test_python_dispatch.py:1940) 2024-12-18T00:27:24.5923079Z W1218 00:27:20.134000 2117 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] last reason: 4/0: GLOBAL_STATE changed: grad_mode 2024-12-18T00:27:24.5924084Z W1218 00:27:20.134000 2117 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:27:24.5925337Z W1218 00:27:20.134000 2117 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:27:24.5926211Z PASSED [1.2989s] [ 44%] 2024-12-18T00:27:24.5926745Z test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode PASSED [0.0375s] [ 45%] 2024-12-18T00:27:24.5927500Z test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list PASSED [0.0536s] [ 46%] 2024-12-18T00:27:24.5928191Z test_python_dispatch.py::TestPythonDispatch::test_out PASSED [0.0322s] [ 47%] 2024-12-18T00:27:24.5928860Z test_python_dispatch.py::TestPythonDispatch::test_produce_real_type PASSED [0.0323s] [ 48%] 2024-12-18T00:27:24.5929574Z test_python_dispatch.py::TestPythonDispatch::test_record_stream PASSED [0.0181s] [ 50%] 2024-12-18T00:27:24.5930409Z test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride PASSED [0.0753s] [ 51%] 2024-12-18T00:27:24.5931242Z test_python_dispatch.py::TestPythonDispatch::test_return_stream PASSED [0.0482s] [ 52%] 2024-12-18T00:27:24.5931915Z test_python_dispatch.py::TestPythonDispatch::test_set_data PASSED [0.0545s] [ 53%] 2024-12-18T00:27:24.5932630Z test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach PASSED [0.0196s] [ 54%] 2024-12-18T00:27:24.5933368Z test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path PASSED [0.2064s] [ 55%] 2024-12-18T00:27:24.5934119Z test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass PASSED [0.0386s] [ 56%] 2024-12-18T00:27:24.5934843Z test_python_dispatch.py::TestPythonDispatch::test_storage PASSED [0.0548s] [ 57%] 2024-12-18T00:27:24.5935624Z test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object PASSED [0.0529s] [ 58%] 2024-12-18T00:27:24.5936601Z test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path PASSED [0.2044s] [ 59%] 2024-12-18T00:27:24.5937392Z test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check PASSED [0.8275s] [ 60%] 2024-12-18T00:27:24.5938167Z test_python_dispatch.py::TestPythonDispatch::test_subclass_creation PASSED [0.0197s] [ 61%] 2024-12-18T00:27:24.5938906Z test_python_dispatch.py::TestPythonDispatch::test_subclass_priority PASSED [0.2121s] [ 62%] 2024-12-18T00:27:24.5939685Z test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path PASSED [0.1039s] [ 63%] 2024-12-18T00:27:24.5940528Z test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode PASSED [0.0610s] [ 64%] 2024-12-18T00:27:24.5941364Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic PASSED [0.0305s] [ 65%] 2024-12-18T00:27:24.5942216Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch PASSED [0.0366s] [ 66%] 2024-12-18T00:27:24.5943111Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority PASSED [0.0753s] [ 67%] 2024-12-18T00:27:24.5943985Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors PASSED [0.0360s] [ 68%] 2024-12-18T00:27:24.5944747Z test_python_dispatch.py::TestPythonDispatch::test_version PASSED [0.1024s] [ 69%] 2024-12-18T00:27:24.5945521Z test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch PASSED [0.0192s] [ 70%] 2024-12-18T00:27:24.5946373Z test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately PASSED [0.0181s] [ 71%] 2024-12-18T00:27:24.5947145Z test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes PASSED [0.0185s] [ 72%] 2024-12-18T00:27:24.5948049Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys PASSED [0.0718s] [ 73%] 2024-12-18T00:27:24.5949054Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype PASSED [0.1179s] [ 75%] 2024-12-18T00:27:24.5950036Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode PASSED [0.0221s] [ 76%] 2024-12-18T00:27:24.5950924Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes PASSED [0.0463s] [ 77%] 2024-12-18T00:27:24.5951655Z test_python_dispatch.py::TestPythonDispatcher::test_basic PASSED [0.0480s] [ 78%] 2024-12-18T00:27:24.5952376Z test_python_dispatch.py::TestPythonDispatcher::test_lstsq PASSED [0.1233s] [ 79%] 2024-12-18T00:27:24.5953411Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:27:24.5955767Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu SKIPPED [0.0130s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/139056 for platform(s) dynamo. 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.) [ 81%] 2024-12-18T00:27:24.5958197Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:27:24.5959796Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:27:24.5961394Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:27:24.5963003Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32 SKIPPED [0.0133s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:27:24.5964620Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:27:24.5966235Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:27:24.5967859Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:27:24.5969494Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32 SKIPPED [0.0134s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:27:24.5971197Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32 SKIPPED [0.0137s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:27:24.5972861Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32 SKIPPED [0.0134s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:27:24.5974483Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32 SKIPPED [0.0134s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:27:24.5976940Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu SKIPPED [0.0128s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142021 for platform(s) dynamo. 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.) [ 93%] 2024-12-18T00:27:24.5979320Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32 SKIPPED [0.0135s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:27:24.5980779Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32 SKIPPED [0.0134s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:27:24.5982861Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu /opt/conda/envs/py_3.12/lib/python3.12/site-packages/_pytest/unraisableexception.py:78: PytestUnraisableExceptionWarning: Exception ignored in: 2024-12-18T00:27:24.5984171Z 2024-12-18T00:27:24.5984294Z Traceback (most recent call last): 2024-12-18T00:27:24.5984932Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/multiprocessing/reductions.py", line 50, in __del__ 2024-12-18T00:27:24.5985596Z self._free_weak_ref(self.cdata) 2024-12-18T00:27:24.5985892Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:27:24.5986296Z AttributeError: 'StorageWeakRef' object has no attribute '_free_weak_ref' 2024-12-18T00:27:24.5986658Z 2024-12-18T00:27:24.5986885Z warnings.warn(pytest.PytestUnraisableExceptionWarning(msg)) 2024-12-18T00:27:24.5987305Z PASSED [0.2724s] [ 96%] 2024-12-18T00:27:24.5988120Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32 SKIPPED [0.0138s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:27:24.5989652Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32 SKIPPED [0.0136s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:27:24.5991115Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 SKIPPED [0.0136s] (Policy: we don't run OpInfo tests w/ Dynamo) [100%] 2024-12-18T00:27:24.5991891Z 2024-12-18T00:27:24.5992473Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-5b26bb002adfdcee.xml - 2024-12-18T00:27:24.5993365Z ================ 76 passed, 20 skipped, 23 deselected in 7.77s ================= 2024-12-18T00:27:24.5994207Z The following tests failed and then succeeded when run in a new process['test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint'] 2024-12-18T00:27:24.5994849Z 2024-12-18T00:27:24.5995274Z FINISHED PRINTING LOG FILE of test_python_dispatch 1/1 (test/test-reports/test_python_dispatch_1.1_3235abbc3b780f40_.log) 2024-12-18T00:27:24.5995798Z 2024-12-18T00:27:24.5996005Z Running test_mobile_optimizer 1/1 ... [2024-12-18 00:27:24.550247] 2024-12-18T00:27:24.5996429Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:27:24.5997487Z Executing ['/opt/conda/envs/py_3.12/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 00:27:24.550643] 2024-12-18T00:27:32.5758310Z 2024-12-18T00:27:32.5759327Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_d0661131986c0e81_.log 2024-12-18T00:27:32.5762652Z 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 2024-12-18T00:27:32.5765519Z 2024-12-18T00:27:32.5765729Z Running nn/test_convolution 1/1 ... [2024-12-18 00:27:32.575909] 2024-12-18T00:27:32.5766153Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:27:32.5767215Z Executing ['/opt/conda/envs/py_3.12/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 00:27:32.576237] 2024-12-18T00:32:21.1138652Z 2024-12-18T00:32:21.1140229Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_f702af882e782b14_.log 2024-12-18T00:32:21.1475769Z Running 588 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv1d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv3d, test/nn/test_convolution.py::TestConvolutionNN::test_mismatch_shape_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_nnpack_conv, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNN::test_thnn_conv_strided_padded_dilated, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose3d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_contig_wrong_stride_cudnn_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_no_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu, 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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 2024-12-18T00:32:21.1800684Z 2024-12-18T00:32:21.1800877Z Running test_nn 1/2 ... [2024-12-18 00:32:21.115017] 2024-12-18T00:32:21.1801255Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:32:21.1802277Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:32:21.115341] 2024-12-18T00:40:05.6173482Z 2024-12-18T00:40:05.6174622Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_0a22125e3dd5b85d_.log 2024-12-18T00:40:05.6724997Z Running 1051 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_cuda_double, 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_half, 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_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_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, 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_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, 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_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_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_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_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same2, 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_cuda, 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_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_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_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_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_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_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, 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_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_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_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_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_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, 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_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_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_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, 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_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, 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_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, 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_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_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_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_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_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_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, 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_Flatten_cuda, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, 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_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, 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_HuberLoss_no_batch_dim_mean, 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_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_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_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_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, 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_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_float, 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_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_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, 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_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_sum, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, 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_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, 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_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, 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_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, 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_cuda_double, 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_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_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, 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_margin_no_reduce, 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_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, 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_cuda_double, 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_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_cuda, 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_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, 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_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_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_dropout_state, 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_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, 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_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, 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_rmul, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, 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_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_sum_cuda_double, 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_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, 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_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, 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_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, 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_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_half, 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_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, 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_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_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_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_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_ones_in_pos_weights_are_the_same_as_none, 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_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, 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_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, 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_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_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_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, 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_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_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_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, 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_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, 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_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_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_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_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, 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_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_scalar, 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_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, 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_padding_list, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, 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_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, 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_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, 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_smoothl1loss_intergral_target, 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_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial, 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_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_to, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, 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_type, test/test_nn.py::TestNN::test_unflatten, 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_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_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::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, 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_CTCLoss_no_batch_dim_reduction_mean_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_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_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_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_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_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_simple_average_mixed_cpu_bfloat16, 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_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_2_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_cross_entropy_64bit_reduction_none_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_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_one_hot_target_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_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_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_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_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, 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_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_InstanceNorm2d_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_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_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_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_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_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_nll_loss_byte_target_matches_long_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_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_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_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, 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_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_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_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_True_mode_bicubic_memory_format0_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_True_align_corners_False_mode_bicubic_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_format1_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_int8_cpu_int8, 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_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_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_int32_cpu_int32, 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_bilinear_float32_cpu_float32, 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_uint8_cpu_uint8, 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_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_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_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_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_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_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_int64_cpu_int64, 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_int16_cpu_int16, 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_int64_cpu_int64, 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_int16_cpu_int16, 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_int16_cpu_int16, 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_nearest-exact_float32_cpu_float32, 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_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_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_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_format1_isize_20_osize_11_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_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_launch_config_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_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_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_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 2024-12-18T00:40:05.7280262Z 2024-12-18T00:40:05.7280451Z Running test_nn 2/2 ... [2024-12-18 00:40:05.619460] 2024-12-18T00:40:05.7281074Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:40:05.7282080Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nn.py', '--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 00:40:05.619773] 2024-12-18T00:47:02.7623691Z 2024-12-18T00:47:02.7624508Z test_nn 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_2.2_cc5ba6a9ba674b42_.log 2024-12-18T00:47:02.8229782Z Running 1128 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, 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_reduce, 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_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, 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_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, 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_scalar, 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_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad2, 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_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, 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_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, 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_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_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_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_strided, 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_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, 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_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_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_groups, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_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_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, 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_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_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, 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_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose3d, 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_half, 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_half, 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_half, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_max, 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_padding_idx_cuda, 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_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, 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_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, 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_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_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_half, 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_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_cuda_double, 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_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, 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_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, 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_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, 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_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, 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_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_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_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_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_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, 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_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_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_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_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, 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_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_ModuleDict, 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_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, 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_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, 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_cuda_float, 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_half, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, 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_NLLLoss2d_no_reduce, 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_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_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, 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_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_neg, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, 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_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, 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_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, 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_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_with_up_down, 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_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim, 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_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_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_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, 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_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, 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_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_Softplus_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_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_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_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, 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_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_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_affine_grid, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, 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_has_correct_grad_at_zero, 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_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_not_requiring_grad, 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_overwrite, 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_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, 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_weight_format, 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_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fold_invalid_arg, 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_nearest_neighbor_rounding_mode_consistency, 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_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, 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_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, 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_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, 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_nearest_1d, 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_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, 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_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, 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_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_layer_norm_eps, 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_weightStrided, 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_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_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_margin_ranking_loss_margin_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_to_argparse, test/test_nn.py::TestNN::test_mse_loss_size_warning, 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_native_channel_shuffle_return_alias_of_self, 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_pad_scalar_error, 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_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_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_lstm_check_device, 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_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, 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_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, 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_special_cuda, 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_train_errors_for_invalid_mode, 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_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, 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_zero_grad, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, 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_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_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, 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_float64, 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_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, 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_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_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_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_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_4_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_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_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_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_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_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_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_none_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_cudnn_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_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_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_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_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_True_affine_True_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_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_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_mish_inplace_overlap_cpu, 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_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_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_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_pad_cpu_float64, 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_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_smooth_l1_loss_bfloat16_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_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, 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_transformerencoderlayer_gelu_cpu_float32, 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_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_403_output_size_377_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_format1_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_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_True_mode_bicubic_memory_format0_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_int8_cpu_int8, 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_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_int64_cpu_int64, 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_float64_cpu_float64, 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_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_int64_cpu_int64, 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_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_int8_cpu_int8, 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_int16_cpu_int16, 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_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_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_int64_cpu_int64, 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_int32_cpu_int32, 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_nearest-exact_float64_cpu_float64, 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_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_int8_cpu_int8, 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_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_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_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_uint8_cpu_uint8, 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_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_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_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_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_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_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_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_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_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_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32 2024-12-18T00:47:02.8815623Z 2024-12-18T00:47:02.8815910Z Running test_multiprocessing_spawn 1/1 ... [2024-12-18 00:47:02.764956] 2024-12-18T00:47:02.8816431Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:47:02.8817514Z Executing ['/opt/conda/envs/py_3.12/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 00:47:02.765323] 2024-12-18T00:49:45.6565884Z 2024-12-18T00:49:45.6567594Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_41d370392500aa35_.log 2024-12-18T00:49:45.6582236Z 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 2024-12-18T00:49:45.6593493Z 2024-12-18T00:49:45.6593692Z Running test_overrides 1/1 ... [2024-12-18 00:49:45.656805] 2024-12-18T00:49:45.6594106Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:49:45.6595138Z Executing ['/opt/conda/envs/py_3.12/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 00:49:45.657201] 2024-12-18T00:54:20.6871234Z 2024-12-18T00:54:20.6872142Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_ec2dc2108c0d9604_.log 2024-12-18T00:54:20.7351950Z 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__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ilshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___imod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___imul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___index__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___int__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___invert__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ior__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___irshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___isub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ixor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___le__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___len__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___long__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___lshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___lt__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___matmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___mod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___mul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ne__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___nonzero__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___or__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___radd__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rand__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rdiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___reduce_ex__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___repr__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___reversed__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rfloordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rlshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmatmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ror__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rpow__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rrshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rsub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rxor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___setitem__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___setstate__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___sub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___truediv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___xor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__autocast_to_full_precision, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__autocast_to_reduced_precision, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__clear_non_serializable_cached_data, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__coalesced_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__dimI, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__dimV, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__is_view, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, 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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, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cond, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eig, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigh, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lu_factor_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_rank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_multi_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_pinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_qr, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_psi, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_u, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_v, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_w, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_sinc, 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, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, 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, 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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, 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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 2024-12-18T00:54:20.7817424Z 2024-12-18T00:54:20.7817714Z Running distributions/test_distributions 1/2 ... [2024-12-18 00:54:20.689042] 2024-12-18T00:54:20.7818224Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:54:20.7819357Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:54:20.689364] 2024-12-18T01:01:29.1635365Z 2024-12-18T01:01:29.1636810Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_3356c6265d741f49_.log 2024-12-18T01:01:29.1687786Z Running 130 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_underflow, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, 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_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_distribution_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_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_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_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_lkj_cholesky_log_prob, 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_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_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_log_prob, 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_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, 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_2d, 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_log_prob, 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_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_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_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_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, 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_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_tensor_params, 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_gumbel_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_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, 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_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, 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_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_transformed, 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_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_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, 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_mean, test/distributions/test_distributions.py::TestJit::test_sample 2024-12-18T01:01:29.1738136Z 2024-12-18T01:01:29.1738405Z Running distributions/test_distributions 2/2 ... [2024-12-18 01:01:29.163802] 2024-12-18T01:01:29.1738907Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:01:29.1740036Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_distributions.py', '--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 01:01:29.164140] 2024-12-18T01:09:35.4302227Z 2024-12-18T01:09:35.4303405Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_c7de4f3b10a8faf8_.log 2024-12-18T01:09:35.4341315Z Running 95 items in this shard: 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_gpu, 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_log_prob_and_entropy, 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_chi2_sample, 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_subclass_expand, 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_gumbel, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, 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_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal, 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_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_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, 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_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, 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_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, 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_wishart_shape, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, 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_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_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_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_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_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_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_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_infinite, 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_shape, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, 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_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, 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_variance_stddev, 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::TestValidation::test_invalid, 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_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2024-12-18T01:09:35.4378003Z 2024-12-18T01:09:35.4378212Z Running test_autoload_disable 1/1 ... [2024-12-18 01:09:35.430541] 2024-12-18T01:09:38.1175669Z running install 2024-12-18T01:09:38.1177091Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T01:09:38.1178580Z !! 2024-12-18T01:09:38.1178778Z 2024-12-18T01:09:38.1179007Z ******************************************************************************** 2024-12-18T01:09:38.1179668Z Please avoid running ``setup.py`` directly. 2024-12-18T01:09:38.1180428Z Instead, use pypa/build, pypa/installer or other 2024-12-18T01:09:38.1181122Z standards-based tools. 2024-12-18T01:09:38.1181481Z 2024-12-18T01:09:38.1182079Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T01:09:38.1183084Z ******************************************************************************** 2024-12-18T01:09:38.1183880Z 2024-12-18T01:09:38.1184022Z !! 2024-12-18T01:09:38.1184405Z self.initialize_options() 2024-12-18T01:09:38.1309435Z running build 2024-12-18T01:09:38.1309719Z running build_py 2024-12-18T01:09:38.1387748Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T01:09:38.1390446Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T01:09:38.1394283Z running build_ext 2024-12-18T01:09:38.2175334Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T01:09:38.2177013Z creating build/temp.linux-x86_64-cpython-312 2024-12-18T01:09:38.2183131Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T01:09:39.9085876Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T01:09:39.9087569Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T01:09:39.9089076Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-12-18T01:09:39.9090009Z from extension.cpp:1: 2024-12-18T01:09:39.9091460Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T01:09:39.9092311Z extension.cpp:45:53: required from here 2024-12-18T01:09:39.9093752Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T01:09:39.9097212Z 1539 | class class_ : public detail::generic_type { 2024-12-18T01:09:39.9097724Z | ^~~~~~ 2024-12-18T01:09:39.9100203Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T01:09:39.9102630Z extension.cpp:45:53: required from here 2024-12-18T01:09:39.9106148Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2024-12-18T01:09:39.9109878Z 1599 | with_internals([&](internals &internals) { 2024-12-18T01:09:39.9110298Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:09:39.9110830Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T01:09:39.9111632Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:09:39.9112097Z 1601 | : internals.registered_types_cpp; 2024-12-18T01:09:39.9112501Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:09:39.9112930Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T01:09:39.9113343Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:09:39.9113747Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T01:09:39.9114147Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:09:39.9114589Z 1604 | }); 2024-12-18T01:09:39.9114833Z | ~ 2024-12-18T01:09:39.9118004Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-12-18T01:09:40.3157499Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T01:09:40.3161980Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T01:09:41.5900184Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-12-18T01:09:41.9722438Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T01:09:41.9726885Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T01:09:43.2533600Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:09:43.2535071Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:09:43.2536548Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:09:43.2538352Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:09:43.2539246Z from rng_extension.cpp:6: 2024-12-18T01:09:43.2540078Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:09:43.2540866Z 1123 | # pragma unroll 2024-12-18T01:09:43.2541124Z | 2024-12-18T01:09:43.2541684Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T01:09:43.2542581Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:09:43.2543418Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:09:43.2544306Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:09:43.2545204Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:09:43.2545892Z from rng_extension.cpp:6: 2024-12-18T01:09:43.2546676Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:09:43.2547449Z 59 | #pragma unroll 2024-12-18T01:09:43.2547703Z | 2024-12-18T01:09:43.2548457Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:09:43.2549212Z 72 | #pragma unroll 2024-12-18T01:09:43.2549464Z | 2024-12-18T01:09:43.2550139Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:09:43.2550900Z 87 | #pragma unroll 2024-12-18T01:09:43.2551156Z | 2024-12-18T01:09:43.2551691Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T01:09:43.2552588Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:09:43.2553401Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:09:43.2554189Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:09:43.2555096Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:09:43.2555768Z from rng_extension.cpp:6: 2024-12-18T01:09:43.2556574Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:09:43.2557352Z 153 | #pragma unroll 2024-12-18T01:09:43.2557603Z | 2024-12-18T01:09:43.2560695Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-12-18T01:09:43.6575752Z running install_lib 2024-12-18T01:09:43.6658276Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T01:09:43.6751051Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T01:09:43.6842308Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T01:09:43.6942990Z running install_egg_info 2024-12-18T01:09:43.7113316Z running egg_info 2024-12-18T01:09:43.7182028Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T01:09:43.7185111Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T01:09:43.7187135Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T01:09:43.7188922Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T01:09:43.7264084Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:09:43.7273346Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:09:43.7274986Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2024-12-18T01:09:43.7277548Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-12-18T01:09:43.7283836Z running install_scripts 2024-12-18T01:09:47.0725687Z 2024-12-18T01:09:47.0726281Z Running tests... 2024-12-18T01:09:47.0726919Z ---------------------------------------------------------------------- 2024-12-18T01:09:47.1818549Z . 2024-12-18T01:09:47.1818947Z ---------------------------------------------------------------------- 2024-12-18T01:09:47.1819360Z Ran 1 test in 0.109s 2024-12-18T01:09:47.1819546Z 2024-12-18T01:09:47.1819643Z OK 2024-12-18T01:09:47.1819752Z 2024-12-18T01:09:47.1819861Z Generating XML reports... 2024-12-18T01:09:47.7633178Z Running doctests 1/1 ... [2024-12-18 01:09:47.762954] 2024-12-18T01:09:47.8684457Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-12-18T01:09:47.8684978Z Listing tests 2024-12-18T01:09:48.1970995Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py line=1496. 2024-12-18T01:09:48.1971901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.1972277Z 2024-12-18T01:09:48.1972424Z dim_order(ambiguity_check=False) -> tuple 2024-12-18T01:09:48.1972659Z 2024-12-18T01:09:48.1972894Z Returns the uniquely determined tuple of int describing the dim order or 2024-12-18T01:09:48.1973372Z physical layout of :attr:`self`. 2024-12-18T01:09:48.1973586Z 2024-12-18T01:09:48.1973814Z The dim order represents how dimensions are laid out in memory, 2024-12-18T01:09:48.1974301Z starting from the outermost to the innermost dimension. 2024-12-18T01:09:48.1974579Z 2024-12-18T01:09:48.1975064Z Note that the dim order may not always be uniquely determined. 2024-12-18T01:09:48.1975762Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2024-12-18T01:09:48.1976688Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2024-12-18T01:09:48.1977450Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2024-12-18T01:09:48.1978154Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2024-12-18T01:09:48.1978717Z Otherwise, it will raise TypeError. 2024-12-18T01:09:48.1978942Z 2024-12-18T01:09:48.1979029Z Args: 2024-12-18T01:09:48.1979592Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2024-12-18T01:09:48.1980028Z 2024-12-18T01:09:48.1980160Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2024-12-18T01:09:48.1980470Z (0, 1, 2, 3) 2024-12-18T01:09:48.1980786Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2024-12-18T01:09:48.1981154Z (0, 2, 1, 3) 2024-12-18T01:09:48.1981507Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2024-12-18T01:09:48.1981926Z (0, 2, 3, 1) 2024-12-18T01:09:48.1982173Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2024-12-18T01:09:48.1982490Z (0, 1, 2, 3) 2024-12-18T01:09:48.1982718Z >>> try: 2024-12-18T01:09:48.1983021Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2024-12-18T01:09:48.1983418Z ... except RuntimeError as e: 2024-12-18T01:09:48.1983702Z ... print(e) 2024-12-18T01:09:48.1984169Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2024-12-18T01:09:48.1984713Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2024-12-18T01:09:48.1985148Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2024-12-18T01:09:48.1985607Z ... ) # It can be mapped to contiguous format 2024-12-18T01:09:48.1985952Z (0, 1, 2, 3) 2024-12-18T01:09:48.1986171Z >>> try: 2024-12-18T01:09:48.1986493Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2024-12-18T01:09:48.1986904Z ... except TypeError as e: 2024-12-18T01:09:48.1987190Z ... print(e) 2024-12-18T01:09:48.1987568Z The ambiguity_check argument must be a bool or a list of memory formats. 2024-12-18T01:09:48.1988033Z .. warning:: 2024-12-18T01:09:48.1988444Z The dim_order tensor API is experimental and subject to change. 2024-12-18T01:09:48.1988765Z 2024-12-18T01:09:48.1989014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.1989378Z 2024-12-18T01:09:48.2543922Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=431. 2024-12-18T01:09:48.2544794Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2545431Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-12-18T01:09:48.2545792Z 2024-12-18T01:09:48.2545972Z This is helpful when you want to visualize data over some 2024-12-18T01:09:48.2546426Z range of inputs. See below for a plotting example. 2024-12-18T01:09:48.2546692Z 2024-12-18T01:09:48.2546879Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-12-18T01:09:48.2547341Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-12-18T01:09:48.2547842Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-12-18T01:09:48.2548395Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-12-18T01:09:48.2548865Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-12-18T01:09:48.2549293Z to the result shape. 2024-12-18T01:09:48.2549476Z 2024-12-18T01:09:48.2549601Z .. note:: 2024-12-18T01:09:48.2549900Z 0D inputs are treated equivalently to 1D inputs of a 2024-12-18T01:09:48.2550474Z single element. 2024-12-18T01:09:48.2550662Z 2024-12-18T01:09:48.2550759Z .. warning:: 2024-12-18T01:09:48.2551105Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-12-18T01:09:48.2551575Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-12-18T01:09:48.2551849Z 2024-12-18T01:09:48.2552017Z In the future `torch.meshgrid` will transition to 2024-12-18T01:09:48.2552386Z `indexing='xy'` as the default. 2024-12-18T01:09:48.2552616Z 2024-12-18T01:09:48.2552811Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-12-18T01:09:48.2553289Z this issue with the goal of migrating to NumPy's behavior. 2024-12-18T01:09:48.2553581Z 2024-12-18T01:09:48.2553788Z .. seealso:: 2024-12-18T01:09:48.2553937Z 2024-12-18T01:09:48.2554125Z :func:`torch.cartesian_prod` has the same effect but it 2024-12-18T01:09:48.2554547Z collects the data in a tensor of vectors. 2024-12-18T01:09:48.2554793Z 2024-12-18T01:09:48.2554881Z Args: 2024-12-18T01:09:48.2555281Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-12-18T01:09:48.2555832Z treated as tensors of size :math:`(1,)` automatically 2024-12-18T01:09:48.2556106Z 2024-12-18T01:09:48.2556317Z indexing: (str, optional): the indexing mode, either "xy" 2024-12-18T01:09:48.2556780Z or "ij", defaults to "ij". See warning for future changes. 2024-12-18T01:09:48.2557063Z 2024-12-18T01:09:48.2557230Z If "xy" is selected, the first dimension corresponds 2024-12-18T01:09:48.2557669Z to the cardinality of the second input and the second 2024-12-18T01:09:48.2558117Z dimension corresponds to the cardinality of the first 2024-12-18T01:09:48.2558497Z input. 2024-12-18T01:09:48.2558660Z 2024-12-18T01:09:48.2558810Z If "ij" is selected, the dimensions are in the same 2024-12-18T01:09:48.2559213Z order as the cardinality of the inputs. 2024-12-18T01:09:48.2559453Z 2024-12-18T01:09:48.2559555Z Returns: 2024-12-18T01:09:48.2559856Z seq (sequence of Tensors): If the input has :math:`N` 2024-12-18T01:09:48.2560296Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-12-18T01:09:48.2560760Z output will also have :math:`N` tensors, where each tensor 2024-12-18T01:09:48.2561191Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-12-18T01:09:48.2561425Z 2024-12-18T01:09:48.2561535Z Example:: 2024-12-18T01:09:48.2561676Z 2024-12-18T01:09:48.2561801Z >>> x = torch.tensor([1, 2, 3]) 2024-12-18T01:09:48.2562124Z >>> y = torch.tensor([4, 5, 6]) 2024-12-18T01:09:48.2562348Z 2024-12-18T01:09:48.2562540Z Observe the element-wise pairings across the grid, (1, 4), 2024-12-18T01:09:48.2562977Z (1, 5), ..., (3, 6). This is the same thing as the 2024-12-18T01:09:48.2563404Z cartesian product. 2024-12-18T01:09:48.2563958Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-12-18T01:09:48.2564534Z >>> grid_x 2024-12-18T01:09:48.2564904Z tensor([[1, 1, 1], 2024-12-18T01:09:48.2565337Z [2, 2, 2], 2024-12-18T01:09:48.2565730Z [3, 3, 3]]) 2024-12-18T01:09:48.2566006Z >>> grid_y 2024-12-18T01:09:48.2566253Z tensor([[4, 5, 6], 2024-12-18T01:09:48.2566590Z [4, 5, 6], 2024-12-18T01:09:48.2566864Z [4, 5, 6]]) 2024-12-18T01:09:48.2567052Z 2024-12-18T01:09:48.2567280Z This correspondence can be seen when these grids are 2024-12-18T01:09:48.2567689Z stacked properly. 2024-12-18T01:09:48.2568141Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-12-18T01:09:48.2568589Z ... torch.cartesian_prod(x, y)) 2024-12-18T01:09:48.2569069Z True 2024-12-18T01:09:48.2569222Z 2024-12-18T01:09:48.2569427Z `torch.meshgrid` is commonly used to produce a grid for 2024-12-18T01:09:48.2569857Z plotting. 2024-12-18T01:09:48.2570165Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-12-18T01:09:48.2570579Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-12-18T01:09:48.2570980Z >>> import matplotlib.pyplot as plt 2024-12-18T01:09:48.2571382Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-12-18T01:09:48.2571791Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-12-18T01:09:48.2572176Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-12-18T01:09:48.2572600Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-12-18T01:09:48.2573042Z >>> ax = plt.axes(projection='3d') 2024-12-18T01:09:48.2573481Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-12-18T01:09:48.2573855Z >>> plt.show() 2024-12-18T01:09:48.2574051Z 2024-12-18T01:09:48.2574209Z .. image:: ../_static/img/meshgrid.png 2024-12-18T01:09:48.2574531Z :width: 512 2024-12-18T01:09:48.2574685Z 2024-12-18T01:09:48.2574791Z 2024-12-18T01:09:48.2575194Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.2575604Z 2024-12-18T01:09:48.2576140Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=820. 2024-12-18T01:09:48.2577043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2577874Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-12-18T01:09:48.2578403Z 2024-12-18T01:09:48.2578555Z Returns the unique elements of the input tensor. 2024-12-18T01:09:48.2578880Z 2024-12-18T01:09:48.2579175Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-12-18T01:09:48.2579856Z this function also eliminates non-consecutive duplicate values. 2024-12-18T01:09:48.2580226Z 2024-12-18T01:09:48.2580469Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-12-18T01:09:48.2581133Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-12-18T01:09:48.2581837Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-12-18T01:09:48.2582420Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-12-18T01:09:48.2582776Z 2024-12-18T01:09:48.2582865Z Args: 2024-12-18T01:09:48.2583104Z input (Tensor): the input tensor 2024-12-18T01:09:48.2583546Z sorted (bool): Whether to sort the unique elements in ascending order 2024-12-18T01:09:48.2583995Z before returning as output. 2024-12-18T01:09:48.2584412Z return_inverse (bool): Whether to also return the indices for where 2024-12-18T01:09:48.2585028Z elements in the original input ended up in the returned unique list. 2024-12-18T01:09:48.2585592Z return_counts (bool): Whether to also return the counts for each unique 2024-12-18T01:09:48.2586025Z element. 2024-12-18T01:09:48.2586387Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-12-18T01:09:48.2586923Z unique of the flattened input is returned. Otherwise, each of the 2024-12-18T01:09:48.2587441Z tensors indexed by the given dimension is treated as one of the 2024-12-18T01:09:48.2587976Z elements to apply the unique operation upon. See examples for more 2024-12-18T01:09:48.2588543Z details. Default: ``None`` 2024-12-18T01:09:48.2588754Z 2024-12-18T01:09:48.2588863Z Returns: 2024-12-18T01:09:48.2589288Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-12-18T01:09:48.2589779Z 2024-12-18T01:09:48.2589975Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-12-18T01:09:48.2590439Z - **inverse_indices** (*Tensor*): (optional) if 2024-12-18T01:09:48.2590883Z :attr:`return_inverse` is True, there will be an additional 2024-12-18T01:09:48.2591393Z returned tensor (same shape as input) representing the indices 2024-12-18T01:09:48.2591917Z for where elements in the original input map to in the output; 2024-12-18T01:09:48.2592427Z otherwise, this function will only return a single tensor. 2024-12-18T01:09:48.2592844Z - **counts** (*Tensor*): (optional) if 2024-12-18T01:09:48.2593258Z :attr:`return_counts` is True, there will be an additional 2024-12-18T01:09:48.2593829Z returned tensor (same shape as output or output.size(dim), 2024-12-18T01:09:48.2594329Z if dim was specified) representing the number of occurrences 2024-12-18T01:09:48.2594765Z for each unique value or tensor. 2024-12-18T01:09:48.2594990Z 2024-12-18T01:09:48.2595100Z Example:: 2024-12-18T01:09:48.2595231Z 2024-12-18T01:09:48.2595502Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-12-18T01:09:48.2595929Z >>> output 2024-12-18T01:09:48.2596169Z tensor([1, 2, 3]) 2024-12-18T01:09:48.2596334Z 2024-12-18T01:09:48.2596471Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:09:48.2596951Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:09:48.2597398Z >>> output 2024-12-18T01:09:48.2597626Z tensor([1, 2, 3]) 2024-12-18T01:09:48.2597894Z >>> inverse_indices 2024-12-18T01:09:48.2598164Z tensor([0, 2, 1, 2]) 2024-12-18T01:09:48.2598342Z 2024-12-18T01:09:48.2598480Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:09:48.2598960Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:09:48.2599404Z >>> output 2024-12-18T01:09:48.2599645Z tensor([1, 2, 3]) 2024-12-18T01:09:48.2599918Z >>> inverse_indices 2024-12-18T01:09:48.2600192Z tensor([[0, 2], 2024-12-18T01:09:48.2600442Z [1, 2]]) 2024-12-18T01:09:48.2600599Z 2024-12-18T01:09:48.2600701Z >>> a = torch.tensor([ 2024-12-18T01:09:48.2600974Z ... [ 2024-12-18T01:09:48.2601211Z ... [1, 1, 0, 0], 2024-12-18T01:09:48.2601492Z ... [1, 1, 0, 0], 2024-12-18T01:09:48.2601768Z ... [0, 0, 1, 1], 2024-12-18T01:09:48.2602031Z ... ], 2024-12-18T01:09:48.2602261Z ... [ 2024-12-18T01:09:48.2602500Z ... [0, 0, 1, 1], 2024-12-18T01:09:48.2602781Z ... [0, 0, 1, 1], 2024-12-18T01:09:48.2603062Z ... [1, 1, 1, 1], 2024-12-18T01:09:48.2603337Z ... ], 2024-12-18T01:09:48.2603571Z ... [ 2024-12-18T01:09:48.2603817Z ... [1, 1, 0, 0], 2024-12-18T01:09:48.2604101Z ... [1, 1, 0, 0], 2024-12-18T01:09:48.2604370Z ... [0, 0, 1, 1], 2024-12-18T01:09:48.2604650Z ... ], 2024-12-18T01:09:48.2604886Z ... ]) 2024-12-18T01:09:48.2605021Z 2024-12-18T01:09:48.2605250Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-12-18T01:09:48.2605799Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-12-18T01:09:48.2606269Z >>> # each other, so one of them will be removed. 2024-12-18T01:09:48.2606615Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-12-18T01:09:48.2606933Z tensor(True) 2024-12-18T01:09:48.2607220Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-12-18T01:09:48.2607564Z >>> a_unique_dim0 2024-12-18T01:09:48.2607833Z tensor([[[0, 0, 1, 1], 2024-12-18T01:09:48.2608097Z [0, 0, 1, 1], 2024-12-18T01:09:48.2608456Z [1, 1, 1, 1]], 2024-12-18T01:09:48.2608756Z [[1, 1, 0, 0], 2024-12-18T01:09:48.2609076Z [1, 1, 0, 0], 2024-12-18T01:09:48.2609351Z [0, 0, 1, 1]]]) 2024-12-18T01:09:48.2609531Z 2024-12-18T01:09:48.2609745Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-12-18T01:09:48.2610183Z >>> # `a_unique_dim0`: 2024-12-18T01:09:48.2610495Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-12-18T01:09:48.2610824Z tensor(True) 2024-12-18T01:09:48.2611101Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-12-18T01:09:48.2611417Z tensor(True) 2024-12-18T01:09:48.2611576Z 2024-12-18T01:09:48.2611780Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-12-18T01:09:48.2612355Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-12-18T01:09:48.2612766Z >>> # them will be removed. 2024-12-18T01:09:48.2613079Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-12-18T01:09:48.2613376Z tensor(True) 2024-12-18T01:09:48.2613644Z >>> torch.unique(a, dim=1) 2024-12-18T01:09:48.2613948Z tensor([[[0, 0, 1, 1], 2024-12-18T01:09:48.2614224Z [1, 1, 0, 0]], 2024-12-18T01:09:48.2614494Z [[1, 1, 1, 1], 2024-12-18T01:09:48.2614751Z [0, 0, 1, 1]], 2024-12-18T01:09:48.2615022Z [[0, 0, 1, 1], 2024-12-18T01:09:48.2615291Z [1, 1, 0, 0]]]) 2024-12-18T01:09:48.2615469Z 2024-12-18T01:09:48.2615691Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-12-18T01:09:48.2616185Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-12-18T01:09:48.2616648Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-12-18T01:09:48.2617044Z >>> # sub-tensors will be removed. 2024-12-18T01:09:48.2617372Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-12-18T01:09:48.2617683Z tensor(True) 2024-12-18T01:09:48.2617944Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-12-18T01:09:48.2618254Z tensor(True) 2024-12-18T01:09:48.2618503Z >>> torch.unique(a, dim=2) 2024-12-18T01:09:48.2618803Z tensor([[[0, 1], 2024-12-18T01:09:48.2619062Z [0, 1], 2024-12-18T01:09:48.2619316Z [1, 0]], 2024-12-18T01:09:48.2619572Z [[1, 0], 2024-12-18T01:09:48.2619810Z [1, 0], 2024-12-18T01:09:48.2620067Z [1, 1]], 2024-12-18T01:09:48.2620322Z [[0, 1], 2024-12-18T01:09:48.2620571Z [0, 1], 2024-12-18T01:09:48.2620810Z [1, 0]]]) 2024-12-18T01:09:48.2621058Z 2024-12-18T01:09:48.2621432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.2621808Z 2024-12-18T01:09:48.2735665Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-12-18T01:09:48.2736624Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2737001Z 2024-12-18T01:09:48.2737173Z Load a model from a github repo or a local directory. 2024-12-18T01:09:48.2737439Z 2024-12-18T01:09:48.2737676Z Note: Loading a model is the typical use case, but this can also be used to 2024-12-18T01:09:48.2738224Z for loading other objects such as tokenizers, loss functions, etc. 2024-12-18T01:09:48.2738556Z 2024-12-18T01:09:48.2738724Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-12-18T01:09:48.2739177Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-12-18T01:09:48.2739564Z ref (a tag or a branch). 2024-12-18T01:09:48.2739732Z 2024-12-18T01:09:48.2739917Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-12-18T01:09:48.2740307Z path to a local directory. 2024-12-18T01:09:48.2740482Z 2024-12-18T01:09:48.2740568Z Args: 2024-12-18T01:09:48.2740978Z repo_or_dir (str): If ``source`` is 'github', 2024-12-18T01:09:48.2741513Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-12-18T01:09:48.2742380Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-12-18T01:09:48.2743212Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-12-18T01:09:48.2743851Z If ``source`` is 'local' then it should be a path to a local directory. 2024-12-18T01:09:48.2744372Z model (str): the name of a callable (entrypoint) defined in the 2024-12-18T01:09:48.2744833Z repo/dir's ``hubconf.py``. 2024-12-18T01:09:48.2745256Z *args (optional): the corresponding args for callable ``model``. 2024-12-18T01:09:48.2745904Z source (str, optional): 'github' or 'local'. Specifies how 2024-12-18T01:09:48.2746436Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-12-18T01:09:48.2747047Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-12-18T01:09:48.2747814Z This parameter was introduced in v1.12 and helps ensuring that users 2024-12-18T01:09:48.2748375Z only run code from repos that they trust. 2024-12-18T01:09:48.2748629Z 2024-12-18T01:09:48.2748827Z - If ``False``, a prompt will ask the user whether the repo should 2024-12-18T01:09:48.2749237Z be trusted. 2024-12-18T01:09:48.2749590Z - If ``True``, the repo will be added to the trusted list and loaded 2024-12-18T01:09:48.2750038Z without requiring explicit confirmation. 2024-12-18T01:09:48.2750448Z - If ``"check"``, the repo will be checked against the list of 2024-12-18T01:09:48.2750954Z trusted repos in the cache. If it is not present in that list, the 2024-12-18T01:09:48.2751483Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-12-18T01:09:48.2751984Z - If ``None``: this will raise a warning, inviting the user to set 2024-12-18T01:09:48.2752486Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-12-18T01:09:48.2753010Z is only present for backward compatibility and will be removed in 2024-12-18T01:09:48.2753418Z v2.0. 2024-12-18T01:09:48.2753572Z 2024-12-18T01:09:48.2753780Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-12-18T01:09:48.2754324Z force_reload (bool, optional): whether to force a fresh download of 2024-12-18T01:09:48.2754854Z the github repo unconditionally. Does not have any effect if 2024-12-18T01:09:48.2755293Z ``source = 'local'``. Default is ``False``. 2024-12-18T01:09:48.2755729Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-12-18T01:09:48.2756273Z local caches. Note that the message about first download cannot be 2024-12-18T01:09:48.2756775Z muted. Does not have any effect if ``source = 'local'``. 2024-12-18T01:09:48.2757171Z Default is ``True``. 2024-12-18T01:09:48.2757651Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-12-18T01:09:48.2758356Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-12-18T01:09:48.2759017Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-12-18T01:09:48.2759591Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-12-18T01:09:48.2760111Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-12-18T01:09:48.2760457Z 2024-12-18T01:09:48.2760547Z Returns: 2024-12-18T01:09:48.2760863Z The output of the ``model`` callable when called with the given 2024-12-18T01:09:48.2761275Z ``*args`` and ``**kwargs``. 2024-12-18T01:09:48.2761455Z 2024-12-18T01:09:48.2761545Z Example: 2024-12-18T01:09:48.2761805Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:09:48.2762154Z >>> # from a github repo 2024-12-18T01:09:48.2762534Z >>> repo = "pytorch/vision" 2024-12-18T01:09:48.2762832Z >>> model = torch.hub.load( 2024-12-18T01:09:48.2763200Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-12-18T01:09:48.2763588Z ... ) 2024-12-18T01:09:48.2763820Z >>> # from a local directory 2024-12-18T01:09:48.2764149Z >>> path = "/some/local/path/pytorch/vision" 2024-12-18T01:09:48.2764491Z >>> # xdoctest: +SKIP 2024-12-18T01:09:48.2764895Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-12-18T01:09:48.2765268Z 2024-12-18T01:09:48.2765519Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.2765898Z 2024-12-18T01:09:48.2766452Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687. 2024-12-18T01:09:48.2767305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2767837Z Download object at the given URL to a local path. 2024-12-18T01:09:48.2768091Z 2024-12-18T01:09:48.2768194Z Args: 2024-12-18T01:09:48.2768446Z url (str): URL of the object to download 2024-12-18T01:09:48.2768902Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-12-18T01:09:48.2769586Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-12-18T01:09:48.2770136Z Default: None 2024-12-18T01:09:48.2770558Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-12-18T01:09:48.2771029Z Default: True 2024-12-18T01:09:48.2771193Z 2024-12-18T01:09:48.2771294Z Example: 2024-12-18T01:09:48.2771560Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:09:48.2771926Z >>> # xdoctest: +REQUIRES(POSIX) 2024-12-18T01:09:48.2772267Z >>> torch.hub.download_url_to_file( 2024-12-18T01:09:48.2772731Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-12-18T01:09:48.2773182Z ... "/tmp/temporary_file", 2024-12-18T01:09:48.2773474Z ... ) 2024-12-18T01:09:48.2773613Z 2024-12-18T01:09:48.2773696Z 2024-12-18T01:09:48.2774063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.2774427Z 2024-12-18T01:09:48.2774933Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812. 2024-12-18T01:09:48.2775799Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2776332Z Loads the Torch serialized object at the given URL. 2024-12-18T01:09:48.2776591Z 2024-12-18T01:09:48.2776776Z If downloaded file is a zip file, it will be automatically 2024-12-18T01:09:48.2777165Z decompressed. 2024-12-18T01:09:48.2777322Z 2024-12-18T01:09:48.2777534Z If the object is already present in `model_dir`, it's deserialized and 2024-12-18T01:09:48.2777961Z returned. 2024-12-18T01:09:48.2778309Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-12-18T01:09:48.2778826Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-12-18T01:09:48.2779143Z 2024-12-18T01:09:48.2779230Z Args: 2024-12-18T01:09:48.2779483Z url (str): URL of the object to download 2024-12-18T01:09:48.2779918Z model_dir (str, optional): directory in which to save the object 2024-12-18T01:09:48.2780582Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-12-18T01:09:48.2781305Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-12-18T01:09:48.2781770Z Default: True 2024-12-18T01:09:48.2782269Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-12-18T01:09:48.2783023Z ``filename-.ext`` where ```` is the first eight or more 2024-12-18T01:09:48.2783590Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-12-18T01:09:48.2784132Z ensure unique names and to verify the contents of the file. 2024-12-18T01:09:48.2784542Z Default: False 2024-12-18T01:09:48.2785041Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-12-18T01:09:48.2785812Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-12-18T01:09:48.2786511Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-12-18T01:09:48.2786895Z 2024-12-18T01:09:48.2786988Z Example: 2024-12-18T01:09:48.2787316Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:09:48.2787721Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-12-18T01:09:48.2788196Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-12-18T01:09:48.2788715Z ... ) 2024-12-18T01:09:48.2788860Z 2024-12-18T01:09:48.2788948Z 2024-12-18T01:09:48.2789322Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.2789687Z 2024-12-18T01:09:48.2818507Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=360. 2024-12-18T01:09:48.2819361Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:48.2819954Z Registers the function implementation as the fallback for the given key. 2024-12-18T01:09:48.2820316Z 2024-12-18T01:09:48.2820541Z This function only works for a library with global namespace ("_"). 2024-12-18T01:09:48.2820882Z 2024-12-18T01:09:48.2820970Z Args: 2024-12-18T01:09:48.2821383Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-12-18T01:09:48.2821900Z to register a fallthrough. 2024-12-18T01:09:48.2822449Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-12-18T01:09:48.2823046Z the dispatch key that the library was created with. 2024-12-18T01:09:48.2823727Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-12-18T01:09:48.2824539Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-12-18T01:09:48.2824994Z 2024-12-18T01:09:48.2825104Z Example:: 2024-12-18T01:09:48.2825370Z >>> my_lib = Library("_", "IMPL") 2024-12-18T01:09:48.2825738Z >>> def fallback_kernel(op, *args, **kwargs): 2024-12-18T01:09:48.2826123Z >>> # Handle all autocast ops generically 2024-12-18T01:09:48.2826451Z >>> # ... 2024-12-18T01:09:48.2826773Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:09:48.2827129Z 2024-12-18T01:09:48.2827878Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-12-18T01:09:48.2828691Z 2024-12-18T01:09:48.2828846Z my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:09:48.2829173Z ^ 2024-12-18T01:09:48.2885249Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=725. 2024-12-18T01:09:48.2886093Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:48.2886699Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-12-18T01:09:48.2887038Z 2024-12-18T01:09:48.2887233Z Also sometimes known as a "meta kernel", "abstract impl". 2024-12-18T01:09:48.2887521Z 2024-12-18T01:09:48.2887780Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-12-18T01:09:48.2889044Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-12-18T01:09:48.2889615Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-12-18T01:09:48.2890113Z what the properties of the output Tensors are. 2024-12-18T01:09:48.2890375Z 2024-12-18T01:09:48.2890608Z The FakeTensor implementation has the same signature as the operator. 2024-12-18T01:09:48.2891190Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-12-18T01:09:48.2891734Z implementation, assume that all Tensor inputs to the operator are 2024-12-18T01:09:48.2892276Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-12-18T01:09:48.2892889Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-12-18T01:09:48.2893434Z The FakeTensor implementation must consist of only PyTorch operations 2024-12-18T01:09:48.2893984Z (and may not directly access the storage or data of any input or 2024-12-18T01:09:48.2894397Z intermediate Tensors). 2024-12-18T01:09:48.2894635Z 2024-12-18T01:09:48.2894868Z This API may be used as a decorator (see examples). 2024-12-18T01:09:48.2895226Z 2024-12-18T01:09:48.2895392Z For a detailed guide on custom ops, please see 2024-12-18T01:09:48.2896121Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-12-18T01:09:48.2896467Z 2024-12-18T01:09:48.2896560Z Examples: 2024-12-18T01:09:48.2896797Z >>> import torch 2024-12-18T01:09:48.2897078Z >>> import numpy as np 2024-12-18T01:09:48.2897378Z >>> from torch import Tensor 2024-12-18T01:09:48.2897677Z >>> 2024-12-18T01:09:48.2897994Z >>> # Example 1: an operator without data-dependent output shape 2024-12-18T01:09:48.2898524Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-12-18T01:09:48.2899076Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-12-18T01:09:48.2899607Z >>> raise NotImplementedError("Implementation goes here") 2024-12-18T01:09:48.2899992Z >>> 2024-12-18T01:09:48.2900292Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-12-18T01:09:48.2900669Z >>> def _(x, weight, bias): 2024-12-18T01:09:48.2900977Z >>> assert x.dim() == 2 2024-12-18T01:09:48.2901293Z >>> assert weight.dim() == 2 2024-12-18T01:09:48.2901621Z >>> assert bias.dim() == 1 2024-12-18T01:09:48.2901964Z >>> assert x.shape[1] == weight.shape[1] 2024-12-18T01:09:48.2902324Z >>> assert weight.shape[0] == bias.shape[0] 2024-12-18T01:09:48.2902692Z >>> assert x.device == weight.device 2024-12-18T01:09:48.2903017Z >>> 2024-12-18T01:09:48.2903266Z >>> return (x @ weight.t()) + bias 2024-12-18T01:09:48.2903580Z >>> 2024-12-18T01:09:48.2903866Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-12-18T01:09:48.2904267Z >>> x = torch.randn(2, 3) 2024-12-18T01:09:48.2904579Z >>> w = torch.randn(3, 3) 2024-12-18T01:09:48.2904886Z >>> b = torch.randn(3) 2024-12-18T01:09:48.2905225Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-12-18T01:09:48.2905554Z >>> 2024-12-18T01:09:48.2905785Z >>> assert y.shape == (2, 3) 2024-12-18T01:09:48.2906078Z >>> 2024-12-18T01:09:48.2906379Z >>> # Example 2: an operator with data-dependent output shape 2024-12-18T01:09:48.2906891Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-12-18T01:09:48.2907348Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-12-18T01:09:48.2907702Z >>> x_np = x.numpy(force=True) 2024-12-18T01:09:48.2908060Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-12-18T01:09:48.2908568Z >>> return torch.tensor(res, device=x.device) 2024-12-18T01:09:48.2908911Z >>> 2024-12-18T01:09:48.2909305Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-12-18T01:09:48.2909686Z >>> def _(x): 2024-12-18T01:09:48.2909995Z >>> # Number of nonzero-elements is data-dependent. 2024-12-18T01:09:48.2910419Z >>> # Since we cannot peek at the data in an fake impl, 2024-12-18T01:09:48.2910849Z >>> # we use the ctx object to construct a new symint that 2024-12-18T01:09:48.2911252Z >>> # represents the data-dependent size. 2024-12-18T01:09:48.2911601Z >>> ctx = torch.library.get_ctx() 2024-12-18T01:09:48.2911946Z >>> nnz = ctx.new_dynamic_size() 2024-12-18T01:09:48.2912274Z >>> shape = [nnz, x.dim()] 2024-12-18T01:09:48.2912640Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-12-18T01:09:48.2913065Z >>> return result 2024-12-18T01:09:48.2913327Z >>> 2024-12-18T01:09:48.2913642Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:09:48.2914033Z >>> 2024-12-18T01:09:48.2914273Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-12-18T01:09:48.2914747Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-12-18T01:09:48.2915210Z >>> trace.print_readable() 2024-12-18T01:09:48.2915505Z >>> 2024-12-18T01:09:48.2925182Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-12-18T01:09:48.2925643Z 2024-12-18T01:09:48.2925756Z 2024-12-18T01:09:48.2926408Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-12-18T01:09:48.2927035Z 2024-12-18T01:09:48.2927127Z _._ = None 2024-12-18T01:09:48.2927350Z ^ 2024-12-18T01:09:48.2927998Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=846. 2024-12-18T01:09:48.2928870Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2929406Z Register a backward formula for this custom op. 2024-12-18T01:09:48.2929659Z 2024-12-18T01:09:48.2929878Z In order for an operator to work with autograd, you need to register 2024-12-18T01:09:48.2930303Z a backward formula: 2024-12-18T01:09:48.2930667Z 1. You must tell us how to compute gradients during the backward pass 2024-12-18T01:09:48.2931117Z by providing us a "backward" function. 2024-12-18T01:09:48.2931559Z 2. If you need any values from the forward to compute gradients, you can 2024-12-18T01:09:48.2932037Z use `setup_context` to save values for backward. 2024-12-18T01:09:48.2932289Z 2024-12-18T01:09:48.2932531Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-12-18T01:09:48.2933083Z - ``grads`` is one or more gradients. The number of gradients matches 2024-12-18T01:09:48.2933514Z the number of outputs of the operator. 2024-12-18T01:09:48.2933966Z The ``ctx`` object is `the same ctx object `_ used by 2024-12-18T01:09:48.2934552Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-12-18T01:09:48.2935074Z same as :meth:`torch.autograd.Function.backward`. 2024-12-18T01:09:48.2935339Z 2024-12-18T01:09:48.2935565Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-12-18T01:09:48.2936308Z Please save quantities needed for backward onto the ``ctx`` object via 2024-12-18T01:09:48.2936877Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-12-18T01:09:48.2937436Z or assigning them as attributes of ``ctx``. If your custom op has 2024-12-18T01:09:48.2937974Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-12-18T01:09:48.2938510Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-12-18T01:09:48.2938814Z 2024-12-18T01:09:48.2939046Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-12-18T01:09:48.2939788Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-12-18T01:09:48.2940356Z not depend on or mutate global state. If you need a non-traceable backward, 2024-12-18T01:09:48.2940938Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-12-18T01:09:48.2941293Z 2024-12-18T01:09:48.2941512Z If you need different autograd behavior on different devices, then we 2024-12-18T01:09:48.2942382Z recommend creating two different custom operators, one for each device 2024-12-18T01:09:48.2943226Z that needs different behavior, and switching between them at runtime. 2024-12-18T01:09:48.2943746Z 2024-12-18T01:09:48.2943903Z Examples: 2024-12-18T01:09:48.2944145Z >>> import torch 2024-12-18T01:09:48.2944533Z >>> import numpy as np 2024-12-18T01:09:48.2944840Z >>> from torch import Tensor 2024-12-18T01:09:48.2945142Z >>> 2024-12-18T01:09:48.2945490Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-12-18T01:09:48.2945933Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-12-18T01:09:48.2946277Z >>> x_np = x.cpu().numpy() 2024-12-18T01:09:48.2946595Z >>> y_np = np.sin(x_np) 2024-12-18T01:09:48.2946964Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:09:48.2947330Z >>> 2024-12-18T01:09:48.2947605Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-12-18T01:09:48.2947972Z >>> x, = inputs 2024-12-18T01:09:48.2948336Z >>> ctx.save_for_backward(x) 2024-12-18T01:09:48.2948651Z >>> 2024-12-18T01:09:48.2948892Z >>> def backward(ctx, grad): 2024-12-18T01:09:48.2949194Z >>> x, = ctx.saved_tensors 2024-12-18T01:09:48.2949516Z >>> return grad * x.cos() 2024-12-18T01:09:48.2949816Z >>> 2024-12-18T01:09:48.2950082Z >>> torch.library.register_autograd( 2024-12-18T01:09:48.2950501Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-12-18T01:09:48.2950881Z ... ) 2024-12-18T01:09:48.2951104Z >>> 2024-12-18T01:09:48.2951359Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:09:48.2951702Z >>> y = numpy_sin(x) 2024-12-18T01:09:48.2952059Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:09:48.2952461Z >>> assert torch.allclose(grad_x, x.cos()) 2024-12-18T01:09:48.2952796Z >>> 2024-12-18T01:09:48.2953047Z >>> # Example with a keyword-only arg 2024-12-18T01:09:48.2953480Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:09:48.2953959Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-12-18T01:09:48.2954325Z >>> x_np = x.cpu().numpy() 2024-12-18T01:09:48.2954640Z >>> y_np = x_np * val 2024-12-18T01:09:48.2954999Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:09:48.2955365Z >>> 2024-12-18T01:09:48.2955724Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-12-18T01:09:48.2956189Z >>> ctx.val = keyword_only_inputs["val"] 2024-12-18T01:09:48.2956518Z >>> 2024-12-18T01:09:48.2956750Z >>> def backward(ctx, grad): 2024-12-18T01:09:48.2957070Z >>> return grad * ctx.val 2024-12-18T01:09:48.2957376Z >>> 2024-12-18T01:09:48.2957616Z >>> torch.library.register_autograd( 2024-12-18T01:09:48.2958029Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-12-18T01:09:48.2958419Z ... ) 2024-12-18T01:09:48.2958645Z >>> 2024-12-18T01:09:48.2958899Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:09:48.2959236Z >>> y = numpy_mul(x, val=3.14) 2024-12-18T01:09:48.2959626Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:09:48.2960095Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-12-18T01:09:48.2960479Z 2024-12-18T01:09:48.2960565Z 2024-12-18T01:09:48.2960938Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.2961306Z 2024-12-18T01:09:48.2961806Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1258. 2024-12-18T01:09:48.2962635Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.2963233Z Given an operator and some sample arguments, tests if the operator is 2024-12-18T01:09:48.2963658Z registered correctly. 2024-12-18T01:09:48.2963846Z 2024-12-18T01:09:48.2964059Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-12-18T01:09:48.2964691Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-12-18T01:09:48.2965284Z and these APIs require that the functions you pass them satisfy certain 2024-12-18T01:09:48.2965857Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-12-18T01:09:48.2966357Z ``opcheck`` tests these metadata and properties. 2024-12-18T01:09:48.2966617Z 2024-12-18T01:09:48.2966739Z Concretely, we test the following: 2024-12-18T01:09:48.2966966Z 2024-12-18T01:09:48.2967145Z - test_schema: If the schema matches the implementation of 2024-12-18T01:09:48.2967662Z the operator. For example: if the schema specifies a Tensor is mutated, 2024-12-18T01:09:48.2968216Z then we check the implementation mutates the Tensor. If the schema 2024-12-18T01:09:48.2968723Z specifies that we return a new Tensor, then we check that the 2024-12-18T01:09:48.2969252Z implementation returns a new Tensor (instead of an existing one or 2024-12-18T01:09:48.2969700Z a view of an existing one). 2024-12-18T01:09:48.2970115Z - test_autograd_registration: If the operator supports training 2024-12-18T01:09:48.2970630Z (autograd): we check that its autograd formula is registered via 2024-12-18T01:09:48.2971158Z torch.library.register_autograd or a manual registration to one 2024-12-18T01:09:48.2971680Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2024-12-18T01:09:48.2972139Z registrations may lead to undefined behavior. 2024-12-18T01:09:48.2972579Z - test_faketensor: If the operator has a FakeTensor kernel 2024-12-18T01:09:48.2973049Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-12-18T01:09:48.2973561Z but not sufficient) for the operator to work with PyTorch compilation 2024-12-18T01:09:48.2974102Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2024-12-18T01:09:48.2974633Z (also sometimes known as a meta kernel) was registered for the 2024-12-18T01:09:48.2975210Z operator and that it is correct. This test takes the result of 2024-12-18T01:09:48.2975724Z running the operator on real tensors and the result of running 2024-12-18T01:09:48.2976232Z the operator on FakeTensors and checks that they have the same 2024-12-18T01:09:48.2976707Z Tensor metadata (sizes/strides/dtype/device/etc). 2024-12-18T01:09:48.2977170Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-12-18T01:09:48.2977672Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-12-18T01:09:48.2978173Z This checks that the outputs (and gradients, if applicable) are the 2024-12-18T01:09:48.2978652Z same under eager-mode PyTorch and torch.compile. 2024-12-18T01:09:48.2979108Z This test is a superset of ``test_faketensor`` and is an e2e test; 2024-12-18T01:09:48.2979570Z other things it tests are that the operator supports 2024-12-18T01:09:48.2980066Z functionalization and that the backward pass (if it exists) also 2024-12-18T01:09:48.2980539Z supports FakeTensor and functionalization. 2024-12-18T01:09:48.2980794Z 2024-12-18T01:09:48.2980993Z For best results, please call ``opcheck`` multiple times with a 2024-12-18T01:09:48.2981488Z representative set of inputs. If your operator supports 2024-12-18T01:09:48.2982090Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-12-18T01:09:48.2982658Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-12-18T01:09:48.2983167Z use ``opcheck`` with inputs on all supported devices. 2024-12-18T01:09:48.2983447Z 2024-12-18T01:09:48.2983535Z Args: 2024-12-18T01:09:48.2983842Z op: The operator. Must either be a function decorated with 2024-12-18T01:09:48.2984351Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-12-18T01:09:48.2984907Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-12-18T01:09:48.2985338Z args: The args to the operator 2024-12-18T01:09:48.2985789Z kwargs: The kwargs to the operator 2024-12-18T01:09:48.2986200Z test_utils: Tests that we should run. Default: all of them. 2024-12-18T01:09:48.2986636Z Example: ("test_schema", "test_faketensor") 2024-12-18T01:09:48.2987087Z raise_exception: If we should raise an exception on the first 2024-12-18T01:09:48.2987554Z error. If False, we will return a dict with information 2024-12-18T01:09:48.2987962Z on if each test passed or not. 2024-12-18T01:09:48.2988193Z 2024-12-18T01:09:48.2988384Z .. warning:: 2024-12-18T01:09:48.2988540Z 2024-12-18T01:09:48.2988758Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-12-18T01:09:48.2989311Z opcheck tests if your usage of torch.library APIs is correct while 2024-12-18T01:09:48.2989858Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-12-18T01:09:48.2990397Z mathematically correct. Use both to test custom ops that support 2024-12-18T01:09:48.2990846Z gradient computation. 2024-12-18T01:09:48.2991045Z 2024-12-18T01:09:48.2991135Z Example: 2024-12-18T01:09:48.2991276Z 2024-12-18T01:09:48.2991416Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:48.2991880Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:09:48.2992344Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2024-12-18T01:09:48.2992702Z >>> x_np = x.numpy(force=True) 2024-12-18T01:09:48.2993022Z >>> z_np = x_np * y 2024-12-18T01:09:48.2993349Z >>> return torch.from_numpy(z_np).to(x.device) 2024-12-18T01:09:48.2993692Z >>> 2024-12-18T01:09:48.2993927Z >>> @numpy_mul.register_fake 2024-12-18T01:09:48.2994220Z >>> def _(x, y): 2024-12-18T01:09:48.2994504Z >>> return torch.empty_like(x) 2024-12-18T01:09:48.2994815Z >>> 2024-12-18T01:09:48.2995073Z >>> def setup_context(ctx, inputs, output): 2024-12-18T01:09:48.2995416Z >>> y, = inputs 2024-12-18T01:09:48.2995673Z >>> ctx.y = y 2024-12-18T01:09:48.2995928Z >>> 2024-12-18T01:09:48.2996161Z >>> def backward(ctx, grad): 2024-12-18T01:09:48.2996482Z >>> return grad * ctx.y, None 2024-12-18T01:09:48.2996785Z >>> 2024-12-18T01:09:48.2997119Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2024-12-18T01:09:48.2997549Z >>> 2024-12-18T01:09:48.2997777Z >>> sample_inputs = [ 2024-12-18T01:09:48.2998071Z >>> (torch.randn(3), 3.14), 2024-12-18T01:09:48.2998417Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-12-18T01:09:48.2998801Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-12-18T01:09:48.2999256Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-12-18T01:09:48.2999664Z >>> ] 2024-12-18T01:09:48.2999873Z >>> 2024-12-18T01:09:48.3000109Z >>> for args in sample_inputs: 2024-12-18T01:09:48.3000466Z >>> torch.library.opcheck(numpy_mul, args) 2024-12-18T01:09:48.3000709Z 2024-12-18T01:09:48.3000805Z 2024-12-18T01:09:48.3001174Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.3001612Z 2024-12-18T01:09:48.3382506Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1226. 2024-12-18T01:09:48.3383422Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.3384137Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2024-12-18T01:09:48.3384579Z 2024-12-18T01:09:48.3384763Z Loads an object saved with :func:`torch.save` from a file. 2024-12-18T01:09:48.3385069Z 2024-12-18T01:09:48.3385307Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-12-18T01:09:48.3386163Z which underlie tensors, specially. They are first deserialized on the 2024-12-18T01:09:48.3386734Z CPU and are then moved to the device they were saved from. If this fails 2024-12-18T01:09:48.3387304Z (e.g. because the run time system doesn't have certain devices), an exception 2024-12-18T01:09:48.3387907Z is raised. However, storages can be dynamically remapped to an alternative 2024-12-18T01:09:48.3388495Z set of devices using the :attr:`map_location` argument. 2024-12-18T01:09:48.3388787Z 2024-12-18T01:09:48.3389033Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-12-18T01:09:48.3389625Z storage with two arguments: storage and location. The storage argument 2024-12-18T01:09:48.3390211Z will be the initial deserialization of the storage, residing on the CPU. 2024-12-18T01:09:48.3390782Z Each serialized storage has a location tag associated with it which 2024-12-18T01:09:48.3391337Z identifies the device it was saved from, and this tag is the second 2024-12-18T01:09:48.3391908Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-12-18T01:09:48.3392509Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-12-18T01:09:48.3393060Z :attr:`map_location` should return either ``None`` or a storage. If 2024-12-18T01:09:48.3393636Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-12-18T01:09:48.3394253Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-12-18T01:09:48.3394866Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-12-18T01:09:48.3395225Z 2024-12-18T01:09:48.3395471Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-12-18T01:09:48.3396051Z a device tag, it indicates the location where all tensors should be loaded. 2024-12-18T01:09:48.3396407Z 2024-12-18T01:09:48.3396671Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-12-18T01:09:48.3397262Z appearing in the file (keys), to ones that specify where to put the 2024-12-18T01:09:48.3397691Z storages (values). 2024-12-18T01:09:48.3397850Z 2024-12-18T01:09:48.3398088Z User extensions can register their own location tags and tagging and 2024-12-18T01:09:48.3398693Z deserialization methods using :func:`torch.serialization.register_package`. 2024-12-18T01:09:48.3399064Z 2024-12-18T01:09:48.3399152Z Args: 2024-12-18T01:09:48.3399601Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-12-18T01:09:48.3400216Z or a string or os.PathLike object containing a file name 2024-12-18T01:09:48.3400831Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-12-18T01:09:48.3401367Z locations 2024-12-18T01:09:48.3401756Z pickle_module: module used for unpickling metadata and objects (has to 2024-12-18T01:09:48.3402276Z match the :attr:`pickle_module` used to serialize file) 2024-12-18T01:09:48.3402775Z weights_only: Indicates whether unpickler should be restricted to 2024-12-18T01:09:48.3403422Z loading only tensors, primitive types, dictionaries 2024-12-18T01:09:48.3403924Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-12-18T01:09:48.3404405Z See :ref:`weights-only` for more details. 2024-12-18T01:09:48.3404979Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-12-18T01:09:48.3405775Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-12-18T01:09:48.3406568Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-12-18T01:09:48.3407413Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-12-18T01:09:48.3408100Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-12-18T01:09:48.3408704Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-12-18T01:09:48.3409296Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-12-18T01:09:48.3409730Z :attr:`errors=...`. 2024-12-18T01:09:48.3409923Z 2024-12-18T01:09:48.3410036Z .. warning:: 2024-12-18T01:09:48.3410394Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-12-18T01:09:48.3410924Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-12-18T01:09:48.3411516Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-12-18T01:09:48.3412138Z during unpickling. Never load data that could have come from an untrusted 2024-12-18T01:09:48.3412792Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-12-18T01:09:48.3413212Z 2024-12-18T01:09:48.3413304Z .. note:: 2024-12-18T01:09:48.3413699Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-12-18T01:09:48.3414338Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-12-18T01:09:48.3414987Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-12-18T01:09:48.3415372Z 2024-12-18T01:09:48.3415474Z .. note:: 2024-12-18T01:09:48.3415845Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-12-18T01:09:48.3416428Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-12-18T01:09:48.3416965Z when loading files saved by Python 2 in Python 3. If this default 2024-12-18T01:09:48.3417545Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-12-18T01:09:48.3418166Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-12-18T01:09:48.3418763Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-12-18T01:09:48.3419339Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-12-18T01:09:48.3419694Z 2024-12-18T01:09:48.3419784Z Example: 2024-12-18T01:09:48.3420054Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-12-18T01:09:48.3420443Z >>> torch.load("tensors.pt", weights_only=True) 2024-12-18T01:09:48.3420810Z # Load all tensors onto the CPU 2024-12-18T01:09:48.3421267Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-12-18T01:09:48.3421786Z # Load all tensors onto the CPU, using a function 2024-12-18T01:09:48.3422143Z >>> torch.load( 2024-12-18T01:09:48.3422553Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-12-18T01:09:48.3423010Z ... ) 2024-12-18T01:09:48.3423243Z # Load all tensors onto GPU 1 2024-12-18T01:09:48.3423550Z >>> torch.load( 2024-12-18T01:09:48.3423816Z ... "tensors.pt", 2024-12-18T01:09:48.3424249Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-12-18T01:09:48.3424634Z ... weights_only=True, 2024-12-18T01:09:48.3424939Z ... ) # type: ignore[attr-defined] 2024-12-18T01:09:48.3425281Z # Map tensors from GPU 1 to GPU 0 2024-12-18T01:09:48.3425747Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-12-18T01:09:48.3426234Z # Load tensor from io.BytesIO object 2024-12-18T01:09:48.3426714Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-12-18T01:09:48.3427210Z >>> with open("tensor.pt", "rb") as f: 2024-12-18T01:09:48.3427547Z ... buffer = io.BytesIO(f.read()) 2024-12-18T01:09:48.3427964Z >>> torch.load(buffer, weights_only=False) 2024-12-18T01:09:48.3428447Z # Load a module with 'ascii' encoding for unpickling 2024-12-18T01:09:48.3428968Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-12-18T01:09:48.3429541Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-12-18T01:09:48.3429935Z 2024-12-18T01:09:48.3430311Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.3430693Z 2024-12-18T01:09:48.4502981Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=21. 2024-12-18T01:09:48.4503891Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:48.4504462Z Check if there is an available :ref:`accelerator`. 2024-12-18T01:09:48.4504771Z 2024-12-18T01:09:48.4504878Z Returns: 2024-12-18T01:09:48.4505350Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2024-12-18T01:09:48.4505907Z 2024-12-18T01:09:48.4506020Z Example:: 2024-12-18T01:09:48.4506168Z 2024-12-18T01:09:48.4506440Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:48.4506935Z 2024-12-18T01:09:48.4507604Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2024-12-18T01:09:48.4508331Z 2024-12-18T01:09:48.4508603Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:48.4509085Z ^ 2024-12-18T01:09:48.4521761Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=110. 2024-12-18T01:09:48.4522807Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:48.4523457Z Wait for all kernels in all streams on the given device to complete. 2024-12-18T01:09:48.4523785Z 2024-12-18T01:09:48.4523874Z Args: 2024-12-18T01:09:48.4524374Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2024-12-18T01:09:48.4525105Z the current :ref:`accelerator` device type. If not given, 2024-12-18T01:09:48.4525696Z use :func:`torch.accelerator.current_device_idx` by default. 2024-12-18T01:09:48.4525996Z 2024-12-18T01:09:48.4526319Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2024-12-18T01:09:48.4526794Z 2024-12-18T01:09:48.4526889Z Example:: 2024-12-18T01:09:48.4527032Z 2024-12-18T01:09:48.4527226Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:48.4527748Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:48.4528351Z >>> start_event = torch.Event(enable_timing=True) 2024-12-18T01:09:48.4528800Z >>> end_event = torch.Event(enable_timing=True) 2024-12-18T01:09:48.4529147Z >>> start_event.record() 2024-12-18T01:09:48.4529869Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2024-12-18T01:09:48.4530399Z >>> sum = torch.sum(tensor) 2024-12-18T01:09:48.4530713Z >>> end_event.record() 2024-12-18T01:09:48.4531077Z >>> torch.accelerator.synchronize() 2024-12-18T01:09:48.4531489Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2024-12-18T01:09:48.4531906Z 2024-12-18T01:09:48.4532636Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2024-12-18T01:09:48.4533362Z 2024-12-18T01:09:48.4533621Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:48.4534149Z ^ 2024-12-18T01:09:48.4734449Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=343. 2024-12-18T01:09:48.4735389Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:48.4735933Z Retrieves the CUDA runtime API module. 2024-12-18T01:09:48.4736346Z 2024-12-18T01:09:48.4736351Z 2024-12-18T01:09:48.4736606Z This function initializes the CUDA runtime environment if it is not already 2024-12-18T01:09:48.4737271Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-12-18T01:09:48.4737905Z runtime API module provides access to various CUDA runtime functions. 2024-12-18T01:09:48.4738255Z 2024-12-18T01:09:48.4738344Z Args: 2024-12-18T01:09:48.4738609Z ``None`` 2024-12-18T01:09:48.4738748Z 2024-12-18T01:09:48.4738848Z Returns: 2024-12-18T01:09:48.4739116Z module: The CUDA runtime API module (_cudart). 2024-12-18T01:09:48.4739435Z 2024-12-18T01:09:48.4739531Z Raises: 2024-12-18T01:09:48.4739897Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-12-18T01:09:48.4740661Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-12-18T01:09:48.4741190Z 2024-12-18T01:09:48.4741337Z Example of CUDA operations with profiling: 2024-12-18T01:09:48.4741738Z >>> import torch 2024-12-18T01:09:48.4742032Z >>> from torch.cuda import cudart, check_error 2024-12-18T01:09:48.4742425Z >>> import os 2024-12-18T01:09:48.4742672Z >>> 2024-12-18T01:09:48.4742916Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-12-18T01:09:48.4743279Z >>> 2024-12-18T01:09:48.4743551Z >>> def perform_cuda_operations_with_streams(): 2024-12-18T01:09:48.4743972Z >>> stream = torch.cuda.Stream() 2024-12-18T01:09:48.4744320Z >>> with torch.cuda.stream(stream): 2024-12-18T01:09:48.4744737Z >>> x = torch.randn(100, 100, device='cuda') 2024-12-18T01:09:48.4745109Z >>> y = torch.randn(100, 100, device='cuda') 2024-12-18T01:09:48.4745491Z >>> z = torch.mul(x, y) 2024-12-18T01:09:48.4745799Z >>> return z 2024-12-18T01:09:48.4746058Z >>> 2024-12-18T01:09:48.4746343Z >>> torch.cuda.synchronize() 2024-12-18T01:09:48.4746690Z >>> print("====== Start nsys profiling ======") 2024-12-18T01:09:48.4747123Z >>> check_error(cudart().cudaProfilerStart()) 2024-12-18T01:09:48.4747514Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-12-18T01:09:48.4747981Z >>> result = perform_cuda_operations_with_streams() 2024-12-18T01:09:48.4748526Z >>> print("CUDA operations completed.") 2024-12-18T01:09:48.4748941Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-12-18T01:09:48.4749401Z >>> print("====== End nsys profiling ======") 2024-12-18T01:09:48.4749655Z 2024-12-18T01:09:48.4749862Z To run this example and save the profiling information, execute: 2024-12-18T01:09:48.4750602Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:09:48.4751261Z 2024-12-18T01:09:48.4751566Z This command profiles the CUDA operations in the provided script and saves 2024-12-18T01:09:48.4752132Z the profiling information to a file named `trace_name.prof`. 2024-12-18T01:09:48.4752755Z The `--profile-from-start off` option ensures that profiling starts only 2024-12-18T01:09:48.4753305Z after the `cudaProfilerStart` call in the script. 2024-12-18T01:09:48.4753849Z The `--csv` and `--print-summary` options format the profiling output as a 2024-12-18T01:09:48.4754330Z CSV file and print a summary, respectively. 2024-12-18T01:09:48.4754881Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-12-18T01:09:48.4755454Z overwrite of the output file if it already exists. 2024-12-18T01:09:48.4755812Z 2024-12-18T01:09:48.4756759Z 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)) 2024-12-18T01:09:48.4757606Z 2024-12-18T01:09:48.4757963Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:09:48.4758582Z ^ 2024-12-18T01:09:48.4860303Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-12-18T01:09:48.4861249Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.4861634Z 2024-12-18T01:09:48.4861880Z Append the given callback function to this ``Future``, which will be run 2024-12-18T01:09:48.4862447Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-12-18T01:09:48.4862970Z the same ``Future``, but the order in which they will be executed cannot 2024-12-18T01:09:48.4863504Z be guaranteed (to enforce a certain order consider chaining: 2024-12-18T01:09:48.4864017Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-12-18T01:09:48.4864560Z is the reference to this ``Future``. The callback function can use the 2024-12-18T01:09:48.4865098Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-12-18T01:09:48.4865653Z already completed, the given callback will be run immediately inline. 2024-12-18T01:09:48.4865990Z 2024-12-18T01:09:48.4866186Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-12-18T01:09:48.4866720Z callback might be invoked while the async kernels that are populating 2024-12-18T01:09:48.4867287Z those tensors haven't yet finished executing on the device. However, the 2024-12-18T01:09:48.4867841Z callback will be invoked with some dedicated streams set as current 2024-12-18T01:09:48.4868447Z (fetched from a global pool) which will be synchronized with those 2024-12-18T01:09:48.4869005Z kernels. Hence any operation performed by the callback on these tensors 2024-12-18T01:09:48.4869551Z will be scheduled on the device after the kernels complete. In other 2024-12-18T01:09:48.4870150Z words, as long as the callback doesn't switch streams, it can safely 2024-12-18T01:09:48.4870867Z manipulate the result without any additional synchronization. This is 2024-12-18T01:09:48.4871679Z similar to the non-blocking behavior of :meth:`wait`. 2024-12-18T01:09:48.4872021Z 2024-12-18T01:09:48.4872245Z Similarly, if the callback returns a value that contains tensors that 2024-12-18T01:09:48.4872770Z reside on a GPU, it can do so even if the kernels that are producing 2024-12-18T01:09:48.4873288Z these tensors are still running on the device, as long as the callback 2024-12-18T01:09:48.4873827Z didn't change streams during its execution. If one wants to change 2024-12-18T01:09:48.4874361Z streams, one must be careful to re-synchronize them with the original 2024-12-18T01:09:48.4874911Z streams, that is, those that were current when the callback was invoked. 2024-12-18T01:09:48.4875241Z 2024-12-18T01:09:48.4875339Z Args: 2024-12-18T01:09:48.4875657Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-12-18T01:09:48.4876295Z the only argument. 2024-12-18T01:09:48.4876525Z 2024-12-18T01:09:48.4876616Z Returns: 2024-12-18T01:09:48.4876912Z A new ``Future`` object that holds the return value of the 2024-12-18T01:09:48.4877378Z ``callback`` and will be marked as completed when the given 2024-12-18T01:09:48.4877777Z ``callback`` finishes. 2024-12-18T01:09:48.4877951Z 2024-12-18T01:09:48.4878134Z .. note:: Note that if the callback function throws, either 2024-12-18T01:09:48.4878630Z through the original future being completed with an exception and 2024-12-18T01:09:48.4879152Z calling ``fut.wait()``, or through other code in the callback, the 2024-12-18T01:09:48.4879758Z future returned by ``then`` will be marked appropriately with the 2024-12-18T01:09:48.4880279Z encountered error. However, if this callback later completes 2024-12-18T01:09:48.4880816Z additional futures, those futures are not marked as completed with 2024-12-18T01:09:48.4881353Z an error and the user is responsible for handling completion/waiting 2024-12-18T01:09:48.4881799Z on those futures independently. 2024-12-18T01:09:48.4882021Z 2024-12-18T01:09:48.4882117Z Example:: 2024-12-18T01:09:48.4882399Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:09:48.4882761Z >>> def callback(fut): 2024-12-18T01:09:48.4883068Z ... print(f"RPC return value is {fut.wait()}.") 2024-12-18T01:09:48.4883438Z >>> fut = torch.futures.Future() 2024-12-18T01:09:48.4883825Z >>> # The inserted callback will print the return value when 2024-12-18T01:09:48.4884236Z >>> # receiving the response from "worker1" 2024-12-18T01:09:48.4884580Z >>> cb_fut = fut.then(callback) 2024-12-18T01:09:48.4884884Z >>> chain_cb_fut = cb_fut.then( 2024-12-18T01:09:48.4885243Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-12-18T01:09:48.4885597Z ... ) 2024-12-18T01:09:48.4885820Z >>> fut.set_result(5) 2024-12-18T01:09:48.4886100Z RPC return value is 5. 2024-12-18T01:09:48.4886371Z Chained cb done. None 2024-12-18T01:09:48.4886552Z 2024-12-18T01:09:48.4886807Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.4887184Z 2024-12-18T01:09:48.4887736Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-12-18T01:09:48.4888631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.4889019Z 2024-12-18T01:09:48.4889225Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-12-18T01:09:48.4889766Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-12-18T01:09:48.4890219Z cannot be marked completed twice. 2024-12-18T01:09:48.4890425Z 2024-12-18T01:09:48.4890655Z If the result contains tensors that reside on GPUs, this method can be 2024-12-18T01:09:48.4891183Z called even if the asynchronous kernels that are populating those 2024-12-18T01:09:48.4891728Z tensors haven't yet completed running on the device, provided that the 2024-12-18T01:09:48.4892290Z streams on which those kernels were enqueued are set as the current ones 2024-12-18T01:09:48.4892846Z when this method is called. Put simply, it's safe to call this method 2024-12-18T01:09:48.4893391Z immediately after launching those kernels, without any additional 2024-12-18T01:09:48.4893936Z synchronization, as long as one doesn't change streams in between. This 2024-12-18T01:09:48.4894500Z method will record events on all the relevant current streams and will 2024-12-18T01:09:48.4895037Z use them to ensure proper scheduling for all the consumers of this 2024-12-18T01:09:48.4895447Z ``Future``. 2024-12-18T01:09:48.4895571Z 2024-12-18T01:09:48.4895676Z Args: 2024-12-18T01:09:48.4895961Z result (object): the result object of this ``Future``. 2024-12-18T01:09:48.4896230Z 2024-12-18T01:09:48.4896323Z Example:: 2024-12-18T01:09:48.4896684Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:09:48.4897058Z >>> import threading 2024-12-18T01:09:48.4897338Z >>> import time 2024-12-18T01:09:48.4897605Z >>> def slow_set_future(fut, value): 2024-12-18T01:09:48.4897919Z ... time.sleep(0.5) 2024-12-18T01:09:48.4898205Z ... fut.set_result(value) 2024-12-18T01:09:48.4898519Z >>> fut = torch.futures.Future() 2024-12-18T01:09:48.4898836Z >>> t = threading.Thread( 2024-12-18T01:09:48.4899138Z ... target=slow_set_future, 2024-12-18T01:09:48.4899445Z ... args=(fut, torch.ones(2) * 3) 2024-12-18T01:09:48.4899755Z ... ) 2024-12-18T01:09:48.4899973Z >>> t.start() 2024-12-18T01:09:48.4900223Z >>> print(fut.wait()) 2024-12-18T01:09:48.4900481Z tensor([3., 3.]) 2024-12-18T01:09:48.4900789Z >>> t.join() 2024-12-18T01:09:48.4900941Z 2024-12-18T01:09:48.4901196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.4901581Z 2024-12-18T01:09:48.4971025Z msg = Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/mps/__init__.py line=144. 2024-12-18T01:09:48.4972713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.4973823Z Compiles compute shader from source and allows one to invoke kernels 2024-12-18T01:09:48.4974705Z defined there from the comfort of Python runtime 2024-12-18T01:09:48.4975345Z Example:: 2024-12-18T01:09:48.4975596Z 2024-12-18T01:09:48.4975841Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2024-12-18T01:09:48.4976500Z >>> lib = torch.mps._compile_shader( 2024-12-18T01:09:48.4977645Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2024-12-18T01:09:48.4978730Z ... ) 2024-12-18T01:09:48.4979160Z >>> x = torch.zeros(16, device="mps") 2024-12-18T01:09:48.4979760Z >>> lib.full(x, 3.14) 2024-12-18T01:09:48.4980247Z 2024-12-18T01:09:48.4980910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.4981585Z 2024-12-18T01:09:48.5156269Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202. 2024-12-18T01:09:48.5157928Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:48.5158918Z Return the sum of each row of the given sparse tensor. 2024-12-18T01:09:48.5159402Z 2024-12-18T01:09:48.5159815Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-12-18T01:09:48.5160784Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-12-18T01:09:48.5161707Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-12-18T01:09:48.5162586Z returns a dense tensor instead of a sparse tensor. 2024-12-18T01:09:48.5163063Z 2024-12-18T01:09:48.5163544Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-12-18T01:09:48.5164624Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-12-18T01:09:48.5165200Z 2024-12-18T01:09:48.5165614Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-12-18T01:09:48.5166680Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-12-18T01:09:48.5167376Z 2024-12-18T01:09:48.5167520Z Args: 2024-12-18T01:09:48.5167953Z input (Tensor): the input sparse tensor 2024-12-18T01:09:48.5168879Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-12-18T01:09:48.5169783Z over all dims. 2024-12-18T01:09:48.5170569Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-12-18T01:09:48.5171428Z Default: dtype of :attr:`input`. 2024-12-18T01:09:48.5171837Z 2024-12-18T01:09:48.5171998Z Example:: 2024-12-18T01:09:48.5172665Z 2024-12-18T01:09:48.5172808Z >>> nnz = 3 2024-12-18T01:09:48.5173224Z >>> dims = [5, 5, 2, 3] 2024-12-18T01:09:48.5173838Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-12-18T01:09:48.5174603Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-12-18T01:09:48.5175360Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-12-18T01:09:48.5175949Z >>> size = torch.Size(dims) 2024-12-18T01:09:48.5176539Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:48.5177201Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-12-18T01:09:48.5177759Z >>> S 2024-12-18T01:09:48.5178161Z tensor(indices=tensor([[2, 0, 3], 2024-12-18T01:09:48.5178937Z [2, 4, 1]]), 2024-12-18T01:09:48.5179540Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-12-18T01:09:48.5180170Z [ 0.3411, 0.0918, -0.2312]], 2024-12-18T01:09:48.5180590Z 2024-12-18T01:09:48.5180771Z [[ 0.5348, 0.0634, -2.0494], 2024-12-18T01:09:48.5181368Z [-0.7125, -1.0646, 2.1844]], 2024-12-18T01:09:48.5181786Z 2024-12-18T01:09:48.5181975Z [[ 0.1276, 0.1874, -0.6334], 2024-12-18T01:09:48.5182575Z [-1.9682, -0.5340, 0.7483]]]), 2024-12-18T01:09:48.5183241Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:09:48.5183700Z 2024-12-18T01:09:48.5184062Z # when sum over only part of sparse_dims, return a sparse tensor 2024-12-18T01:09:48.5184827Z >>> torch.sparse.sum(S, [1, 3]) 2024-12-18T01:09:48.5185411Z tensor(indices=tensor([[0, 2, 3]]), 2024-12-18T01:09:48.5186021Z values=tensor([[-1.4512, 0.4073], 2024-12-18T01:09:48.5186608Z [-0.8901, 0.2017], 2024-12-18T01:09:48.5187171Z [-0.3183, -1.7539]]), 2024-12-18T01:09:48.5187807Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:09:48.5188344Z 2024-12-18T01:09:48.5188618Z # when sum over all sparse dim, return a dense tensor 2024-12-18T01:09:48.5189251Z # with summed dims squeezed 2024-12-18T01:09:48.5189818Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-12-18T01:09:48.5190387Z tensor([-2.6596, -1.1450]) 2024-12-18T01:09:48.5190868Z 2024-12-18T01:09:48.5191508Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:48.5192212Z 2024-12-18T01:09:49.0681225Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-12-18T01:09:49.0682994Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:49.0683724Z 2024-12-18T01:09:49.0684144Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-12-18T01:09:49.0685143Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-12-18T01:09:49.0686161Z pushes the map into PyTorch operations called by ``func``, effectively 2024-12-18T01:09:49.0686978Z vectorizing those operations. 2024-12-18T01:09:49.0687343Z 2024-12-18T01:09:49.0687741Z vmap is useful for handling batch dimensions: one can write a function 2024-12-18T01:09:49.0688732Z ``func`` that runs on examples and then lift it to a function that can 2024-12-18T01:09:49.0689719Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-12-18T01:09:49.0690648Z compute batched gradients when composed with autograd. 2024-12-18T01:09:49.0691165Z 2024-12-18T01:09:49.0691360Z .. note:: 2024-12-18T01:09:49.0691893Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-12-18T01:09:49.0692692Z convenience. Use whichever one you'd like. 2024-12-18T01:09:49.0693130Z 2024-12-18T01:09:49.0693294Z Args: 2024-12-18T01:09:49.0693887Z func (function): A Python function that takes one or more arguments. 2024-12-18T01:09:49.0695066Z Must return one or more Tensors. 2024-12-18T01:09:49.0695846Z in_dims (int or nested structure): Specifies which dimension of the 2024-12-18T01:09:49.0696757Z inputs should be mapped over. ``in_dims`` should have a 2024-12-18T01:09:49.0697659Z structure like the inputs. If the ``in_dim`` for a particular 2024-12-18T01:09:49.0698566Z input is None, then that indicates there is no map dimension. 2024-12-18T01:09:49.0699293Z Default: 0. 2024-12-18T01:09:49.0699923Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-12-18T01:09:49.0700868Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-12-18T01:09:49.0701737Z it should have one element per output. Default: 0. 2024-12-18T01:09:49.0702726Z randomness (str): Specifies whether the randomness in this 2024-12-18T01:09:49.0703660Z vmap should be the same or different across batches. If 'different', 2024-12-18T01:09:49.0704583Z the randomness for each batch will be different. If 'same', the 2024-12-18T01:09:49.0705537Z randomness will be the same across batches. If 'error', any calls to 2024-12-18T01:09:49.0706466Z random functions will error. Default: 'error'. WARNING: this flag 2024-12-18T01:09:49.0707471Z only applies to random PyTorch operations and does not apply to 2024-12-18T01:09:49.0708389Z Python's random module or numpy randomness. 2024-12-18T01:09:49.0709258Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-12-18T01:09:49.0710305Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-12-18T01:09:49.0711372Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-12-18T01:09:49.0712557Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-12-18T01:09:49.0713276Z 2024-12-18T01:09:49.0713431Z Returns: 2024-12-18T01:09:49.0713939Z Returns a new "batched" function. It takes the same inputs as 2024-12-18T01:09:49.0714834Z ``func``, except each input has an extra dimension at the index 2024-12-18T01:09:49.0715741Z specified by ``in_dims``. It takes returns the same outputs as 2024-12-18T01:09:49.0716643Z ``func``, except each output has an extra dimension at the index 2024-12-18T01:09:49.0717377Z specified by ``out_dims``. 2024-12-18T01:09:49.0717720Z 2024-12-18T01:09:49.0717886Z .. warning: 2024-12-18T01:09:49.0718461Z :func:`vmap` works best with functional-style code. Please do not 2024-12-18T01:09:49.0719342Z perform any side-effects in ``func``, with the exception of 2024-12-18T01:09:49.0720315Z in-place PyTorch operations. Examples of side-effects include mutating 2024-12-18T01:09:49.0721369Z Python data structures and assigning values to variables not captured 2024-12-18T01:09:49.0722185Z in ``func``. 2024-12-18T01:09:49.0722437Z 2024-12-18T01:09:49.0722873Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-12-18T01:09:49.0723911Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-12-18T01:09:49.0724907Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-12-18T01:09:49.0725541Z 2024-12-18T01:09:49.0725807Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:09:49.0726630Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-12-18T01:09:49.0727420Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:09:49.0727992Z >>> batched_dot(x, y) 2024-12-18T01:09:49.0728289Z 2024-12-18T01:09:49.0728693Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-12-18T01:09:49.0729463Z model authoring experience. 2024-12-18T01:09:49.0729837Z 2024-12-18T01:09:49.0730031Z >>> batch_size, feature_size = 3, 5 2024-12-18T01:09:49.0730724Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-12-18T01:09:49.0731618Z >>> 2024-12-18T01:09:49.0731980Z >>> def model(feature_vec): 2024-12-18T01:09:49.0732550Z >>> # Very simple linear model with activation 2024-12-18T01:09:49.0733221Z >>> return feature_vec.dot(weights).relu() 2024-12-18T01:09:49.0733796Z >>> 2024-12-18T01:09:49.0734232Z >>> examples = torch.randn(batch_size, feature_size) 2024-12-18T01:09:49.0734872Z >>> result = torch.vmap(model)(examples) 2024-12-18T01:09:49.0735293Z 2024-12-18T01:09:49.0735729Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-12-18T01:09:49.0737048Z or impossible to batch. One example is higher-order gradient computation. 2024-12-18T01:09:49.0738095Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-12-18T01:09:49.0739359Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-12-18T01:09:49.0740454Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-12-18T01:09:49.0741528Z we can vectorize the whole computation, computing the Jacobian in a single 2024-12-18T01:09:49.0742384Z call to ``autograd.grad``. 2024-12-18T01:09:49.0742724Z 2024-12-18T01:09:49.0742871Z >>> # Setup 2024-12-18T01:09:49.0743264Z >>> N = 5 2024-12-18T01:09:49.0743653Z >>> f = lambda x: x ** 2 2024-12-18T01:09:49.0744166Z >>> x = torch.randn(N, requires_grad=True) 2024-12-18T01:09:49.0744744Z >>> y = f(x) 2024-12-18T01:09:49.0745135Z >>> I_N = torch.eye(N) 2024-12-18T01:09:49.0745585Z >>> 2024-12-18T01:09:49.0745966Z >>> # Sequential approach 2024-12-18T01:09:49.0746656Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-12-18T01:09:49.0747457Z >>> for v in I_N.unbind()] 2024-12-18T01:09:49.0748072Z >>> jacobian = torch.stack(jacobian_rows) 2024-12-18T01:09:49.0748740Z >>> 2024-12-18T01:09:49.0749155Z >>> # vectorized gradient computation 2024-12-18T01:09:49.0749712Z >>> def get_vjp(v): 2024-12-18T01:09:49.0750212Z >>> return torch.autograd.grad(y, x, v) 2024-12-18T01:09:49.0750859Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-12-18T01:09:49.0751264Z 2024-12-18T01:09:49.0751756Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-12-18T01:09:49.0752466Z 2024-12-18T01:09:49.0752734Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:09:49.0753672Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-12-18T01:09:49.0754580Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-12-18T01:09:49.0755251Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-12-18T01:09:49.0755699Z 2024-12-18T01:09:49.0756146Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-12-18T01:09:49.0757090Z the dimension that each inputs are batched along as 2024-12-18T01:09:49.0757578Z 2024-12-18T01:09:49.0757832Z >>> torch.dot # [N], [N] -> [] 2024-12-18T01:09:49.0758686Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-12-18T01:09:49.0759532Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:09:49.0760398Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-12-18T01:09:49.0761078Z 2024-12-18T01:09:49.0761551Z If there are multiple inputs each of which is batched along different dimensions, 2024-12-18T01:09:49.0762613Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-12-18T01:09:49.0763200Z 2024-12-18T01:09:49.0763461Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:09:49.0764361Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-12-18T01:09:49.0765225Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:09:49.0766092Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-12-18T01:09:49.0766781Z 2024-12-18T01:09:49.0767419Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-12-18T01:09:49.0768280Z matching the shape of the input: 2024-12-18T01:09:49.0768643Z 2024-12-18T01:09:49.0768899Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-12-18T01:09:49.0769567Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:09:49.0770156Z >>> input = {'x': x, 'y': y} 2024-12-18T01:09:49.0770820Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-12-18T01:09:49.0771544Z >>> batched_dot(input) 2024-12-18T01:09:49.0771847Z 2024-12-18T01:09:49.0772361Z By default, the output is batched along the first dimension. However, it can be batched 2024-12-18T01:09:49.0773315Z along any dimension by using ``out_dims`` 2024-12-18T01:09:49.0773715Z 2024-12-18T01:09:49.0774038Z >>> f = lambda x: x ** 2 2024-12-18T01:09:49.0774546Z >>> x = torch.randn(2, 5) 2024-12-18T01:09:49.0775083Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-12-18T01:09:49.0775676Z >>> batched_pow(x) # [5, 2] 2024-12-18T01:09:49.0775962Z 2024-12-18T01:09:49.0776514Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-12-18T01:09:49.0777433Z accept kwargs 2024-12-18T01:09:49.0777658Z 2024-12-18T01:09:49.0777843Z >>> x = torch.randn([2, 5]) 2024-12-18T01:09:49.0778333Z >>> def fn(x, scale=4.): 2024-12-18T01:09:49.0778824Z >>> return x * scale 2024-12-18T01:09:49.0779248Z >>> 2024-12-18T01:09:49.0779620Z >>> batched_pow = torch.vmap(fn) 2024-12-18T01:09:49.0780205Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-12-18T01:09:49.0781032Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-12-18T01:09:49.0781677Z 2024-12-18T01:09:49.0781847Z .. note:: 2024-12-18T01:09:49.0782455Z vmap does not provide general autobatching or handle variable-length 2024-12-18T01:09:49.0783272Z sequences out of the box. 2024-12-18T01:09:49.0783601Z 2024-12-18T01:09:49.0784071Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:49.0784764Z 2024-12-18T01:09:50.4781196Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=20. 2024-12-18T01:09:50.4782876Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.4784077Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-12-18T01:09:50.4784812Z 2024-12-18T01:09:50.4785152Z This is a more structured way of using triton kernels with PyTorch. 2024-12-18T01:09:50.4786221Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2024-12-18T01:09:50.4787428Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2024-12-18T01:09:50.4788257Z that is simpler; 2024-12-18T01:09:50.4789126Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2024-12-18T01:09:50.4790247Z want to create an operator that behaves like PyTorch built-in operators. 2024-12-18T01:09:50.4791231Z For example, you may use a ``torch.library`` wrapper API to define the 2024-12-18T01:09:50.4792131Z behavior of the triton kernel when passed a tensor subclass or under 2024-12-18T01:09:50.4792903Z a TorchDispatchMode. 2024-12-18T01:09:50.4793200Z 2024-12-18T01:09:50.4793638Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2024-12-18T01:09:50.4794478Z when the implementation 2024-12-18T01:09:50.4795155Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-12-18T01:09:50.4796066Z custom operators as opaque (:func:`torch.compile` and 2024-12-18T01:09:50.4797025Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-12-18T01:09:50.4798090Z makes the implementation visible to these subsystems, allowing them 2024-12-18T01:09:50.4798907Z to optimize the triton kernel(s). 2024-12-18T01:09:50.4799658Z 2024-12-18T01:09:50.4800021Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-12-18T01:09:50.4800995Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-12-18T01:09:50.4801999Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2024-12-18T01:09:50.4802567Z 2024-12-18T01:09:50.4802717Z Args: 2024-12-18T01:09:50.4803357Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-12-18T01:09:50.4804361Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-12-18T01:09:50.4805262Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-12-18T01:09:50.4806173Z To avoid name collisions, please use your project name as the namespace; 2024-12-18T01:09:50.4807424Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-12-18T01:09:50.4808524Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-12-18T01:09:50.4809701Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-12-18T01:09:50.4810756Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-12-18T01:09:50.4811793Z schema (None | str): A schema string for the operator. If None 2024-12-18T01:09:50.4812709Z (recommended) we'll infer a schema for the operator from its type 2024-12-18T01:09:50.4813665Z annotations. We recommend letting us infer a schema unless you 2024-12-18T01:09:50.4814469Z have a specific reason not to. 2024-12-18T01:09:50.4815147Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-12-18T01:09:50.4815624Z 2024-12-18T01:09:50.4815819Z Example:: 2024-12-18T01:09:50.4816059Z 2024-12-18T01:09:50.4816310Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:50.4816920Z >>> import torch 2024-12-18T01:09:50.4817476Z >>> from torch._library import triton_op, wrap_triton 2024-12-18T01:09:50.4818131Z >>> 2024-12-18T01:09:50.4818509Z >>> import triton 2024-12-18T01:09:50.4819011Z >>> from triton import language as tl 2024-12-18T01:09:50.4819564Z >>> 2024-12-18T01:09:50.4819935Z >>> @triton.jit 2024-12-18T01:09:50.4820376Z >>> def add_kernel( 2024-12-18T01:09:50.4820863Z >>> in_ptr0, 2024-12-18T01:09:50.4821296Z >>> in_ptr1, 2024-12-18T01:09:50.4821755Z >>> out_ptr, 2024-12-18T01:09:50.4822211Z >>> n_elements, 2024-12-18T01:09:50.4822717Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:09:50.4823251Z >>> ): 2024-12-18T01:09:50.4823678Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:09:50.4824295Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:09:50.4824963Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:09:50.4825632Z >>> mask = offsets < n_elements 2024-12-18T01:09:50.4826261Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:09:50.4826910Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:09:50.4827521Z >>> output = x + y 2024-12-18T01:09:50.4828097Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:09:50.4828850Z >>> 2024-12-18T01:09:50.4829258Z >>> @triton_op("mylib::add", mutates_args={}) 2024-12-18T01:09:50.4829996Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-12-18T01:09:50.4830685Z >>> output = torch.empty_like(x) 2024-12-18T01:09:50.4831287Z >>> n_elements = output.numel() 2024-12-18T01:09:50.4831827Z >>> 2024-12-18T01:09:50.4832203Z >>> def grid(meta): 2024-12-18T01:09:50.4832840Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:09:50.4833510Z >>> 2024-12-18T01:09:50.4834014Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2024-12-18T01:09:50.4835069Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-12-18T01:09:50.4835797Z >>> return output 2024-12-18T01:09:50.4836456Z >>> 2024-12-18T01:09:50.4836846Z >>> @torch.compile 2024-12-18T01:09:50.4837312Z >>> def f(x, y): 2024-12-18T01:09:50.4837769Z >>> return add(x, y) 2024-12-18T01:09:50.4838249Z >>> 2024-12-18T01:09:50.4838642Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:09:50.4839252Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:09:50.4839807Z >>> 2024-12-18T01:09:50.4840175Z >>> z = f(x, y) 2024-12-18T01:09:50.4840650Z >>> assert torch.allclose(z, x + y) 2024-12-18T01:09:50.4841030Z 2024-12-18T01:09:50.4841391Z 2024-12-18T01:09:50.4842042Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.4842745Z 2024-12-18T01:09:50.4843725Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=183. 2024-12-18T01:09:50.4845391Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.4846454Z Allows capture of a triton kernel into a graph via make_fx or 2024-12-18T01:09:50.4847211Z non-strict ``torch.export``. 2024-12-18T01:09:50.4847575Z 2024-12-18T01:09:50.4847919Z These technologies perform Dispatcher-based tracing (via 2024-12-18T01:09:50.4848820Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-12-18T01:09:50.4849746Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2024-12-18T01:09:50.4850530Z can actually be traced into a graph. 2024-12-18T01:09:50.4850951Z 2024-12-18T01:09:50.4851338Z Please use this API together with :func:`torch.library.triton_op`. 2024-12-18T01:09:50.4851935Z 2024-12-18T01:09:50.4852108Z Examples: 2024-12-18T01:09:50.4852341Z 2024-12-18T01:09:50.4852538Z >>> # xdoctest: +SKIP 2024-12-18T01:09:50.4853019Z >>> import torch 2024-12-18T01:09:50.4853449Z >>> import triton 2024-12-18T01:09:50.4853937Z >>> from triton import language as tl 2024-12-18T01:09:50.4854652Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:09:50.4855387Z >>> from torch.library import wrap_triton 2024-12-18T01:09:50.4855926Z >>> 2024-12-18T01:09:50.4856289Z >>> @triton.jit 2024-12-18T01:09:50.4856728Z >>> def add_kernel( 2024-12-18T01:09:50.4857164Z >>> in_ptr0, 2024-12-18T01:09:50.4857576Z >>> in_ptr1, 2024-12-18T01:09:50.4857991Z >>> out_ptr, 2024-12-18T01:09:50.4858421Z >>> n_elements, 2024-12-18T01:09:50.4858941Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:09:50.4859482Z >>> ): 2024-12-18T01:09:50.4859910Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:09:50.4860491Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:09:50.4861179Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:09:50.4861844Z >>> mask = offsets < n_elements 2024-12-18T01:09:50.4862461Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:09:50.4863132Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:09:50.4863715Z >>> output = x + y 2024-12-18T01:09:50.4864292Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:09:50.4864901Z >>> 2024-12-18T01:09:50.4865269Z >>> def add(x, y): 2024-12-18T01:09:50.4865764Z >>> output = torch.empty_like(x) 2024-12-18T01:09:50.4866344Z >>> n_elements = output.numel() 2024-12-18T01:09:50.4866908Z >>> 2024-12-18T01:09:50.4867298Z >>> def grid_fn(meta): 2024-12-18T01:09:50.4867953Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:09:50.4868729Z >>> 2024-12-18T01:09:50.4869513Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-12-18T01:09:50.4870269Z >>> return output 2024-12-18T01:09:50.4870741Z >>> 2024-12-18T01:09:50.4871158Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:09:50.4871754Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:09:50.4872320Z >>> gm = make_fx(add)(x, y) 2024-12-18T01:09:50.4872851Z >>> print(gm.code) 2024-12-18T01:09:50.4873350Z >>> # def forward(self, x_1, y_1): 2024-12-18T01:09:50.4874168Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-12-18T01:09:50.4875203Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-12-18T01:09:50.4876232Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-12-18T01:09:50.4876865Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-12-18T01:09:50.4877556Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-12-18T01:09:50.4878288Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-12-18T01:09:50.4878875Z >>> # }) 2024-12-18T01:09:50.4890030Z >>> # return empty_like 2024-12-18T01:09:50.4890506Z 2024-12-18T01:09:50.4890682Z 2024-12-18T01:09:50.4891271Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.4891780Z 2024-12-18T01:09:50.5572989Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330. 2024-12-18T01:09:50.5574637Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.5575325Z 2024-12-18T01:09:50.5575758Z Raises an AssertionError if two items are not equal up to desired 2024-12-18T01:09:50.5576535Z precision. 2024-12-18T01:09:50.5576775Z 2024-12-18T01:09:50.5577103Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:09:50.5577949Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:09:50.5578848Z instead of this function for more consistent floating point 2024-12-18T01:09:50.5579575Z comparisons. 2024-12-18T01:09:50.5579860Z 2024-12-18T01:09:50.5580251Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-12-18T01:09:50.5580877Z 2024-12-18T01:09:50.5581169Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-12-18T01:09:50.5581666Z 2024-12-18T01:09:50.5582042Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:09:50.5582976Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-12-18T01:09:50.5584042Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-12-18T01:09:50.5584968Z delegates to assert_array_almost_equal 2024-12-18T01:09:50.5585380Z 2024-12-18T01:09:50.5585532Z Parameters 2024-12-18T01:09:50.5585903Z ---------- 2024-12-18T01:09:50.5586299Z actual : array_like 2024-12-18T01:09:50.5586744Z The object to check. 2024-12-18T01:09:50.5587198Z desired : array_like 2024-12-18T01:09:50.5587644Z The expected object. 2024-12-18T01:09:50.5588110Z decimal : int, optional 2024-12-18T01:09:50.5588683Z Desired precision, default is 7. 2024-12-18T01:09:50.5589257Z err_msg : str, optional 2024-12-18T01:09:50.5589762Z The error message to be printed in case of failure. 2024-12-18T01:09:50.5590335Z verbose : bool, optional 2024-12-18T01:09:50.5590896Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:50.5591435Z 2024-12-18T01:09:50.5591574Z Raises 2024-12-18T01:09:50.5591938Z ------ 2024-12-18T01:09:50.5592317Z AssertionError 2024-12-18T01:09:50.5592914Z If actual and desired are not equal up to specified precision. 2024-12-18T01:09:50.5593521Z 2024-12-18T01:09:50.5593666Z See Also 2024-12-18T01:09:50.5594033Z -------- 2024-12-18T01:09:50.5594683Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:50.5595923Z relative and/or absolute precision. 2024-12-18T01:09:50.5596741Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:50.5597350Z 2024-12-18T01:09:50.5597506Z Examples 2024-12-18T01:09:50.5597882Z -------- 2024-12-18T01:09:50.5598403Z >>> from torch._numpy.testing import assert_almost_equal 2024-12-18T01:09:50.5599150Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-12-18T01:09:50.5599911Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-12-18T01:09:50.5600698Z Traceback (most recent call last): 2024-12-18T01:09:50.5601213Z ... 2024-12-18T01:09:50.5601595Z AssertionError: 2024-12-18T01:09:50.5602057Z Arrays are not almost equal to 10 decimals 2024-12-18T01:09:50.5602627Z ACTUAL: 2.3333333333333 2024-12-18T01:09:50.5603338Z DESIRED: 2.33333334 2024-12-18T01:09:50.5603643Z 2024-12-18T01:09:50.5603912Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-12-18T01:09:50.5604615Z ... np.array([1.0,2.33333334]), decimal=9) 2024-12-18T01:09:50.5605241Z Traceback (most recent call last): 2024-12-18T01:09:50.5605747Z ... 2024-12-18T01:09:50.5606108Z AssertionError: 2024-12-18T01:09:50.5606530Z Arrays are not almost equal to 9 decimals 2024-12-18T01:09:50.5607097Z 2024-12-18T01:09:50.5607500Z Mismatched elements: 1 / 2 (50%) 2024-12-18T01:09:50.5608064Z Max absolute difference: 6.666699636781459e-09 2024-12-18T01:09:50.5608721Z Max relative difference: 2.8571569790287484e-09 2024-12-18T01:09:50.5609412Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:09:50.5610096Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:09:50.5610544Z 2024-12-18T01:09:50.5610553Z 2024-12-18T01:09:50.5611023Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.5611694Z 2024-12-18T01:09:50.5612724Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455. 2024-12-18T01:09:50.5614463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.5615166Z 2024-12-18T01:09:50.5615578Z Raises an AssertionError if two items are not equal up to significant 2024-12-18T01:09:50.5616368Z digits. 2024-12-18T01:09:50.5616589Z 2024-12-18T01:09:50.5616903Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:09:50.5617764Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:09:50.5618649Z instead of this function for more consistent floating point 2024-12-18T01:09:50.5619377Z comparisons. 2024-12-18T01:09:50.5619677Z 2024-12-18T01:09:50.5620012Z Given two numbers, check that they are approximately equal. 2024-12-18T01:09:50.5620987Z Approximately equal is defined as the number of significant digits 2024-12-18T01:09:50.5621776Z that agree. 2024-12-18T01:09:50.5621999Z 2024-12-18T01:09:50.5622173Z Parameters 2024-12-18T01:09:50.5622577Z ---------- 2024-12-18T01:09:50.5622960Z actual : scalar 2024-12-18T01:09:50.5623373Z The object to check. 2024-12-18T01:09:50.5623839Z desired : scalar 2024-12-18T01:09:50.5624273Z The expected object. 2024-12-18T01:09:50.5624746Z significant : int, optional 2024-12-18T01:09:50.5625251Z Desired precision, default is 7. 2024-12-18T01:09:50.5625790Z err_msg : str, optional 2024-12-18T01:09:50.5626344Z The error message to be printed in case of failure. 2024-12-18T01:09:50.5627003Z verbose : bool, optional 2024-12-18T01:09:50.5627662Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:50.5628232Z 2024-12-18T01:09:50.5628475Z Raises 2024-12-18T01:09:50.5628814Z ------ 2024-12-18T01:09:50.5629155Z AssertionError 2024-12-18T01:09:50.5629788Z If actual and desired are not equal up to specified precision. 2024-12-18T01:09:50.5630369Z 2024-12-18T01:09:50.5630521Z See Also 2024-12-18T01:09:50.5630870Z -------- 2024-12-18T01:09:50.5631694Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:50.5632596Z relative and/or absolute precision. 2024-12-18T01:09:50.5633405Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:50.5634018Z 2024-12-18T01:09:50.5634198Z Examples 2024-12-18T01:09:50.5634555Z -------- 2024-12-18T01:09:50.5635244Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-12-18T01:09:50.5636573Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-12-18T01:09:50.5637465Z ... significant=8) 2024-12-18T01:09:50.5638314Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-12-18T01:09:50.5639378Z ... significant=8) 2024-12-18T01:09:50.5639976Z Traceback (most recent call last): 2024-12-18T01:09:50.5640480Z ... 2024-12-18T01:09:50.5640841Z AssertionError: 2024-12-18T01:09:50.5641335Z Items are not equal to 8 significant digits: 2024-12-18T01:09:50.5641917Z ACTUAL: 1.234567e-21 2024-12-18T01:09:50.5642355Z DESIRED: 1.2345672e-21 2024-12-18T01:09:50.5642658Z 2024-12-18T01:09:50.5642931Z the evaluated condition that raises the exception is 2024-12-18T01:09:50.5643432Z 2024-12-18T01:09:50.5643757Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-12-18T01:09:50.5644428Z True 2024-12-18T01:09:50.5644621Z 2024-12-18T01:09:50.5644629Z 2024-12-18T01:09:50.5645111Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.5645808Z 2024-12-18T01:09:50.5646793Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734. 2024-12-18T01:09:50.5648526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.5649261Z 2024-12-18T01:09:50.5649645Z Raises an AssertionError if two array_like objects are not equal. 2024-12-18T01:09:50.5650272Z 2024-12-18T01:09:50.5650638Z Given two array_like objects, check that the shape is equal and all 2024-12-18T01:09:50.5651629Z elements of these objects are equal (but see the Notes for the special 2024-12-18T01:09:50.5652608Z handling of a scalar). An exception is raised at shape mismatch or 2024-12-18T01:09:50.5653554Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-12-18T01:09:50.5654564Z are compared like numbers, no assertion is raised if both objects have 2024-12-18T01:09:50.5655361Z NaNs in the same positions. 2024-12-18T01:09:50.5655699Z 2024-12-18T01:09:50.5656098Z The usual caution for verifying equality with floating point numbers is 2024-12-18T01:09:50.5656900Z advised. 2024-12-18T01:09:50.5657113Z 2024-12-18T01:09:50.5657292Z Parameters 2024-12-18T01:09:50.5657650Z ---------- 2024-12-18T01:09:50.5658030Z x : array_like 2024-12-18T01:09:50.5658448Z The actual object to check. 2024-12-18T01:09:50.5658949Z y : array_like 2024-12-18T01:09:50.5659401Z The desired, expected object. 2024-12-18T01:09:50.5659915Z err_msg : str, optional 2024-12-18T01:09:50.5660479Z The error message to be printed in case of failure. 2024-12-18T01:09:50.5661149Z verbose : bool, optional 2024-12-18T01:09:50.5661828Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:50.5662591Z strict : bool, optional 2024-12-18T01:09:50.5663231Z If True, raise an AssertionError when either the shape or the data 2024-12-18T01:09:50.5664081Z type of the array_like objects does not match. The special 2024-12-18T01:09:50.5665119Z handling for scalars mentioned in the Notes section is disabled. 2024-12-18T01:09:50.5665713Z 2024-12-18T01:09:50.5665872Z Raises 2024-12-18T01:09:50.5666220Z ------ 2024-12-18T01:09:50.5666574Z AssertionError 2024-12-18T01:09:50.5667014Z If actual and desired objects are not equal. 2024-12-18T01:09:50.5667470Z 2024-12-18T01:09:50.5667618Z See Also 2024-12-18T01:09:50.5667961Z -------- 2024-12-18T01:09:50.5668903Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:50.5669761Z relative and/or absolute precision. 2024-12-18T01:09:50.5670584Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:50.5671202Z 2024-12-18T01:09:50.5671351Z Notes 2024-12-18T01:09:50.5671697Z ----- 2024-12-18T01:09:50.5672254Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-12-18T01:09:50.5673201Z function checks that each element of the array_like object is equal to 2024-12-18T01:09:50.5674255Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-12-18T01:09:50.5674926Z 2024-12-18T01:09:50.5675076Z Examples 2024-12-18T01:09:50.5675437Z -------- 2024-12-18T01:09:50.5676019Z The first assert does not raise an exception: 2024-12-18T01:09:50.5676485Z 2024-12-18T01:09:50.5676774Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-12-18T01:09:50.5677463Z ... [np.exp(0),2.33333, np.nan]) 2024-12-18T01:09:50.5677925Z 2024-12-18T01:09:50.5678346Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-12-18T01:09:50.5679188Z functions for these cases instead: 2024-12-18T01:09:50.5679563Z 2024-12-18T01:09:50.5679831Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-12-18T01:09:50.5680539Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-12-18T01:09:50.5681146Z ... rtol=1e-10, atol=0) 2024-12-18T01:09:50.5681508Z 2024-12-18T01:09:50.5681831Z As mentioned in the Notes section, `assert_array_equal` has special 2024-12-18T01:09:50.5682782Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-12-18T01:09:50.5683457Z 2024-12-18T01:09:50.5683653Z >>> x = np.full((2, 5), fill_value=3) 2024-12-18T01:09:50.5684240Z >>> np.testing.assert_array_equal(x, 3) 2024-12-18T01:09:50.5684641Z 2024-12-18T01:09:50.5685042Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-12-18T01:09:50.5685819Z array: 2024-12-18T01:09:50.5686016Z 2024-12-18T01:09:50.5686273Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-12-18T01:09:50.5686953Z Traceback (most recent call last): 2024-12-18T01:09:50.5687482Z ... 2024-12-18T01:09:50.5687864Z AssertionError: 2024-12-18T01:09:50.5688303Z Arrays are not equal 2024-12-18T01:09:50.5688735Z 2024-12-18T01:09:50.5689138Z (shapes (2, 5), () mismatch) 2024-12-18T01:09:50.5689640Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-12-18T01:09:50.5690148Z [3, 3, 3, 3, 3]]) 2024-12-18T01:09:50.5690594Z y: torch.ndarray(3) 2024-12-18T01:09:50.5690886Z 2024-12-18T01:09:50.5691272Z The `strict` parameter also ensures that the array data types match: 2024-12-18T01:09:50.5691901Z 2024-12-18T01:09:50.5692095Z >>> x = np.array([2, 2, 2]) 2024-12-18T01:09:50.5692636Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-12-18T01:09:50.5693339Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-12-18T01:09:50.5694023Z Traceback (most recent call last): 2024-12-18T01:09:50.5694541Z ... 2024-12-18T01:09:50.5694920Z AssertionError: 2024-12-18T01:09:50.5695346Z Arrays are not equal 2024-12-18T01:09:50.5695764Z 2024-12-18T01:09:50.5696249Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-12-18T01:09:50.5696913Z x: torch.ndarray([2, 2, 2]) 2024-12-18T01:09:50.5697427Z y: torch.ndarray([2., 2., 2.]) 2024-12-18T01:09:50.5697765Z 2024-12-18T01:09:50.5698242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.5698926Z 2024-12-18T01:09:50.5700031Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840. 2024-12-18T01:09:50.5701777Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.5702429Z 2024-12-18T01:09:50.5702787Z Raises an AssertionError if two objects are not equal up to desired 2024-12-18T01:09:50.5703685Z precision. 2024-12-18T01:09:50.5703899Z 2024-12-18T01:09:50.5704227Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:09:50.5705033Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:09:50.5705896Z instead of this function for more consistent floating point 2024-12-18T01:09:50.5706579Z comparisons. 2024-12-18T01:09:50.5706866Z 2024-12-18T01:09:50.5707295Z The test verifies identical shapes and that the elements of ``actual`` and 2024-12-18T01:09:50.5708110Z ``desired`` satisfy. 2024-12-18T01:09:50.5708490Z 2024-12-18T01:09:50.5708718Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-12-18T01:09:50.5709157Z 2024-12-18T01:09:50.5709585Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:09:50.5710789Z actual implementation did up to rounding vagaries. An exception is raised 2024-12-18T01:09:50.5711870Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-12-18T01:09:50.5712926Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-12-18T01:09:50.5713769Z objects have NaNs in the same positions. 2024-12-18T01:09:50.5714156Z 2024-12-18T01:09:50.5714323Z Parameters 2024-12-18T01:09:50.5714684Z ---------- 2024-12-18T01:09:50.5715044Z x : array_like 2024-12-18T01:09:50.5715459Z The actual object to check. 2024-12-18T01:09:50.5715935Z y : array_like 2024-12-18T01:09:50.5716339Z The desired, expected object. 2024-12-18T01:09:50.5716846Z decimal : int, optional 2024-12-18T01:09:50.5717277Z Desired precision, default is 6. 2024-12-18T01:09:50.5717824Z err_msg : str, optional 2024-12-18T01:09:50.5718373Z The error message to be printed in case of failure. 2024-12-18T01:09:50.5719024Z verbose : bool, optional 2024-12-18T01:09:50.5719712Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:50.5720271Z 2024-12-18T01:09:50.5720412Z Raises 2024-12-18T01:09:50.5720736Z ------ 2024-12-18T01:09:50.5721098Z AssertionError 2024-12-18T01:09:50.5721670Z If actual and desired are not equal up to specified precision. 2024-12-18T01:09:50.5722224Z 2024-12-18T01:09:50.5722389Z See Also 2024-12-18T01:09:50.5722729Z -------- 2024-12-18T01:09:50.5723389Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:50.5724262Z relative and/or absolute precision. 2024-12-18T01:09:50.5725086Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:50.5725671Z 2024-12-18T01:09:50.5725830Z Examples 2024-12-18T01:09:50.5726164Z -------- 2024-12-18T01:09:50.5726578Z the first assert does not raise an exception 2024-12-18T01:09:50.5727028Z 2024-12-18T01:09:50.5727312Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-12-18T01:09:50.5728015Z ... [1.0,2.333,np.nan]) 2024-12-18T01:09:50.5728423Z 2024-12-18T01:09:50.5728750Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:09:50.5729420Z ... [1.0,2.33339,np.nan], decimal=5) 2024-12-18T01:09:50.5730001Z Traceback (most recent call last): 2024-12-18T01:09:50.5730514Z ... 2024-12-18T01:09:50.5730872Z AssertionError: 2024-12-18T01:09:50.5731321Z Arrays are not almost equal to 5 decimals 2024-12-18T01:09:50.5731884Z 2024-12-18T01:09:50.5732268Z Mismatched elements: 1 / 3 (33.3%) 2024-12-18T01:09:50.5732834Z Max absolute difference: 5.999999999994898e-05 2024-12-18T01:09:50.5733487Z Max relative difference: 2.5713661239633743e-05 2024-12-18T01:09:50.5734179Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:09:50.5734936Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-12-18T01:09:50.5735419Z 2024-12-18T01:09:50.5735726Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:09:50.5736645Z ... [1.0,2.33333, 5], decimal=5) 2024-12-18T01:09:50.5737240Z Traceback (most recent call last): 2024-12-18T01:09:50.5737971Z ... 2024-12-18T01:09:50.5738325Z AssertionError: 2024-12-18T01:09:50.5738771Z Arrays are not almost equal to 5 decimals 2024-12-18T01:09:50.5739327Z 2024-12-18T01:09:50.5739730Z x and y nan location mismatch: 2024-12-18T01:09:50.5740319Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:09:50.5741071Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-12-18T01:09:50.5741544Z 2024-12-18T01:09:50.5741552Z 2024-12-18T01:09:50.5742008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.5742715Z 2024-12-18T01:09:50.5744069Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-12-18T01:09:50.5745803Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.5746809Z Context manager that resets warning registry for catching warnings 2024-12-18T01:09:50.5747415Z 2024-12-18T01:09:50.5747851Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-12-18T01:09:50.5748999Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-12-18T01:09:50.5750062Z it impossible to retrigger the warning in this module, whatever you put in 2024-12-18T01:09:50.5751147Z the warnings filters. This context manager accepts a sequence of `modules` 2024-12-18T01:09:50.5752047Z as a keyword argument to its constructor and: 2024-12-18T01:09:50.5752515Z 2024-12-18T01:09:50.5752925Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-12-18T01:09:50.5753722Z on entry; 2024-12-18T01:09:50.5754300Z * resets ``__warningregistry__`` to its previous state on exit. 2024-12-18T01:09:50.5754793Z 2024-12-18T01:09:50.5755216Z This makes it possible to trigger any warning afresh inside the context 2024-12-18T01:09:50.5756163Z manager without disturbing the state of warnings outside. 2024-12-18T01:09:50.5756712Z 2024-12-18T01:09:50.5757135Z For compatibility with Python 3.0, please consider all arguments to be 2024-12-18T01:09:50.5757955Z keyword-only. 2024-12-18T01:09:50.5758217Z 2024-12-18T01:09:50.5758377Z Parameters 2024-12-18T01:09:50.5758785Z ---------- 2024-12-18T01:09:50.5759199Z record : bool, optional 2024-12-18T01:09:50.5759859Z Specifies whether warnings should be captured by a custom 2024-12-18T01:09:50.5760859Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-12-18T01:09:50.5761895Z returned by the context manager. Otherwise None is returned by the 2024-12-18T01:09:50.5762823Z context manager. The objects appended to the list are arguments whose 2024-12-18T01:09:50.5763765Z attributes mirror the arguments to ``showwarning()``. 2024-12-18T01:09:50.5764463Z modules : sequence, optional 2024-12-18T01:09:50.5765212Z Sequence of modules for which to reset warnings registry on entry and 2024-12-18T01:09:50.5766211Z restore on exit. To work correctly, all 'ignore' filters should 2024-12-18T01:09:50.5766978Z filter by one of these modules. 2024-12-18T01:09:50.5767370Z 2024-12-18T01:09:50.5767537Z Examples 2024-12-18T01:09:50.5767922Z -------- 2024-12-18T01:09:50.5768304Z >>> import warnings 2024-12-18T01:09:50.5768915Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-12-18T01:09:50.5769690Z ... modules=[np.core.fromnumeric]): 2024-12-18T01:09:50.5770324Z ... warnings.simplefilter('always') 2024-12-18T01:09:50.5771131Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-12-18T01:09:50.5772072Z ... # do something that raises a warning but ignore those in 2024-12-18T01:09:50.5772783Z ... # np.core.fromnumeric 2024-12-18T01:09:50.5773275Z 2024-12-18T01:09:50.5773889Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.5774754Z 2024-12-18T01:09:50.7341316Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2024-12-18T01:09:50.7342273Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.7342859Z Applies a 1D convolution over a quantized input signal composed of 2024-12-18T01:09:50.7343305Z several quantized input planes. 2024-12-18T01:09:50.7343526Z 2024-12-18T01:09:50.7343741Z For details on input arguments, parameters, and implementation see 2024-12-18T01:09:50.7344177Z :class:`~torch.nn.Conv1d`. 2024-12-18T01:09:50.7344364Z 2024-12-18T01:09:50.7344484Z .. note:: 2024-12-18T01:09:50.7345078Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-12-18T01:09:50.7345408Z 2024-12-18T01:09:50.7345497Z .. note:: 2024-12-18T01:09:50.7345814Z Only `torch.quint8` is supported for the input data type. 2024-12-18T01:09:50.7346139Z 2024-12-18T01:09:50.7346143Z 2024-12-18T01:09:50.7346236Z Attributes: 2024-12-18T01:09:50.7346597Z weight (Tensor): packed tensor derived from the learnable weight 2024-12-18T01:09:50.7347034Z parameter. 2024-12-18T01:09:50.7347380Z scale (Tensor): scalar for the output scale 2024-12-18T01:09:50.7347803Z zero_point (Tensor): scalar for the output zero point 2024-12-18T01:09:50.7348089Z 2024-12-18T01:09:50.7348342Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-12-18T01:09:50.7348623Z 2024-12-18T01:09:50.7348720Z Examples:: 2024-12-18T01:09:50.7348855Z 2024-12-18T01:09:50.7349018Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-12-18T01:09:50.7349426Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-12-18T01:09:50.7349793Z >>> input = torch.randn(20, 16, 100) 2024-12-18T01:09:50.7350117Z >>> # quantize input to quint8 2024-12-18T01:09:50.7350441Z >>> # xdoctest: +SKIP 2024-12-18T01:09:50.7350840Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-12-18T01:09:50.7351306Z ... dtype=torch.quint8) 2024-12-18T01:09:50.7351657Z >>> output = m(q_input) 2024-12-18T01:09:50.7351841Z 2024-12-18T01:09:50.7351926Z 2024-12-18T01:09:50.7352298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.7352677Z 2024-12-18T01:09:50.7536716Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-12-18T01:09:50.7537755Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.7538283Z A quantized long short-term memory (LSTM). 2024-12-18T01:09:50.7538515Z 2024-12-18T01:09:50.7538803Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-12-18T01:09:50.7539207Z 2024-12-18T01:09:50.7539316Z Attributes: 2024-12-18T01:09:50.7539571Z layers : instances of the `_LSTMLayer` 2024-12-18T01:09:50.7539816Z 2024-12-18T01:09:50.7539915Z .. note:: 2024-12-18T01:09:50.7540264Z To access the weights and biases, you need to access them per layer. 2024-12-18T01:09:50.7540771Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-12-18T01:09:50.7541054Z 2024-12-18T01:09:50.7541159Z Examples:: 2024-12-18T01:09:50.7541404Z >>> # xdoctest: +SKIP 2024-12-18T01:09:50.7541686Z >>> custom_module_config = { 2024-12-18T01:09:50.7542032Z ... 'float_to_observed_custom_module_class': { 2024-12-18T01:09:50.7542411Z ... nn.LSTM: nn.quantizable.LSTM, 2024-12-18T01:09:50.7542740Z ... }, 2024-12-18T01:09:50.7543041Z ... 'observed_to_quantized_custom_module_class': { 2024-12-18T01:09:50.7543440Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-12-18T01:09:50.7543788Z ... } 2024-12-18T01:09:50.7544242Z ... } 2024-12-18T01:09:50.7544601Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-12-18T01:09:50.7545156Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-12-18T01:09:50.7545562Z 2024-12-18T01:09:50.7545930Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.7546318Z 2024-12-18T01:09:50.8394640Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-12-18T01:09:50.8395737Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.8396536Z Squashes the sparse masks into the appropriate tensors. 2024-12-18T01:09:50.8396838Z 2024-12-18T01:09:50.8397051Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-12-18T01:09:50.8397575Z the module will have a `sparse_params` dict attached to it. 2024-12-18T01:09:50.8397884Z 2024-12-18T01:09:50.8398007Z Args: 2024-12-18T01:09:50.8398337Z params_to_keep: List of keys to save in the module or a dict 2024-12-18T01:09:50.8398798Z representing the modules and keys that will have 2024-12-18T01:09:50.8399215Z sparsity parameters saved 2024-12-18T01:09:50.8399677Z params_to_keep_per_layer: Dict to specify the params that should be 2024-12-18T01:09:50.8400174Z saved for specific layers. The keys in the dict 2024-12-18T01:09:50.8400617Z should be the module fqn, while the values should 2024-12-18T01:09:50.8401072Z be a list of strings with the names of the variables 2024-12-18T01:09:50.8401471Z to save in the `sparse_params` 2024-12-18T01:09:50.8401722Z 2024-12-18T01:09:50.8401814Z Examples: 2024-12-18T01:09:50.8402105Z >>> # xdoctest: +SKIP("locals are undefined") 2024-12-18T01:09:50.8402473Z >>> # Don't save any sparse params 2024-12-18T01:09:50.8402825Z >>> sparsifier.squash_mask() 2024-12-18T01:09:50.8403179Z >>> hasattr(model.submodule1, 'sparse_params') 2024-12-18T01:09:50.8403527Z False 2024-12-18T01:09:50.8403683Z 2024-12-18T01:09:50.8403802Z >>> # Keep sparse params per layer 2024-12-18T01:09:50.8404146Z >>> sparsifier.squash_mask( 2024-12-18T01:09:50.8404478Z ... params_to_keep_per_layer={ 2024-12-18T01:09:50.8404830Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-12-18T01:09:50.8405208Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:09:50.8405540Z ... }) 2024-12-18T01:09:50.8405851Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:09:50.8406225Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:50.8406570Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:09:50.8406947Z {'baz': 0.1} 2024-12-18T01:09:50.8407120Z 2024-12-18T01:09:50.8407246Z >>> # Keep sparse params for all layers 2024-12-18T01:09:50.8407666Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-12-18T01:09:50.8408114Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:09:50.8408488Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:50.8408834Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:09:50.8409207Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:50.8409408Z 2024-12-18T01:09:50.8409606Z >>> # Keep some sparse params for all layers, and specific ones for 2024-12-18T01:09:50.8410028Z >>> # some other layers 2024-12-18T01:09:50.8410343Z >>> sparsifier.squash_mask( 2024-12-18T01:09:50.8410669Z ... params_to_keep=('foo', 'bar'), 2024-12-18T01:09:50.8411020Z ... params_to_keep_per_layer={ 2024-12-18T01:09:50.8411508Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:09:50.8411840Z ... }) 2024-12-18T01:09:50.8412157Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:09:50.8412533Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:50.8412882Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:09:50.8413271Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-12-18T01:09:50.8413580Z 2024-12-18T01:09:50.8413955Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.8414323Z 2024-12-18T01:09:50.9166848Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-12-18T01:09:50.9167922Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:50.9168301Z 2024-12-18T01:09:50.9168573Z Config object that specifies the supported data types passed as arguments to 2024-12-18T01:09:50.9169167Z quantize ops in the reference model spec, for input and output activations, 2024-12-18T01:09:50.9169623Z weights, and biases. 2024-12-18T01:09:50.9169792Z 2024-12-18T01:09:50.9169950Z For example, consider the following reference model: 2024-12-18T01:09:50.9170217Z 2024-12-18T01:09:50.9170393Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-12-18T01:09:50.9170655Z 2024-12-18T01:09:50.9170879Z The pattern in the square brackets refers to the reference pattern of 2024-12-18T01:09:50.9171447Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-12-18T01:09:50.9172014Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-12-18T01:09:50.9172589Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-12-18T01:09:50.9173150Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-12-18T01:09:50.9173608Z the second quantize op (quant2). 2024-12-18T01:09:50.9173804Z 2024-12-18T01:09:50.9174035Z Note that the dtype here does not refer to the interface dtypes of the 2024-12-18T01:09:50.9174571Z op. For example, the "input dtype" here is not the dtype of the input 2024-12-18T01:09:50.9175096Z tensor passed to the quantized linear op. Though it can still be the 2024-12-18T01:09:50.9175626Z same as the interface dtype, this is not always the case, e.g. the 2024-12-18T01:09:50.9176157Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-12-18T01:09:50.9176703Z specified in the DTypeConfig would still be quint8. The semantics of 2024-12-18T01:09:50.9177241Z dtypes here are the same as the semantics of the dtypes specified in 2024-12-18T01:09:50.9177649Z the observers. 2024-12-18T01:09:50.9177801Z 2024-12-18T01:09:50.9178011Z These dtypes are matched against the ones specified in the user's 2024-12-18T01:09:50.9178547Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-12-18T01:09:50.9179107Z specified in the DTypeConfig (if any), then we will quantize the given 2024-12-18T01:09:50.9179672Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-12-18T01:09:50.9180126Z the pattern will not be quantized. 2024-12-18T01:09:50.9180329Z 2024-12-18T01:09:50.9180443Z Example usage:: 2024-12-18T01:09:50.9180595Z 2024-12-18T01:09:50.9180701Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:09:50.9181021Z >>> dtype_config1 = DTypeConfig( 2024-12-18T01:09:50.9181347Z ... input_dtype=torch.quint8, 2024-12-18T01:09:50.9181693Z ... output_dtype=torch.quint8, 2024-12-18T01:09:50.9182019Z ... weight_dtype=torch.qint8, 2024-12-18T01:09:50.9182325Z ... bias_dtype=torch.float) 2024-12-18T01:09:50.9182537Z 2024-12-18T01:09:50.9182653Z >>> dtype_config2 = DTypeConfig( 2024-12-18T01:09:50.9182996Z ... input_dtype=DTypeWithConstraints( 2024-12-18T01:09:50.9183341Z ... dtype=torch.quint8, 2024-12-18T01:09:50.9183785Z ... quant_min_lower_bound=0, 2024-12-18T01:09:50.9184105Z ... quant_max_upper_bound=255, 2024-12-18T01:09:50.9184420Z ... ), 2024-12-18T01:09:50.9184694Z ... output_dtype=DTypeWithConstraints( 2024-12-18T01:09:50.9185046Z ... dtype=torch.quint8, 2024-12-18T01:09:50.9185482Z ... quant_min_lower_bound=0, 2024-12-18T01:09:50.9185807Z ... quant_max_upper_bound=255, 2024-12-18T01:09:50.9186123Z ... ), 2024-12-18T01:09:50.9186394Z ... weight_dtype=DTypeWithConstraints( 2024-12-18T01:09:50.9186744Z ... dtype=torch.qint8, 2024-12-18T01:09:50.9187064Z ... quant_min_lower_bound=-128, 2024-12-18T01:09:50.9187393Z ... quant_max_upper_bound=127, 2024-12-18T01:09:50.9187709Z ... ), 2024-12-18T01:09:50.9188063Z ... bias_dtype=torch.float) 2024-12-18T01:09:50.9188330Z 2024-12-18T01:09:50.9188465Z >>> dtype_config1.input_dtype 2024-12-18T01:09:50.9188770Z torch.quint8 2024-12-18T01:09:50.9188917Z 2024-12-18T01:09:50.9189024Z >>> dtype_config2.input_dtype 2024-12-18T01:09:50.9189330Z torch.quint8 2024-12-18T01:09:50.9189486Z 2024-12-18T01:09:50.9189623Z >>> dtype_config2.input_dtype_with_constraints 2024-12-18T01:09:50.9190409Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-12-18T01:09:50.9191050Z 2024-12-18T01:09:50.9191318Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:50.9191686Z 2024-12-18T01:09:51.0237094Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-12-18T01:09:51.0238745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.0239168Z 2024-12-18T01:09:51.0239455Z Takes in optional filter values and generates two tables with desired information. 2024-12-18T01:09:51.0239912Z 2024-12-18T01:09:51.0240129Z The generated tables are presented in both a list-of-lists format 2024-12-18T01:09:51.0240519Z 2024-12-18T01:09:51.0240725Z The reason for the two tables are that they handle different things: 2024-12-18T01:09:51.0241262Z 1.) the first table handles all tensor level information 2024-12-18T01:09:51.0241749Z 2.) the second table handles and displays all channel based information 2024-12-18T01:09:51.0242236Z 2024-12-18T01:09:51.0242660Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:09:51.0243874Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:09:51.0244709Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:09:51.0245173Z 2024-12-18T01:09:51.0245285Z Tensor table columns: 2024-12-18T01:09:51.0245793Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:51.0246358Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:09:51.0246816Z 2024-12-18T01:09:51.0247015Z Per-Channel table columns: 2024-12-18T01:09:51.0247611Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:51.0248119Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:09:51.0248400Z 2024-12-18T01:09:51.0248499Z Args: 2024-12-18T01:09:51.0248874Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:09:51.0249370Z contain this filter substring 2024-12-18T01:09:51.0249760Z Default = "", results in all the features being printed 2024-12-18T01:09:51.0250299Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:51.0250930Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:51.0251524Z 2024-12-18T01:09:51.0251650Z Returns a dictionary with two keys: 2024-12-18T01:09:51.0252028Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-12-18T01:09:51.0252446Z "tensor_level_info", "channel_level_info" 2024-12-18T01:09:51.0252793Z Each key maps to a tuple with: 2024-12-18T01:09:51.0253138Z A list of the headers of each table 2024-12-18T01:09:51.0253559Z A list of lists containing the table information row by row 2024-12-18T01:09:51.0254025Z The 0th index row will contain the headers of the columns 2024-12-18T01:09:51.0254446Z The rest of the rows will contain data 2024-12-18T01:09:51.0254694Z 2024-12-18T01:09:51.0254787Z Example Use: 2024-12-18T01:09:51.0255155Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.0255549Z >>> mod_report_visualizer.generate_filtered_tables( 2024-12-18T01:09:51.0255942Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:51.0256282Z ... module_fqn_filter = "block1" 2024-12-18T01:09:51.0256776Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-12-18T01:09:51.0257173Z 2024-12-18T01:09:51.0257426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.0257803Z 2024-12-18T01:09:51.0258702Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-12-18T01:09:51.0259959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.0260334Z 2024-12-18T01:09:51.0260620Z Takes in optional filter values and prints out formatted tables of the information. 2024-12-18T01:09:51.0261004Z 2024-12-18T01:09:51.0261352Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-12-18T01:09:51.0261970Z 1.) the first table handles all tensor level information 2024-12-18T01:09:51.0262463Z 2.) the second table handles and displays all channel based information 2024-12-18T01:09:51.0262800Z 2024-12-18T01:09:51.0263111Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:09:51.0263869Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:09:51.0264667Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:09:51.0265130Z 2024-12-18T01:09:51.0265242Z Tensor table columns: 2024-12-18T01:09:51.0265601Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:51.0266047Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:09:51.0266321Z 2024-12-18T01:09:51.0266427Z Per-Channel table columns: 2024-12-18T01:09:51.0266619Z 2024-12-18T01:09:51.0266837Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:51.0267333Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:09:51.0267608Z 2024-12-18T01:09:51.0267707Z Args: 2024-12-18T01:09:51.0268089Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:09:51.0268678Z contain this filter substring 2024-12-18T01:09:51.0269060Z Default = "", results in all the features being printed 2024-12-18T01:09:51.0269606Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:51.0270235Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:51.0270601Z 2024-12-18T01:09:51.0270708Z Example Use: 2024-12-18T01:09:51.0270962Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.0271372Z >>> mod_report_visualizer.generate_table_visualization( 2024-12-18T01:09:51.0271854Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:51.0272210Z ... module_fqn_filter = "block1" 2024-12-18T01:09:51.0272521Z ... ) 2024-12-18T01:09:51.0272825Z >>> # prints out neatly formatted table with per_channel_min info 2024-12-18T01:09:51.0273262Z >>> # for all modules in block 1 of the model 2024-12-18T01:09:51.0273507Z 2024-12-18T01:09:51.0273756Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.0274130Z 2024-12-18T01:09:51.0275014Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566. 2024-12-18T01:09:51.0276326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.0276700Z 2024-12-18T01:09:51.0276947Z Takes in a feature and optional module_filter and plots of the desired data. 2024-12-18T01:09:51.0277302Z 2024-12-18T01:09:51.0277582Z For per channel features, it averages the value across the channels and plots a point 2024-12-18T01:09:51.0278248Z per module. The reason for this is that for models with hundreds of channels, it can 2024-12-18T01:09:51.0278894Z be hard to differentiate one channel line from another, and so the point of generating 2024-12-18T01:09:51.0279563Z a single average point per module is to give a sense of general trends that encourage 2024-12-18T01:09:51.0280056Z further deep dives. 2024-12-18T01:09:51.0280206Z 2024-12-18T01:09:51.0280305Z Note: 2024-12-18T01:09:51.0280694Z Only features in the report that have tensor value data are plottable by this class 2024-12-18T01:09:51.0281235Z When the tensor information is plotted, it will plot: 2024-12-18T01:09:51.0281659Z idx as the x val, feature value as the y_val 2024-12-18T01:09:51.0282084Z When the channel information is plotted, it will plot: 2024-12-18T01:09:51.0282641Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-12-18T01:09:51.0283262Z The reason for this is that we want to be able to compare values across the 2024-12-18T01:09:51.0283855Z channels for same layer, and it will be hard if values are staggered by idx 2024-12-18T01:09:51.0284375Z This means each module is represented by only 1 x value 2024-12-18T01:09:51.0284751Z Args: 2024-12-18T01:09:51.0285106Z feature_filter (str): Filters the features presented to only those that 2024-12-18T01:09:51.0285566Z contain this filter substring 2024-12-18T01:09:51.0286046Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:51.0286668Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:51.0287038Z 2024-12-18T01:09:51.0287134Z Example Use: 2024-12-18T01:09:51.0287399Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.0287812Z >>> mod_report_visualizer.generate_plot_visualization( 2024-12-18T01:09:51.0288213Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:51.0288567Z ... module_fqn_filter = "block1" 2024-12-18T01:09:51.0288856Z ... ) 2024-12-18T01:09:51.0289159Z >>> # outputs line plot of per_channel_min information for all 2024-12-18T01:09:51.0289637Z >>> # modules in block1 of model each channel gets it's own line, 2024-12-18T01:09:51.0290115Z >>> # and it's plotted across the in-order modules on the x-axis 2024-12-18T01:09:51.0290399Z 2024-12-18T01:09:51.0290661Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.0291026Z 2024-12-18T01:09:51.0291979Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646. 2024-12-18T01:09:51.0293261Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.0293705Z 2024-12-18T01:09:51.0293980Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-12-18T01:09:51.0294385Z 2024-12-18T01:09:51.0294470Z Note: 2024-12-18T01:09:51.0294861Z Only features in the report that have tensor value data can be viewed as a histogram 2024-12-18T01:09:51.0295509Z If you want to plot a histogram from all the channel values of a specific feature for 2024-12-18T01:09:51.0296144Z a specific model, make sure to specify both the model and the feature properly 2024-12-18T01:09:51.0296756Z in the filters and you should be able to see a distribution of the channel data 2024-12-18T01:09:51.0297118Z 2024-12-18T01:09:51.0297203Z Args: 2024-12-18T01:09:51.0297639Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:09:51.0298132Z contain this filter substring 2024-12-18T01:09:51.0298516Z Default = "", results in all the features being printed 2024-12-18T01:09:51.0299057Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:51.0299680Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:51.0300256Z num_bins (int, optional): The number of bins to create the histogram with 2024-12-18T01:09:51.0300790Z Default = 10, the values will be split into 10 equal sized bins 2024-12-18T01:09:51.0301100Z 2024-12-18T01:09:51.0301191Z Example Use: 2024-12-18T01:09:51.0301431Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.0301906Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-12-18T01:09:51.0302438Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:51.0302793Z ... module_fqn_filter = "block1" 2024-12-18T01:09:51.0303099Z ... ) 2024-12-18T01:09:51.0303500Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-12-18T01:09:51.0304156Z information is gathered across all channels for all modules in block 1 for the 2024-12-18T01:09:51.0304753Z per_channel_min and is displayed in a histogram of equally sized bins 2024-12-18T01:09:51.0305084Z 2024-12-18T01:09:51.0305334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.0305711Z 2024-12-18T01:09:51.2622947Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=660. 2024-12-18T01:09:51.2623919Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:51.2624282Z 2024-12-18T01:09:51.2624589Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-12-18T01:09:51.2625266Z The submesh created consists of the dimensions and the communicators indicated by 2024-12-18T01:09:51.2625747Z ``mesh_dim_names`` 2024-12-18T01:09:51.2625906Z 2024-12-18T01:09:51.2626007Z Args: 2024-12-18T01:09:51.2626355Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-12-18T01:09:51.2626900Z mesh dimension of the DeviceMesh to create the submesh for. 2024-12-18T01:09:51.2627294Z Returns: 2024-12-18T01:09:51.2627532Z A :class:`DeviceMesh` object 2024-12-18T01:09:51.2627725Z 2024-12-18T01:09:51.2628018Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-12-18T01:09:51.2628590Z In the first example: 2024-12-18T01:09:51.2629010Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-12-18T01:09:51.2629623Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-12-18T01:09:51.2630224Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-12-18T01:09:51.2630799Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-12-18T01:09:51.2631593Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-12-18T01:09:51.2632149Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-12-18T01:09:51.2632506Z 2024-12-18T01:09:51.2632606Z In the second example: 2024-12-18T01:09:51.2633049Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-12-18T01:09:51.2633701Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-12-18T01:09:51.2634349Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-12-18T01:09:51.2635097Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-12-18T01:09:51.2635473Z 2024-12-18T01:09:51.2635596Z Example:: 2024-12-18T01:09:51.2635821Z >>> # xdoctest: +SKIP("no rank") 2024-12-18T01:09:51.2636433Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-12-18T01:09:51.2636811Z >>> 2024-12-18T01:09:51.2637138Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-12-18T01:09:51.2637609Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-12-18T01:09:51.2638113Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:09:51.2638596Z >>> tp_mesh = mesh_2d["tp"] 2024-12-18T01:09:51.2638891Z >>> dp_mesh = mesh_2d["dp"] 2024-12-18T01:09:51.2639173Z >>> 2024-12-18T01:09:51.2639400Z >>> # Initialize a 3D mesh. 2024-12-18T01:09:51.2639864Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-12-18T01:09:51.2640572Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-12-18T01:09:51.2641115Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-12-18T01:09:51.2641458Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-12-18T01:09:51.2641677Z 2024-12-18T01:09:51.2642356Z 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)) 2024-12-18T01:09:51.2643125Z 2024-12-18T01:09:51.2643384Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:09:51.2643864Z ^ 2024-12-18T01:09:51.2977247Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3063. 2024-12-18T01:09:51.2978268Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.2978686Z 2024-12-18T01:09:51.2978912Z Gathers picklable objects from the whole group in a single process. 2024-12-18T01:09:51.2979265Z 2024-12-18T01:09:51.2979500Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-12-18T01:09:51.2980024Z object must be picklable in order to be gathered. 2024-12-18T01:09:51.2980281Z 2024-12-18T01:09:51.2980381Z Args: 2024-12-18T01:09:51.2980638Z obj (Any): Input object. Must be picklable. 2024-12-18T01:09:51.2981084Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-12-18T01:09:51.2981601Z should be correctly sized as the size of the group for this 2024-12-18T01:09:51.2982120Z collective and will contain the output. Must be ``None`` on non-dst 2024-12-18T01:09:51.2982562Z ranks. (default is ``None``) 2024-12-18T01:09:51.2983087Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2024-12-18T01:09:51.2983725Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2024-12-18T01:09:51.2984261Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:09:51.2984804Z the default process group will be used. Default is ``None``. 2024-12-18T01:09:51.2985692Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2024-12-18T01:09:51.2986145Z 2024-12-18T01:09:51.2986248Z Returns: 2024-12-18T01:09:51.2986562Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-12-18T01:09:51.2986962Z output of the collective. 2024-12-18T01:09:51.2987163Z 2024-12-18T01:09:51.2987396Z .. note:: Note that this API differs slightly from the gather collective 2024-12-18T01:09:51.2987949Z since it does not provide an async_op handle and thus will be a blocking 2024-12-18T01:09:51.2988462Z call. 2024-12-18T01:09:51.2988583Z 2024-12-18T01:09:51.2988947Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-12-18T01:09:51.2989510Z of objects must be moved to the GPU device before communication takes 2024-12-18T01:09:51.2989978Z place. In this case, the device used is given by 2024-12-18T01:09:51.2990470Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-12-18T01:09:51.2991017Z ensure that this is set so that each rank has an individual GPU, via 2024-12-18T01:09:51.2991454Z ``torch.cuda.set_device()``. 2024-12-18T01:09:51.2991648Z 2024-12-18T01:09:51.2991750Z .. warning:: 2024-12-18T01:09:51.2992085Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-12-18T01:09:51.2992609Z known to be insecure. It is possible to construct malicious pickle data 2024-12-18T01:09:51.2993168Z which will execute arbitrary code during unpickling. Only call this 2024-12-18T01:09:51.2993611Z function with data you trust. 2024-12-18T01:09:51.2993809Z 2024-12-18T01:09:51.2993913Z .. warning:: 2024-12-18T01:09:51.2994271Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-12-18T01:09:51.2994823Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-12-18T01:09:51.2995345Z pickled. Please consider using :func:`gather` instead. 2024-12-18T01:09:51.2995640Z 2024-12-18T01:09:51.2995731Z Example:: 2024-12-18T01:09:51.2996000Z >>> # xdoctest: +SKIP("need process group init") 2024-12-18T01:09:51.2996439Z >>> # Note: Process group initialization omitted on each rank. 2024-12-18T01:09:51.2996852Z >>> import torch.distributed as dist 2024-12-18T01:09:51.2997171Z >>> # Assumes world_size of 3. 2024-12-18T01:09:51.2997549Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-12-18T01:09:51.2997966Z >>> output = [None for _ in gather_objects] 2024-12-18T01:09:51.2998302Z >>> dist.gather_object( 2024-12-18T01:09:51.2998605Z ... gather_objects[dist.get_rank()], 2024-12-18T01:09:51.2998955Z ... output if dist.get_rank() == 0 else None, 2024-12-18T01:09:51.2999295Z ... dst=0 2024-12-18T01:09:51.2999528Z ... ) 2024-12-18T01:09:51.2999743Z >>> # On rank 0 2024-12-18T01:09:51.2999979Z >>> output 2024-12-18T01:09:51.3000202Z ['foo', 12, {1: 2}] 2024-12-18T01:09:51.3000369Z 2024-12-18T01:09:51.3000617Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.3000993Z 2024-12-18T01:09:51.3162813Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-12-18T01:09:51.3163951Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.3164339Z 2024-12-18T01:09:51.3164460Z Module ``torch.distributed.launch``. 2024-12-18T01:09:51.3164676Z 2024-12-18T01:09:51.3164940Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-12-18T01:09:51.3165461Z training processes on each of the training nodes. 2024-12-18T01:09:51.3165720Z 2024-12-18T01:09:51.3165838Z .. warning:: 2024-12-18T01:09:51.3165978Z 2024-12-18T01:09:51.3166231Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-12-18T01:09:51.3166805Z 2024-12-18T01:09:51.3167043Z The utility can be used for single-node distributed training, in which one or 2024-12-18T01:09:51.3167643Z more processes per node will be spawned. The utility can be used for either 2024-12-18T01:09:51.3168218Z CPU training or GPU training. If the utility is used for GPU training, 2024-12-18T01:09:51.3168799Z each distributed process will be operating on a single GPU. This can achieve 2024-12-18T01:09:51.3169393Z well-improved single-node training performance. It can also be used in 2024-12-18T01:09:51.3170013Z multi-node distributed training, by spawning up multiple processes on each node 2024-12-18T01:09:51.3170637Z for well-improved multi-node distributed training performance as well. 2024-12-18T01:09:51.3171219Z This will especially be beneficial for systems with multiple Infiniband 2024-12-18T01:09:51.3171926Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-12-18T01:09:51.3172427Z aggregated communication bandwidth. 2024-12-18T01:09:51.3172644Z 2024-12-18T01:09:51.3172879Z In both cases of single-node distributed training or multi-node distributed 2024-12-18T01:09:51.3173470Z training, this utility will launch the given number of processes per node 2024-12-18T01:09:51.3174061Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-12-18T01:09:51.3174629Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-12-18T01:09:51.3175169Z and each process will be operating on a single GPU from *GPU 0 to 2024-12-18T01:09:51.3175596Z GPU (nproc_per_node - 1)*. 2024-12-18T01:09:51.3175775Z 2024-12-18T01:09:51.3175899Z **How to use this module:** 2024-12-18T01:09:51.3176090Z 2024-12-18T01:09:51.3176243Z 1. Single-Node multi-process distributed training 2024-12-18T01:09:51.3176519Z 2024-12-18T01:09:51.3176619Z :: 2024-12-18T01:09:51.3176730Z 2024-12-18T01:09:51.3177011Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:09:51.3177564Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-12-18T01:09:51.3178009Z arguments of your training script) 2024-12-18T01:09:51.3178242Z 2024-12-18T01:09:51.3178467Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-12-18T01:09:51.3178788Z 2024-12-18T01:09:51.3178792Z 2024-12-18T01:09:51.3178957Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-12-18T01:09:51.3179210Z 2024-12-18T01:09:51.3179298Z :: 2024-12-18T01:09:51.3179425Z 2024-12-18T01:09:51.3179664Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:09:51.3180185Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-12-18T01:09:51.3180682Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:09:51.3181176Z and all other arguments of your training script) 2024-12-18T01:09:51.3181443Z 2024-12-18T01:09:51.3181545Z Node 2: 2024-12-18T01:09:51.3181663Z 2024-12-18T01:09:51.3181750Z :: 2024-12-18T01:09:51.3181875Z 2024-12-18T01:09:51.3182113Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:09:51.3182628Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-12-18T01:09:51.3183116Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:09:51.3183603Z and all other arguments of your training script) 2024-12-18T01:09:51.3183868Z 2024-12-18T01:09:51.3184047Z 3. To look up what optional arguments this module offers: 2024-12-18T01:09:51.3184320Z 2024-12-18T01:09:51.3184407Z :: 2024-12-18T01:09:51.3184529Z 2024-12-18T01:09:51.3184670Z python -m torch.distributed.launch --help 2024-12-18T01:09:51.3184923Z 2024-12-18T01:09:51.3184926Z 2024-12-18T01:09:51.3185024Z **Important Notices:** 2024-12-18T01:09:51.3185200Z 2024-12-18T01:09:51.3185386Z 1. This utility and multi-process distributed (single-node or 2024-12-18T01:09:51.3185936Z multi-node) GPU training currently only achieves the best performance using 2024-12-18T01:09:51.3186628Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-12-18T01:09:51.3187085Z use for GPU training. 2024-12-18T01:09:51.3187255Z 2024-12-18T01:09:51.3187472Z 2. In your training program, you must parse the command-line argument: 2024-12-18T01:09:51.3188038Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-12-18T01:09:51.3188706Z If your training program uses GPUs, you should ensure that your code only 2024-12-18T01:09:51.3189248Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-12-18T01:09:51.3189551Z 2024-12-18T01:09:51.3189673Z Parsing the local_rank argument 2024-12-18T01:09:51.3189865Z 2024-12-18T01:09:51.3189953Z :: 2024-12-18T01:09:51.3190081Z 2024-12-18T01:09:51.3190243Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.3190518Z >>> import argparse 2024-12-18T01:09:51.3190812Z >>> parser = argparse.ArgumentParser() 2024-12-18T01:09:51.3191241Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-12-18T01:09:51.3191656Z >>> args = parser.parse_args() 2024-12-18T01:09:51.3191875Z 2024-12-18T01:09:51.3192003Z Set your device to local rank using either 2024-12-18T01:09:51.3192239Z 2024-12-18T01:09:51.3192326Z :: 2024-12-18T01:09:51.3192446Z 2024-12-18T01:09:51.3192646Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-12-18T01:09:51.3192958Z 2024-12-18T01:09:51.3193053Z or 2024-12-18T01:09:51.3193162Z 2024-12-18T01:09:51.3193258Z :: 2024-12-18T01:09:51.3193365Z 2024-12-18T01:09:51.3193496Z >>> with torch.cuda.device(args.local_rank): 2024-12-18T01:09:51.3193846Z >>> # your code to run 2024-12-18T01:09:51.3194122Z >>> ... 2024-12-18T01:09:51.3194253Z 2024-12-18T01:09:51.3194374Z .. versionchanged:: 2.0.0 2024-12-18T01:09:51.3194546Z 2024-12-18T01:09:51.3194804Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-12-18T01:09:51.3195408Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-12-18T01:09:51.3195900Z previously used underscored ``--local_rank``. 2024-12-18T01:09:51.3196159Z 2024-12-18T01:09:51.3196397Z For backward compatibility, it may be necessary for users to handle both 2024-12-18T01:09:51.3197022Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-12-18T01:09:51.3197625Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-12-18T01:09:51.3198214Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-12-18T01:09:51.3198816Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-12-18T01:09:51.3199307Z including ``"--local-rank"`` should be sufficient. 2024-12-18T01:09:51.3199581Z 2024-12-18T01:09:51.3199813Z 3. In your training program, you are supposed to call the following function 2024-12-18T01:09:51.3200406Z at the beginning to start the distributed backend. It is strongly recommended 2024-12-18T01:09:51.3202506Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-12-18T01:09:51.3203075Z but ``env://`` is the one that is officially supported by this module. 2024-12-18T01:09:51.3203381Z 2024-12-18T01:09:51.3203485Z :: 2024-12-18T01:09:51.3203594Z 2024-12-18T01:09:51.3203803Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-12-18T01:09:51.3204270Z >>> init_method='env://') 2024-12-18T01:09:51.3204525Z 2024-12-18T01:09:51.3204768Z 4. In your training program, you can either use regular distributed functions 2024-12-18T01:09:51.3205367Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-12-18T01:09:51.3205939Z training program uses GPUs for training and you would like to use 2024-12-18T01:09:51.3206452Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-12-18T01:09:51.3206848Z here is how to configure it. 2024-12-18T01:09:51.3207170Z 2024-12-18T01:09:51.3207255Z :: 2024-12-18T01:09:51.3207377Z 2024-12-18T01:09:51.3207570Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-12-18T01:09:51.3208023Z >>> device_ids=[args.local_rank], 2024-12-18T01:09:51.3208433Z >>> output_device=args.local_rank) 2024-12-18T01:09:51.3208693Z 2024-12-18T01:09:51.3208946Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-12-18T01:09:51.3209528Z that your code will be operating on. This is generally the local rank of the 2024-12-18T01:09:51.3210115Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-12-18T01:09:51.3210775Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-12-18T01:09:51.3211200Z utility 2024-12-18T01:09:51.3211316Z 2024-12-18T01:09:51.3211574Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-12-18T01:09:51.3212175Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-12-18T01:09:51.3212787Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-12-18T01:09:51.3213330Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-12-18T01:09:51.3213817Z will not pass ``--local-rank`` when you specify this flag. 2024-12-18T01:09:51.3214096Z 2024-12-18T01:09:51.3214204Z .. warning:: 2024-12-18T01:09:51.3214333Z 2024-12-18T01:09:51.3214551Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-12-18T01:09:51.3215064Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-12-18T01:09:51.3215480Z write to a networked filesystem. See 2024-12-18T01:09:51.3215942Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-12-18T01:09:51.3216443Z how things can go wrong if you don't do this correctly. 2024-12-18T01:09:51.3216714Z 2024-12-18T01:09:51.3216718Z 2024-12-18T01:09:51.3216738Z 2024-12-18T01:09:51.3216742Z 2024-12-18T01:09:51.3216993Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.3217357Z 2024-12-18T01:09:51.3598130Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-12-18T01:09:51.3607850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.3608265Z 2024-12-18T01:09:51.3608514Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-12-18T01:09:51.3609034Z Needs to be called on all ranks in an SPMD fashion. 2024-12-18T01:09:51.3609295Z 2024-12-18T01:09:51.3609398Z Args: 2024-12-18T01:09:51.3609798Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-12-18T01:09:51.3610374Z of shards that represent the local shards on this rank. 2024-12-18T01:09:51.3610903Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-12-18T01:09:51.3611380Z shape of the overall sharded tensor. 2024-12-18T01:09:51.3611606Z 2024-12-18T01:09:51.3611713Z Keyword args: 2024-12-18T01:09:51.3612125Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:09:51.3612627Z the default process group will be used. 2024-12-18T01:09:51.3613043Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:09:51.3613544Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:09:51.3614069Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:09:51.3614485Z Default: ``False``. 2024-12-18T01:09:51.3614659Z 2024-12-18T01:09:51.3614768Z Returns: 2024-12-18T01:09:51.3615035Z A :class:`ShardedTensor` object handle on this rank 2024-12-18T01:09:51.3615311Z 2024-12-18T01:09:51.3615315Z 2024-12-18T01:09:51.3615408Z Examples: 2024-12-18T01:09:51.3616003Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-12-18T01:09:51.3616568Z each shard have a (5, 5) local tensor, we can do it like below: 2024-12-18T01:09:51.3616861Z 2024-12-18T01:09:51.3616969Z on rank 0: 2024-12-18T01:09:51.3617221Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:09:51.3617580Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:09:51.3617920Z >>> shard_offsets=[0, 0], 2024-12-18T01:09:51.3618220Z >>> shard_lengths=[5, 5], 2024-12-18T01:09:51.3618528Z >>> placement="rank:0/cuda:0" 2024-12-18T01:09:51.3618816Z >>> ) 2024-12-18T01:09:51.3619139Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:09:51.3619747Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:09:51.3620066Z 2024-12-18T01:09:51.3620158Z on rank 1: 2024-12-18T01:09:51.3620421Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:09:51.3620781Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:09:51.3621105Z >>> shard_offsets=[5, 0], 2024-12-18T01:09:51.3621405Z >>> shard_lengths=[5, 5], 2024-12-18T01:09:51.3621714Z >>> placement="rank:1/cuda:1" 2024-12-18T01:09:51.3622014Z >>> ) 2024-12-18T01:09:51.3622340Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:09:51.3622838Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:09:51.3623155Z 2024-12-18T01:09:51.3623407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.3623789Z 2024-12-18T01:09:51.3699006Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-12-18T01:09:51.3700143Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.3700533Z 2024-12-18T01:09:51.3700799Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-12-18T01:09:51.3701295Z size and sharding spec on each rank. 2024-12-18T01:09:51.3701507Z 2024-12-18T01:09:51.3701607Z Args: 2024-12-18T01:09:51.3701946Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-12-18T01:09:51.3702560Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-12-18T01:09:51.3703118Z The specification describing how to shard the Tensor. 2024-12-18T01:09:51.3703573Z global_size (Sequence[int]): Size of the sharded tensor. 2024-12-18T01:09:51.3704189Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-12-18T01:09:51.3704666Z Default: None 2024-12-18T01:09:51.3704993Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:09:51.3705497Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:09:51.3706301Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:09:51.3706835Z Default: ``False``. 2024-12-18T01:09:51.3707035Z 2024-12-18T01:09:51.3707164Z Returns: 2024-12-18T01:09:51.3707566Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-12-18T01:09:51.3708051Z tensor stored in the current rank. 2024-12-18T01:09:51.3708352Z 2024-12-18T01:09:51.3708446Z Examples: 2024-12-18T01:09:51.3708680Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.3708994Z >>> # All tensors below are of torch.int64 type. 2024-12-18T01:09:51.3709370Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:09:51.3709767Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:09:51.3710277Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-12-18T01:09:51.3710695Z >>> local_tensor 2024-12-18T01:09:51.3710956Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-12-18T01:09:51.3711261Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-12-18T01:09:51.3711711Z >>> sharding_dim = 0 2024-12-18T01:09:51.3712012Z >>> sharding_spec = ChunkShardingSpec( 2024-12-18T01:09:51.3712346Z dim=sharding_dim, 2024-12-18T01:09:51.3712629Z placements=[ 2024-12-18T01:09:51.3712901Z "rank:0/cuda:0", 2024-12-18T01:09:51.3713175Z "rank:1/cuda:1", 2024-12-18T01:09:51.3713452Z ], 2024-12-18T01:09:51.3713678Z ) 2024-12-18T01:09:51.3714062Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-12-18T01:09:51.3714533Z >>> st 2024-12-18T01:09:51.3714795Z ShardedTensor( 2024-12-18T01:09:51.3715102Z ShardedTensorMetadata( 2024-12-18T01:09:51.3715397Z shards_metadata=[ 2024-12-18T01:09:51.3715949Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-12-18T01:09:51.3716598Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-12-18T01:09:51.3717059Z ], 2024-12-18T01:09:51.3717307Z size=torch.Size([2, 4]) 2024-12-18T01:09:51.3717606Z ) 2024-12-18T01:09:51.3717828Z >>> st.local_tensor() 2024-12-18T01:09:51.3718103Z tensor([1, 2, 3, 4]) # Rank 0 2024-12-18T01:09:51.3718386Z tensor([3, 4, 5, 6]) # Rank 1 2024-12-18T01:09:51.3718587Z 2024-12-18T01:09:51.3718855Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-12-18T01:09:51.3719489Z rank validations, and we only validate the local shard on the current rank. 2024-12-18T01:09:51.3720072Z We fully rely on the user to ensure local tensor is sharded based on the 2024-12-18T01:09:51.3720504Z sharding spec. 2024-12-18T01:09:51.3720664Z 2024-12-18T01:09:51.3720934Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.3721292Z 2024-12-18T01:09:51.3722003Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-12-18T01:09:51.3723042Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.3723415Z 2024-12-18T01:09:51.3723678Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-12-18T01:09:51.3724134Z single local shard. 2024-12-18T01:09:51.3724296Z 2024-12-18T01:09:51.3724517Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-12-18T01:09:51.3725100Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-12-18T01:09:51.3725572Z we swap local shards directly. 2024-12-18T01:09:51.3726027Z For more generic cases, we merge different shards across different ranks and split 2024-12-18T01:09:51.3726663Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-12-18T01:09:51.3727028Z 2024-12-18T01:09:51.3727113Z Args: 2024-12-18T01:09:51.3727526Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-12-18T01:09:51.3728108Z specification describing how the tensor is sharded. 2024-12-18T01:09:51.3728394Z 2024-12-18T01:09:51.3728484Z Returns: 2024-12-18T01:09:51.3728816Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-12-18T01:09:51.3729131Z 2024-12-18T01:09:51.3729233Z Examples: 2024-12-18T01:09:51.3729451Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.3729747Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:09:51.3730157Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:09:51.3730574Z >>> tensor = torch.stack([tensor, tensor]) 2024-12-18T01:09:51.3730899Z >>> tensor 2024-12-18T01:09:51.3731145Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-12-18T01:09:51.3731499Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-12-18T01:09:51.3731845Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-12-18T01:09:51.3732197Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-12-18T01:09:51.3732603Z >>> sharding_dim = 0 2024-12-18T01:09:51.3732877Z >>> spec = ChunkShardingSpec( 2024-12-18T01:09:51.3733186Z dim=sharding_dim, 2024-12-18T01:09:51.3733479Z placements=[ 2024-12-18T01:09:51.3733749Z "rank:0/cuda:0", 2024-12-18T01:09:51.3734037Z "rank:1/cuda:1", 2024-12-18T01:09:51.3734309Z "rank:2/cuda:2", 2024-12-18T01:09:51.3734591Z "rank:3/cuda:3", 2024-12-18T01:09:51.3734866Z ], 2024-12-18T01:09:51.3735090Z ) 2024-12-18T01:09:51.3735304Z >>> current_offsets = [0] * 2 2024-12-18T01:09:51.3735612Z >>> current_offsets[0] = rank * 2 2024-12-18T01:09:51.3735934Z >>> shard_metadata = ShardMetadata( 2024-12-18T01:09:51.3736613Z shard_offsets=copy.deepcopy(current_offsets), 2024-12-18T01:09:51.3736999Z shard_sizes=tensor.size(), 2024-12-18T01:09:51.3737341Z placement=spec.placements[rank], 2024-12-18T01:09:51.3737655Z ) 2024-12-18T01:09:51.3737878Z >>> local_shards = [ 2024-12-18T01:09:51.3738133Z Shard( 2024-12-18T01:09:51.3738375Z tensor=tensor, 2024-12-18T01:09:51.3738657Z metadata=shard_metadata, 2024-12-18T01:09:51.3738963Z ) 2024-12-18T01:09:51.3739185Z ] 2024-12-18T01:09:51.3739541Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-12-18T01:09:51.3739991Z >>> sharding_dim = 1 2024-12-18T01:09:51.3740280Z >>> resharding_spec = ChunkShardingSpec( 2024-12-18T01:09:51.3740615Z dim=sharding_dim, 2024-12-18T01:09:51.3740899Z placements=[ 2024-12-18T01:09:51.3741168Z "rank:0/cuda:0", 2024-12-18T01:09:51.3741453Z "rank:1/cuda:1", 2024-12-18T01:09:51.3741730Z "rank:2/cuda:2", 2024-12-18T01:09:51.3742011Z "rank:3/cuda:3", 2024-12-18T01:09:51.3742284Z ], 2024-12-18T01:09:51.3742511Z ) 2024-12-18T01:09:51.3742748Z >>> st.reshard(resharding_spec) 2024-12-18T01:09:51.3743061Z >>> tensor = st.local_shards()[0].tensor 2024-12-18T01:09:51.3743377Z >>> tensor 2024-12-18T01:09:51.3743658Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-12-18T01:09:51.3744057Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-12-18T01:09:51.3744452Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-12-18T01:09:51.3744839Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-12-18T01:09:51.3745104Z 2024-12-18T01:09:51.3745356Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.3745737Z 2024-12-18T01:09:51.3841485Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-12-18T01:09:51.3842489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.3842889Z 2024-12-18T01:09:51.3843108Z Representation of a sharding plan, describes how to shard a module 2024-12-18T01:09:51.3843707Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-12-18T01:09:51.3844401Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-12-18T01:09:51.3845053Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-12-18T01:09:51.3845416Z 2024-12-18T01:09:51.3845503Z Args: 2024-12-18T01:09:51.3845894Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-12-18T01:09:51.3846558Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-12-18T01:09:51.3847117Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-12-18T01:09:51.3847763Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-12-18T01:09:51.3848255Z a parameter to a `ShardingSpec`. 2024-12-18T01:09:51.3848931Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-12-18T01:09:51.3849424Z to a `Sharder` object. 2024-12-18T01:09:51.3849968Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-12-18T01:09:51.3850680Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-12-18T01:09:51.3851290Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-12-18T01:09:51.3851758Z Default: `None` 2024-12-18T01:09:51.3852182Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-12-18T01:09:51.3852887Z a module's sharded output to be returned as a Tensor from its local shards to 2024-12-18T01:09:51.3853508Z ensure further processing in a data parallel fashion. ("" in list means the 2024-12-18T01:09:51.3853979Z root module). 2024-12-18T01:09:51.3854229Z Default: None 2024-12-18T01:09:51.3854487Z Example: 2024-12-18T01:09:51.3854902Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-12-18T01:09:51.3855593Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-12-18T01:09:51.3855993Z 2024-12-18T01:09:51.3856181Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-12-18T01:09:51.3856570Z >>> class MyModule(nn.Module): 2024-12-18T01:09:51.3856897Z >>> def __init__(self) -> None: 2024-12-18T01:09:51.3857224Z >>> super().__init__() 2024-12-18T01:09:51.3857531Z >>> self.fc1 = nn.Linear() 2024-12-18T01:09:51.3857848Z >>> self.gelu = nn.GELU() 2024-12-18T01:09:51.3858153Z >>> self.fc2 = nn.Linear() 2024-12-18T01:09:51.3858462Z >>> self.relu = nn.Linear() 2024-12-18T01:09:51.3858756Z >>> 2024-12-18T01:09:51.3858985Z >>> def forward(self, input): 2024-12-18T01:09:51.3859374Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-12-18T01:09:51.3859661Z 2024-12-18T01:09:51.3859666Z 2024-12-18T01:09:51.3859807Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-12-18T01:09:51.3860156Z >>> sharding_plan = ShardingPlan( 2024-12-18T01:09:51.3860461Z >>> plan={ 2024-12-18T01:09:51.3860708Z >>> "fc1.weight": spec1, 2024-12-18T01:09:51.3861012Z >>> "fc2.weight": spec2 2024-12-18T01:09:51.3861305Z >>> }, 2024-12-18T01:09:51.3861528Z >>> output_plan={ 2024-12-18T01:09:51.3861807Z >>> "fc2": output_spec 2024-12-18T01:09:51.3862094Z >>> }, 2024-12-18T01:09:51.3862339Z >>> return_local_tensor=["fc2"] 2024-12-18T01:09:51.3862632Z >>> ) 2024-12-18T01:09:51.3862765Z 2024-12-18T01:09:51.3863018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.3863395Z 2024-12-18T01:09:51.4522252Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-12-18T01:09:51.4523428Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4523809Z 2024-12-18T01:09:51.4523934Z Run post-localSGD algorithm. 2024-12-18T01:09:51.4524124Z 2024-12-18T01:09:51.4524374Z This DDP communication hook is used for running post-localSGD algorithm, 2024-12-18T01:09:51.4524879Z by combining with a model averaging component (e.g., 2024-12-18T01:09:51.4525475Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-12-18T01:09:51.4526037Z that runs after the optimizer step. 2024-12-18T01:09:51.4526247Z 2024-12-18T01:09:51.4526360Z Args: 2024-12-18T01:09:51.4526720Z state (PostLocalSGDState): State information to run post-localSGD. 2024-12-18T01:09:51.4527343Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-12-18T01:09:51.4528397Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:09:51.4529193Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:09:51.4529723Z only exactly one tensor is stored in this bucket. 2024-12-18T01:09:51.4530000Z 2024-12-18T01:09:51.4530092Z Returns: 2024-12-18T01:09:51.4530471Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:09:51.4530831Z 2024-12-18T01:09:51.4530950Z Example:: 2024-12-18T01:09:51.4531168Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.4531598Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-12-18T01:09:51.4532210Z start_localSGD_iter=10) 2024-12-18T01:09:51.4532630Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:51.4533256Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-12-18T01:09:51.4534064Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-12-18T01:09:51.4534519Z 2024-12-18T01:09:51.4534771Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4535148Z 2024-12-18T01:09:51.4570371Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=343. 2024-12-18T01:09:51.4571449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4571842Z 2024-12-18T01:09:51.4571956Z Implement PowerSGD algorithm. 2024-12-18T01:09:51.4572163Z 2024-12-18T01:09:51.4572404Z This DDP communication hook implements PowerSGD gradient compression 2024-12-18T01:09:51.4572994Z algorithm described in the `paper `_. 2024-12-18T01:09:51.4573590Z Once gradient tensors are aggregated across all workers, this hook applies 2024-12-18T01:09:51.4574043Z compression as follows: 2024-12-18T01:09:51.4574219Z 2024-12-18T01:09:51.4574653Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-12-18T01:09:51.4575214Z 2024-12-18T01:09:51.4575628Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-12-18T01:09:51.4576167Z 2024-12-18T01:09:51.4576569Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-12-18T01:09:51.4577096Z 2024-12-18T01:09:51.4577210Z 2. Handles uncompressed tensors: 2024-12-18T01:09:51.4577407Z 2024-12-18T01:09:51.4577929Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-12-18T01:09:51.4578550Z 2024-12-18T01:09:51.4578898Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-12-18T01:09:51.4579353Z 2024-12-18T01:09:51.4579597Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-12-18T01:09:51.4579944Z 2024-12-18T01:09:51.4580193Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-12-18T01:09:51.4580858Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-12-18T01:09:51.4581291Z 2024-12-18T01:09:51.4581456Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-12-18T01:09:51.4581723Z 2024-12-18T01:09:51.4581832Z 3.3. Allreduces Ps as a batch; 2024-12-18T01:09:51.4582048Z 2024-12-18T01:09:51.4582169Z 3.4. Orthogonalizes each P in Ps; 2024-12-18T01:09:51.4582397Z 2024-12-18T01:09:51.4582594Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-12-18T01:09:51.4582901Z 2024-12-18T01:09:51.4583175Z 3.6. Allreduces Qs as a batch; 2024-12-18T01:09:51.4583372Z 2024-12-18T01:09:51.4583686Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-12-18T01:09:51.4584095Z 2024-12-18T01:09:51.4584511Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-12-18T01:09:51.4585306Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-12-18T01:09:51.4586135Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-12-18T01:09:51.4586690Z 2024-12-18T01:09:51.4586776Z Args: 2024-12-18T01:09:51.4587411Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-12-18T01:09:51.4588400Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-12-18T01:09:51.4589002Z and ``min_compression_rate``. 2024-12-18T01:09:51.4589626Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:09:51.4590414Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:09:51.4590949Z only exactly one tensor is stored in this bucket. 2024-12-18T01:09:51.4591225Z 2024-12-18T01:09:51.4591316Z Returns: 2024-12-18T01:09:51.4591693Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:09:51.4592050Z 2024-12-18T01:09:51.4592164Z Example:: 2024-12-18T01:09:51.4592384Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.4592843Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-12-18T01:09:51.4593406Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-12-18T01:09:51.4593860Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-12-18T01:09:51.4594135Z 2024-12-18T01:09:51.4594401Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4594767Z 2024-12-18T01:09:51.4609802Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=37. 2024-12-18T01:09:51.4610919Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4611296Z 2024-12-18T01:09:51.4611484Z Averages parameters periodically after the warm-up stage. 2024-12-18T01:09:51.4611797Z 2024-12-18T01:09:51.4612055Z This can be used for running `post-local SGD `_, 2024-12-18T01:09:51.4612634Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-12-18T01:09:51.4613185Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-12-18T01:09:51.4613533Z 2024-12-18T01:09:51.4613633Z Args: 2024-12-18T01:09:51.4613928Z period (int): The number of steps per model averaging. 2024-12-18T01:09:51.4614462Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-12-18T01:09:51.4614988Z Otherwise, only DDP needs to be used. 2024-12-18T01:09:51.4615439Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-12-18T01:09:51.4615886Z model averaging is skipped. 2024-12-18T01:09:51.4616315Z process_group: The process group to be used for all-reduce. 2024-12-18T01:09:51.4616760Z If ``None``, the default process group, which 2024-12-18T01:09:51.4617204Z is created by :func:`torch.distributed.init_process_group`, 2024-12-18T01:09:51.4617643Z will be used. (default: ``None``) 2024-12-18T01:09:51.4617884Z 2024-12-18T01:09:51.4617982Z Example:: 2024-12-18T01:09:51.4618106Z 2024-12-18T01:09:51.4618244Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.4618696Z >>> import torch 2024-12-18T01:09:51.4618962Z >>> import torch.distributed as dist 2024-12-18T01:09:51.4619506Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-12-18T01:09:51.4620213Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:09:51.4620708Z >>> import torch.nn as nn 2024-12-18T01:09:51.4620991Z >>> 2024-12-18T01:09:51.4621297Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:09:51.4621707Z >>> torch.cuda.set_device(rank) 2024-12-18T01:09:51.4622054Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-12-18T01:09:51.4622453Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:09:51.4622922Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:09:51.4623270Z >>> ) 2024-12-18T01:09:51.4623534Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:09:51.4624095Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:09:51.4624899Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:51.4625440Z >>> 2024-12-18T01:09:51.4625978Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:09:51.4626936Z >>> # After 100 steps, run model averaging every 4 steps. 2024-12-18T01:09:51.4627633Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:09:51.4628395Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:09:51.4628879Z >>> for step in range(0, 200): 2024-12-18T01:09:51.4629196Z >>> optimizer.zero_grad() 2024-12-18T01:09:51.4629522Z >>> loss = loss_fn(output, labels) 2024-12-18T01:09:51.4629839Z >>> loss.backward() 2024-12-18T01:09:51.4630122Z >>> optimizer.step() 2024-12-18T01:09:51.4630498Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-12-18T01:09:51.4631015Z >>> # inter-node communication only occurs every 4 iterations after 2024-12-18T01:09:51.4631465Z >>> # the initial ``warmup_steps`` period. 2024-12-18T01:09:51.4631853Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:09:51.4632138Z 2024-12-18T01:09:51.4632389Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4632767Z 2024-12-18T01:09:51.4633640Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-12-18T01:09:51.4634841Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4635229Z 2024-12-18T01:09:51.4635567Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-12-18T01:09:51.4636204Z 2024-12-18T01:09:51.4636515Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-12-18T01:09:51.4637153Z by using different periods concurrently after the warm-up stage. 2024-12-18T01:09:51.4637878Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-12-18T01:09:51.4638737Z that supports `post-local SGD `_, which essentially only supports 2024-12-18T01:09:51.4639506Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-12-18T01:09:51.4640275Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-12-18T01:09:51.4641042Z Similarly, the process groups within this class do not have such an intra-machine process 2024-12-18T01:09:51.4641738Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-12-18T01:09:51.4642140Z 2024-12-18T01:09:51.4642372Z Args: 2024-12-18T01:09:51.4642761Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-12-18T01:09:51.4643350Z process group size, used for initializing process groups of 2024-12-18T01:09:51.4643891Z different sizes in a hierarchy to average parameters concurrently. 2024-12-18T01:09:51.4644455Z Particularly, at each iteration, there will be at most a single 2024-12-18T01:09:51.4645030Z process group that runs averaging -- the period of such group should 2024-12-18T01:09:51.4645601Z have the largest period which the current step can be divided by. 2024-12-18T01:09:51.4646224Z For example, if the dict has three keys: 2, 4, and 8, 2024-12-18T01:09:51.4646731Z then this means totally three process groups will be created to 2024-12-18T01:09:51.4647266Z average parameters every 2, 4, and 8 iterations, respectively. 2024-12-18T01:09:51.4647799Z At the 4th iteration, only the second process group will run 2024-12-18T01:09:51.4648299Z averaging, because the first process group should be a 2024-12-18T01:09:51.4648827Z subset of the second process group, and no need to execute the first 2024-12-18T01:09:51.4649300Z process group redundantly. 2024-12-18T01:09:51.4649748Z On the other hand, the third process group can only be triggered 2024-12-18T01:09:51.4650288Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-12-18T01:09:51.4650946Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-12-18T01:09:51.4651809Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-12-18T01:09:51.4652566Z If ``None``, the default process group, which is created 2024-12-18T01:09:51.4653080Z by :func:`torch.distributed.init_process_group`, will be used. 2024-12-18T01:09:51.4653541Z (default: ``None``) 2024-12-18T01:09:51.4653777Z 2024-12-18T01:09:51.4653890Z Example:: 2024-12-18T01:09:51.4654129Z >>> # xdoctest: +SKIP('undefined rank') 2024-12-18T01:09:51.4654486Z >>> from collections import OrderedDict 2024-12-18T01:09:51.4654817Z >>> import torch 2024-12-18T01:09:51.4655101Z >>> import torch.distributed as dist 2024-12-18T01:09:51.4655608Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:09:51.4656108Z >>> PostLocalSGDState, 2024-12-18T01:09:51.4656410Z >>> post_localSGD_hook, 2024-12-18T01:09:51.4656695Z >>> ) 2024-12-18T01:09:51.4657203Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-12-18T01:09:51.4657808Z >>> import torch.nn as nn 2024-12-18T01:09:51.4658079Z >>> 2024-12-18T01:09:51.4658383Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:09:51.4658795Z >>> torch.cuda.set_device(rank) 2024-12-18T01:09:51.4659151Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:09:51.4659556Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:09:51.4659954Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:09:51.4660302Z >>> ) 2024-12-18T01:09:51.4660574Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:09:51.4661116Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-12-18T01:09:51.4661631Z >>> subgroup, _ = dist.new_subgroups() 2024-12-18T01:09:51.4662160Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-12-18T01:09:51.4662828Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:51.4663193Z >>> 2024-12-18T01:09:51.4663598Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-12-18T01:09:51.4664123Z >>> # the 16 processes every 16 iterations. 2024-12-18T01:09:51.4664548Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-12-18T01:09:51.4665071Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-12-18T01:09:51.4665745Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:09:51.4666448Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:09:51.4667046Z >>> # After 100 steps, run model averaging at two levels. 2024-12-18T01:09:51.4667428Z >>> for step in range(0, 200): 2024-12-18T01:09:51.4667729Z >>> optimizer.zero_grad() 2024-12-18T01:09:51.4668047Z >>> loss = loss_fn(output, labels) 2024-12-18T01:09:51.4668462Z >>> loss.backward() 2024-12-18T01:09:51.4668744Z >>> optimizer.step() 2024-12-18T01:09:51.4669084Z >>> # Average parameters after ``optimizer.step()``. 2024-12-18T01:09:51.4669629Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-12-18T01:09:51.4670198Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:09:51.4670478Z 2024-12-18T01:09:51.4670576Z .. warning :: 2024-12-18T01:09:51.4670964Z The last group size in the dict must be the size of the provided ``process_group``, 2024-12-18T01:09:51.4671566Z which indicates model averaging at the highest level of the hierarchy. 2024-12-18T01:09:51.4672227Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-12-18T01:09:51.4672642Z 2024-12-18T01:09:51.4672737Z .. warning :: 2024-12-18T01:09:51.4673120Z `HierarchicalModelAverager` is experimental and subject to change. 2024-12-18T01:09:51.4673476Z 2024-12-18T01:09:51.4673729Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4674107Z 2024-12-18T01:09:51.4830106Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-12-18T01:09:51.4831206Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4831599Z 2024-12-18T01:09:51.4831887Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-12-18T01:09:51.4832573Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-12-18T01:09:51.4832983Z 2024-12-18T01:09:51.4833161Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-12-18T01:09:51.4833485Z 2024-12-18T01:09:51.4833604Z .. warning:: 2024-12-18T01:09:51.4834140Z Current implementation only supports loading Tensors. 2024-12-18T01:09:51.4834632Z 2024-12-18T01:09:51.4834750Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4835074Z >>> sd = {"mode": model} 2024-12-18T01:09:51.4835337Z >>> dcp.load( 2024-12-18T01:09:51.4835551Z >>> sd, 2024-12-18T01:09:51.4835830Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:09:51.4836397Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:09:51.4836756Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:09:51.4837068Z >>> ) 2024-12-18T01:09:51.4837182Z 2024-12-18T01:09:51.4837447Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4837813Z 2024-12-18T01:09:51.4838514Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-12-18T01:09:51.4839556Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4840096Z 2024-12-18T01:09:51.4840460Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-12-18T01:09:51.4841262Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-12-18T01:09:51.4841822Z metadata file, like Torch Save files. 2024-12-18T01:09:51.4842035Z 2024-12-18T01:09:51.4842229Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-12-18T01:09:51.4842517Z 2024-12-18T01:09:51.4842625Z .. warning:: 2024-12-18T01:09:51.4842925Z Current implementation only supports loading Tensors. 2024-12-18T01:09:51.4843216Z 2024-12-18T01:09:51.4843332Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4843651Z >>> sd = {"mode": model} 2024-12-18T01:09:51.4843988Z >>> dcp.load( 2024-12-18T01:09:51.4844217Z >>> sd, 2024-12-18T01:09:51.4844487Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:09:51.4844878Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:09:51.4845240Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:09:51.4845556Z >>> ) 2024-12-18T01:09:51.4845671Z 2024-12-18T01:09:51.4845939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4846308Z 2024-12-18T01:09:51.4887193Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-12-18T01:09:51.4888244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4888635Z 2024-12-18T01:09:51.4888846Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-12-18T01:09:51.4889176Z 2024-12-18T01:09:51.4889358Z This is the current recommended way to checkpoint FSDP. 2024-12-18T01:09:51.4889738Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.4890055Z >>> import torch.distributed.checkpoint as dist_cp 2024-12-18T01:09:51.4890411Z >>> # Save 2024-12-18T01:09:51.4890650Z >>> model: torch.nn.Model 2024-12-18T01:09:51.4890935Z >>> optim_params = model.parameters() 2024-12-18T01:09:51.4891304Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-12-18T01:09:51.4891655Z >>> # Save 2024-12-18T01:09:51.4892005Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:09:51.4892441Z >>> state_dict = { 2024-12-18T01:09:51.4892753Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-12-18T01:09:51.4893142Z >>> "model": model.state_dict() 2024-12-18T01:09:51.4893455Z >>> } 2024-12-18T01:09:51.4893693Z >>> dist_cp.save_state_dict( 2024-12-18T01:09:51.4894006Z >>> state_dict=optim_state, 2024-12-18T01:09:51.4894385Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-12-18T01:09:51.4894824Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-12-18T01:09:51.4895165Z >>> ) 2024-12-18T01:09:51.4895380Z >>> 2024-12-18T01:09:51.4895592Z >>> # Load 2024-12-18T01:09:51.4895939Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:09:51.4896422Z >>> model_state_dict = model_tp.state_dict() 2024-12-18T01:09:51.4896766Z >>> checkpoint = { 2024-12-18T01:09:51.4897042Z >>> "model": model_state_dict 2024-12-18T01:09:51.4897347Z >>> } 2024-12-18T01:09:51.4897567Z >>> dist_cp.load_state_dict( 2024-12-18T01:09:51.4897870Z >>> state_dict=checkpoint, 2024-12-18T01:09:51.4898263Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-12-18T01:09:51.4898701Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-12-18T01:09:51.4899035Z >>> ) 2024-12-18T01:09:51.4899302Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-12-18T01:09:51.4899654Z >>> 2024-12-18T01:09:51.4899952Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-12-18T01:09:51.4900345Z >>> model_state_dict, 2024-12-18T01:09:51.4900646Z >>> optimizer_key="optimizer", 2024-12-18T01:09:51.4901031Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-12-18T01:09:51.4901523Z >>> ) 2024-12-18T01:09:51.4901737Z >>> 2024-12-18T01:09:51.4902007Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-12-18T01:09:51.4902401Z >>> model, optim, optim_state["optimizer"] 2024-12-18T01:09:51.4902722Z >>> ) 2024-12-18T01:09:51.4902938Z >>> 2024-12-18T01:09:51.4903179Z >>> optim.load_state_dict(flattened_osd) 2024-12-18T01:09:51.4903407Z 2024-12-18T01:09:51.4903670Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4904034Z 2024-12-18T01:09:51.4911885Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-12-18T01:09:51.4912972Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4913351Z 2024-12-18T01:09:51.4913637Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-12-18T01:09:51.4914061Z 2024-12-18T01:09:51.4914351Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-12-18T01:09:51.4914772Z 2024-12-18T01:09:51.4915049Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:09:51.4915572Z will be visible to the whole process. 2024-12-18T01:09:51.4915788Z 2024-12-18T01:09:51.4916077Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-12-18T01:09:51.4916468Z 2024-12-18T01:09:51.4916605Z 1) set_up_planner - called on all ranks. 2024-12-18T01:09:51.4916965Z Signals the start of a checkpoint save. 2024-12-18T01:09:51.4917194Z 2024-12-18T01:09:51.4917320Z 2) create_local_plan - called on all ranks. 2024-12-18T01:09:51.4917844Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-12-18T01:09:51.4918364Z 2024-12-18T01:09:51.4918571Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:09:51.4919122Z Takes the SavePlan from all ranks and make any global decision. 2024-12-18T01:09:51.4919485Z 2024-12-18T01:09:51.4919664Z 4) finish_plan - called on all ranks. 2024-12-18T01:09:51.4920158Z This gives each rank a chance to adjust to global planning decisions. 2024-12-18T01:09:51.4920487Z 2024-12-18T01:09:51.4920653Z 5) resolve_data - called multiple times on each rank 2024-12-18T01:09:51.4921124Z Lookups a value on the `state_dict` for the storage layer to write. 2024-12-18T01:09:51.4921439Z 2024-12-18T01:09:51.4921734Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-12-18T01:09:51.4922342Z most changes can be expressed by changes in a single method. 2024-12-18T01:09:51.4922644Z 2024-12-18T01:09:51.4922769Z There are 3 usual patterns of extension: 2024-12-18T01:09:51.4923004Z 2024-12-18T01:09:51.4923256Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-12-18T01:09:51.4923857Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-12-18T01:09:51.4924195Z 2024-12-18T01:09:51.4924322Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4924666Z >>> class RenamePlanner(DefaultSavePlanner): 2024-12-18T01:09:51.4925015Z >>> def set_up_planner( 2024-12-18T01:09:51.4925286Z >>> self, 2024-12-18T01:09:51.4925544Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:09:51.4925888Z >>> storage_meta: Optional[StorageMeta], 2024-12-18T01:09:51.4926220Z >>> is_coordinator: bool, 2024-12-18T01:09:51.4926513Z >>> ) -> None: 2024-12-18T01:09:51.4926774Z >>> # prefix all keys with `foo_`` 2024-12-18T01:09:51.4927288Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-12-18T01:09:51.4927691Z 2024-12-18T01:09:51.4928029Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-12-18T01:09:51.4928592Z 2024-12-18T01:09:51.4928720Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4929060Z >>> class FP16Planner(DefaultSavePlanner): 2024-12-18T01:09:51.4929406Z >>> def create_local_plan(self): 2024-12-18T01:09:51.4929742Z >>> plan = super().create_local_plan() 2024-12-18T01:09:51.4930078Z >>> for p in plan: 2024-12-18T01:09:51.4930382Z >>> if p.tensor_data is not None: 2024-12-18T01:09:51.4930762Z >>> p.tensor_data.properties.dtype = torch.float16 2024-12-18T01:09:51.4931138Z >>> return plan 2024-12-18T01:09:51.4931388Z >>> 2024-12-18T01:09:51.4931628Z >>> def resolve_data(self, write_item): 2024-12-18T01:09:51.4931982Z >>> item = super().resolve_data(write_item) 2024-12-18T01:09:51.4932537Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-12-18T01:09:51.4932938Z 2024-12-18T01:09:51.4933275Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-12-18T01:09:51.4933745Z 2024-12-18T01:09:51.4933859Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4934195Z >>> from itertools import zip_longest 2024-12-18T01:09:51.4934529Z >>> from dataclasses import replace 2024-12-18T01:09:51.4934919Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-12-18T01:09:51.4935482Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-12-18T01:09:51.4936019Z >>> # This sample doesn't handle ShardedTensors 2024-12-18T01:09:51.4936591Z >>> def create_global_plan(self, all_plans): 2024-12-18T01:09:51.4936994Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2024-12-18T01:09:51.4937373Z >>> items_per_rank = [ 2024-12-18T01:09:51.4937694Z >>> [item for item in items if item is not None] 2024-12-18T01:09:51.4938118Z >>> for items in zip(*zip_longest(*iters), strict=True) 2024-12-18T01:09:51.4938494Z >>> ] 2024-12-18T01:09:51.4938738Z >>> all_plans = [ 2024-12-18T01:09:51.4939032Z >>> replace(plan, items=items) 2024-12-18T01:09:51.4939457Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2024-12-18T01:09:51.4939852Z >>> ] 2024-12-18T01:09:51.4940140Z >>> return super().create_global_plan(all_plans) 2024-12-18T01:09:51.4940408Z 2024-12-18T01:09:51.4940674Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-12-18T01:09:51.4941337Z accomplished by having each rank contribute their data items in the local plan and 2024-12-18T01:09:51.4941844Z the global planner aggregate them: 2024-12-18T01:09:51.4942049Z 2024-12-18T01:09:51.4942166Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4942550Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-12-18T01:09:51.4942960Z >>> def create_local_plan(self) -> SavePlan: 2024-12-18T01:09:51.4943326Z >>> plan = super().create_local_plan() 2024-12-18T01:09:51.4943728Z >>> return replace(plan, planner_data="per-rank-data") 2024-12-18T01:09:51.4944097Z >>> 2024-12-18T01:09:51.4944503Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-12-18T01:09:51.4945114Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-12-18T01:09:51.4945591Z >>> merged_data = [p.planner_data for p in global_plan] 2024-12-18T01:09:51.4946038Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-12-18T01:09:51.4946439Z >>> return global_plan, metadata 2024-12-18T01:09:51.4946653Z 2024-12-18T01:09:51.4946904Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4947278Z 2024-12-18T01:09:51.4947910Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=272. 2024-12-18T01:09:51.4948927Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.4949435Z 2024-12-18T01:09:51.4949718Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-12-18T01:09:51.4950132Z 2024-12-18T01:09:51.4950416Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-12-18T01:09:51.4950830Z 2024-12-18T01:09:51.4951108Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:09:51.4951628Z will be visible to the whole process. 2024-12-18T01:09:51.4951843Z 2024-12-18T01:09:51.4952131Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-12-18T01:09:51.4952523Z 2024-12-18T01:09:51.4952660Z 1) set_up_planner - called on all ranks. 2024-12-18T01:09:51.4953077Z Signals the start of loading a checkpoint. 2024-12-18T01:09:51.4953326Z 2024-12-18T01:09:51.4953451Z 2) create_local_plan - called on all ranks. 2024-12-18T01:09:51.4953967Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-12-18T01:09:51.4954383Z 2024-12-18T01:09:51.4954563Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:09:51.4955064Z Takes the LoadPlan from all ranks and make any global decision. 2024-12-18T01:09:51.4955373Z 2024-12-18T01:09:51.4955533Z 4) load_bytes - called multiple times on each rank 2024-12-18T01:09:51.4955952Z This is called once per non-tensor value in state_dict. 2024-12-18T01:09:51.4956239Z 2024-12-18T01:09:51.4956459Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-12-18T01:09:51.4956982Z They are called in pair for each Tensor value in state_dict. 2024-12-18T01:09:51.4957284Z 2024-12-18T01:09:51.4957581Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-12-18T01:09:51.4958196Z most changes can be expressed by changes in a single method. 2024-12-18T01:09:51.4958488Z 2024-12-18T01:09:51.4958632Z There are two usual patterns of extension: 2024-12-18T01:09:51.4958870Z 2024-12-18T01:09:51.4959135Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-12-18T01:09:51.4959752Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-12-18T01:09:51.4960351Z to keep a reference to the original state_dict as load happens in place so 2024-12-18T01:09:51.4960821Z we need to be able to perform it in place 2024-12-18T01:09:51.4961062Z 2024-12-18T01:09:51.4961175Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4961533Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-12-18T01:09:51.4961880Z >>> def set_up_planner( 2024-12-18T01:09:51.4962140Z >>> self, 2024-12-18T01:09:51.4962398Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:09:51.4962718Z >>> metadata: Metadata, 2024-12-18T01:09:51.4963016Z >>> is_coordinator: bool, 2024-12-18T01:09:51.4963296Z >>> ) -> None: 2024-12-18T01:09:51.4963568Z >>> self.original_state_dict = state_dict 2024-12-18T01:09:51.4963985Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-12-18T01:09:51.4964366Z >>> 2024-12-18T01:09:51.4964602Z >>> if self.flatten_sharded_tensors: 2024-12-18T01:09:51.4964975Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-12-18T01:09:51.4965330Z >>> 2024-12-18T01:09:51.4965556Z >>> if self.flatten_state_dict: 2024-12-18T01:09:51.4965961Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-12-18T01:09:51.4966352Z >>> 2024-12-18T01:09:51.4966568Z >>> self.state_dict = state_dict 2024-12-18T01:09:51.4966896Z >>> self.metadata = metadata 2024-12-18T01:09:51.4967228Z >>> self.is_coordinator = is_coordinator 2024-12-18T01:09:51.4967550Z >>> 2024-12-18T01:09:51.4967795Z >>> def load_bytes(self, read_item, value): 2024-12-18T01:09:51.4968127Z >>> # Remove the "foo_" prefix 2024-12-18T01:09:51.4968660Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-12-18T01:09:51.4969173Z 2024-12-18T01:09:51.4969177Z 2024-12-18T01:09:51.4969437Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-12-18T01:09:51.4969825Z 2024-12-18T01:09:51.4969940Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:51.4970323Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-12-18T01:09:51.4970718Z >>> def resolve_tensor(self, read_item): 2024-12-18T01:09:51.4971073Z >>> tensor = super().resolve_tensor(read_item) 2024-12-18T01:09:51.4971477Z >>> return torch.empty_like(tensor, device="cpu") 2024-12-18T01:09:51.4971826Z >>> 2024-12-18T01:09:51.4972075Z >>> def commit_tensor(self, read_item, tensor): 2024-12-18T01:09:51.4972531Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-12-18T01:09:51.4972801Z 2024-12-18T01:09:51.4973063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.4973429Z 2024-12-18T01:09:51.5100295Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-12-18T01:09:51.5101481Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.5101859Z 2024-12-18T01:09:51.5102019Z Load a distributed ``state_dict`` in SPMD style. 2024-12-18T01:09:51.5102273Z 2024-12-18T01:09:51.5102455Z Each rank will try to read the least amount of data necessary 2024-12-18T01:09:51.5103000Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-12-18T01:09:51.5103612Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-12-18T01:09:51.5103976Z 2024-12-18T01:09:51.5104261Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:09:51.5104903Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-12-18T01:09:51.5105454Z ``load_state_dict`` once the deserialization is complete. 2024-12-18T01:09:51.5105984Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2024-12-18T01:09:51.5106525Z it in the ``state_dict`` with the deserialized object. 2024-12-18T01:09:51.5106798Z 2024-12-18T01:09:51.5106908Z .. warning:: 2024-12-18T01:09:51.5107220Z All tensors in ``state_dict`` must be allocated on their 2024-12-18T01:09:51.5107679Z destination device *prior to* calling this function. 2024-12-18T01:09:51.5107950Z 2024-12-18T01:09:51.5108193Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-12-18T01:09:51.5108694Z on state_dict. 2024-12-18T01:09:51.5108855Z 2024-12-18T01:09:51.5108949Z .. warning:: 2024-12-18T01:09:51.5109305Z Users must call `load_state_dict` on the root module to ensure load 2024-12-18T01:09:51.5109818Z pos-processing and non-tensor data properly propagates. 2024-12-18T01:09:51.5110109Z 2024-12-18T01:09:51.5110213Z .. note: 2024-12-18T01:09:51.5110563Z If no process group is initialized, this function will assume the intent 2024-12-18T01:09:51.5111131Z is to load a checkpoint into the local process. This can be useful in the 2024-12-18T01:09:51.5111724Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-12-18T01:09:51.5112191Z or ShardedTensor) 2024-12-18T01:09:51.5112351Z 2024-12-18T01:09:51.5112451Z .. note: 2024-12-18T01:09:51.5112715Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:09:51.5112958Z 2024-12-18T01:09:51.5113042Z Args: 2024-12-18T01:09:51.5113318Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:09:51.5113728Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:09:51.5114204Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:09:51.5114735Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:09:51.5115216Z It can also be a key if the storage is a key-value store. 2024-12-18T01:09:51.5115747Z (Default: ``None``) 2024-12-18T01:09:51.5116062Z storage_reader (Optional[StorageReader]): 2024-12-18T01:09:51.5116520Z Instance of StorageWriter used to perform reads. If this is not 2024-12-18T01:09:51.5117051Z specified, DCP will automatically infer the reader based on the 2024-12-18T01:09:51.5117568Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:09:51.5118003Z be raised. (Default: ``None``) 2024-12-18T01:09:51.5118337Z planner (Optional[LoadPlanner]): 2024-12-18T01:09:51.5118764Z Instance of LoadPlanner. If this is not specificed, the default 2024-12-18T01:09:51.5119216Z planner will be used. (Default: ``None``) 2024-12-18T01:09:51.5119589Z process_group (Optional[ProcessGroup]): 2024-12-18T01:09:51.5120094Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:09:51.5120493Z (Default: ``None``) 2024-12-18T01:09:51.5120682Z 2024-12-18T01:09:51.5120776Z Returns: 2024-12-18T01:09:51.5120992Z None. 2024-12-18T01:09:51.5121111Z 2024-12-18T01:09:51.5121213Z Examples 2024-12-18T01:09:51.5121428Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.5121711Z >>> my_model = MyModule() 2024-12-18T01:09:51.5122037Z >>> optimizer = Adagrad(my_model.parameters()) 2024-12-18T01:09:51.5122417Z >>> model_state_dict = my_model.state_dict() 2024-12-18T01:09:51.5122958Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-12-18T01:09:51.5123371Z 2024-12-18T01:09:51.5123526Z >>> torch.distributed.checkpoint.load_state_dict( 2024-12-18T01:09:51.5123914Z >>> state_dict=model_state_dict, 2024-12-18T01:09:51.5124254Z >>> storage_reader=fs_storage_reader, 2024-12-18T01:09:51.5124571Z >>> ) 2024-12-18T01:09:51.5124696Z 2024-12-18T01:09:51.5124906Z >>> # module.load_state_dict() function might have customized steps 2024-12-18T01:09:51.5125481Z >>> # to flush the state_dict, must call it to 2024-12-18T01:09:51.5125973Z >>> # ensure correct behavior. 2024-12-18T01:09:51.5126389Z >>> my_model.load_state_dict(model_state_dict) 2024-12-18T01:09:51.5126881Z 2024-12-18T01:09:51.5127035Z .. note:: 2024-12-18T01:09:51.5127381Z load_state_dict uses collectives to coordinate reads across ranks. 2024-12-18T01:09:51.5127925Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:09:51.5128478Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:09:51.5129054Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:09:51.5129621Z and it is the user's responsibility to ensure that this is set so that each 2024-12-18T01:09:51.5130157Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:09:51.5130452Z 2024-12-18T01:09:51.5130713Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.5131077Z 2024-12-18T01:09:51.5131717Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-12-18T01:09:51.5132890Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.5133336Z 2024-12-18T01:09:51.5133519Z Save a distributed model in SPMD style. 2024-12-18T01:09:51.5133740Z 2024-12-18T01:09:51.5133944Z This function is different from ``torch.save()`` as it handles 2024-12-18T01:09:51.5134492Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-12-18T01:09:51.5134878Z 2024-12-18T01:09:51.5135132Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:09:51.5135667Z save will call ``state_dict`` before serialization. 2024-12-18T01:09:51.5135938Z 2024-12-18T01:09:51.5136195Z .. warning:: 2024-12-18T01:09:51.5136604Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-12-18T01:09:51.5137229Z for saved state_dicts. 2024-12-18T01:09:51.5137401Z 2024-12-18T01:09:51.5137494Z .. warning:: 2024-12-18T01:09:51.5137842Z If using the `process_group` argument, make sure that only its ranks 2024-12-18T01:09:51.5138377Z call `save_state_dict` and that all data in state_dict belong to it. 2024-12-18T01:09:51.5138697Z 2024-12-18T01:09:51.5138800Z .. note:: 2024-12-18T01:09:51.5139192Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-12-18T01:09:51.5139816Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-12-18T01:09:51.5140316Z group needs to be passed in. 2024-12-18T01:09:51.5140526Z 2024-12-18T01:09:51.5140614Z .. note:: 2024-12-18T01:09:51.5141079Z If no process group is available, this function assumes the intention is to save the 2024-12-18T01:09:51.5141582Z state_dict in the local process. 2024-12-18T01:09:51.5141792Z 2024-12-18T01:09:51.5141894Z .. note: 2024-12-18T01:09:51.5142148Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:09:51.5142408Z 2024-12-18T01:09:51.5142412Z 2024-12-18T01:09:51.5142497Z Args: 2024-12-18T01:09:51.5142775Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:09:51.5143205Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:09:51.5143678Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:09:51.5144204Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:09:51.5144701Z It can also be a key if the storage is a key-value store. 2024-12-18T01:09:51.5145088Z (Default: ``None``) 2024-12-18T01:09:51.5145401Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:09:51.5145864Z Instance of StorageWriter used to perform writes. If this is not 2024-12-18T01:09:51.5146404Z specified, DCP will automatically infer the writer based on the 2024-12-18T01:09:51.5146924Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:09:51.5147364Z be raised. (Default: ``None``) 2024-12-18T01:09:51.5147701Z planner (Optional[SavePlanner]): 2024-12-18T01:09:51.5148128Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:09:51.5148667Z planner will be used. (Default: ``None``) 2024-12-18T01:09:51.5149032Z process_group (Optional[ProcessGroup]): 2024-12-18T01:09:51.5149460Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:09:51.5149862Z (Default: ``None``) 2024-12-18T01:09:51.5150037Z 2024-12-18T01:09:51.5150141Z Returns: 2024-12-18T01:09:51.5150430Z Metadata: Metadata object for the saved checkpoint. 2024-12-18T01:09:51.5150693Z 2024-12-18T01:09:51.5150798Z Example: 2024-12-18T01:09:51.5151023Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.5151306Z >>> my_model = MyModule() 2024-12-18T01:09:51.5151498Z 2024-12-18T01:09:51.5151609Z >>> state_dict = {"model": my_model} 2024-12-18T01:09:51.5151827Z 2024-12-18T01:09:51.5152130Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:09:51.5152678Z >>> torch.distributed.checkpoint.save( 2024-12-18T01:09:51.5153009Z >>> state_dict=state_dict, 2024-12-18T01:09:51.5153329Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:09:51.5153645Z >>> ) 2024-12-18T01:09:51.5153762Z 2024-12-18T01:09:51.5153862Z .. note:: 2024-12-18T01:09:51.5154202Z save_state_dict uses collectives to coordinate writes across ranks. 2024-12-18T01:09:51.5154736Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:09:51.5155297Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:09:51.5155869Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:09:51.5156420Z and it is the user's responsibility to ensure that this is set so that 2024-12-18T01:09:51.5156949Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:09:51.5157332Z 2024-12-18T01:09:51.5157598Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.5157962Z 2024-12-18T01:09:51.5158619Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-12-18T01:09:51.5159616Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.5160249Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-12-18T01:09:51.5160924Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-12-18T01:09:51.5161339Z 2024-12-18T01:09:51.5161433Z .. warning:: 2024-12-18T01:09:51.5161799Z This feature is experimental and subject to change. 2024-12-18T01:09:51.5162069Z 2024-12-18T01:09:51.5162169Z Args: 2024-12-18T01:09:51.5162452Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:09:51.5162866Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:09:51.5163341Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:09:51.5163886Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:09:51.5164390Z It can also be a key if the storage is a key-value store. 2024-12-18T01:09:51.5164778Z (Default: ``None``) 2024-12-18T01:09:51.5165089Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:09:51.5165553Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-12-18T01:09:51.5166124Z this is not specified, DCP will automatically infer the writer based on the 2024-12-18T01:09:51.5166701Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:09:51.5167141Z be raised. (Default: ``None``) 2024-12-18T01:09:51.5167484Z planner (Optional[SavePlanner]): 2024-12-18T01:09:51.5167909Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:09:51.5168365Z planner will be used. (Default: ``None``) 2024-12-18T01:09:51.5168740Z process_group (Optional[ProcessGroup]): 2024-12-18T01:09:51.5169171Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:09:51.5169572Z (Default: ``None``) 2024-12-18T01:09:51.5169753Z 2024-12-18T01:09:51.5169844Z Returns: 2024-12-18T01:09:51.5170190Z Future: A future holding the resultant Metadata object from `save`. 2024-12-18T01:09:51.5170526Z 2024-12-18T01:09:51.5170621Z Example: 2024-12-18T01:09:51.5170856Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.5171144Z >>> my_model = MyModule() 2024-12-18T01:09:51.5171341Z 2024-12-18T01:09:51.5171453Z >>> state_dict = {"model": my_model} 2024-12-18T01:09:51.5171683Z 2024-12-18T01:09:51.5171981Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:09:51.5172613Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-12-18T01:09:51.5173047Z >>> state_dict=state_dict, 2024-12-18T01:09:51.5173381Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:09:51.5173704Z >>> ) 2024-12-18T01:09:51.5173912Z >>> 2024-12-18T01:09:51.5174138Z >>> # ... do some work ... 2024-12-18T01:09:51.5174422Z >>> 2024-12-18T01:09:51.5174662Z >>> checkpoint_future.result() 2024-12-18T01:09:51.5174874Z 2024-12-18T01:09:51.5174969Z 2024-12-18T01:09:51.5175326Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.5175702Z 2024-12-18T01:09:51.5225196Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-12-18T01:09:51.5226361Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.5226864Z 2024-12-18T01:09:51.5227072Z Initialize rendezvous event object and record its operations. 2024-12-18T01:09:51.5227399Z 2024-12-18T01:09:51.5227486Z Args: 2024-12-18T01:09:51.5227748Z run_id (str): The run id of the rendezvous. 2024-12-18T01:09:51.5228144Z message (str): The message describing the event. 2024-12-18T01:09:51.5228717Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-12-18T01:09:51.5229285Z name (str): Event name. (E.g. Current action being performed). 2024-12-18T01:09:51.5229706Z hostname (str): Hostname of the node. 2024-12-18T01:09:51.5230080Z pid (Optional[int]): The process id of the node. 2024-12-18T01:09:51.5230651Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-12-18T01:09:51.5231289Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-12-18T01:09:51.5231835Z rank (Optional[int]): The rank of the node, if known. 2024-12-18T01:09:51.5232205Z Returns: 2024-12-18T01:09:51.5232424Z None 2024-12-18T01:09:51.5232641Z Example: 2024-12-18T01:09:51.5232899Z >>> # See DynamicRendezvousHandler class 2024-12-18T01:09:51.5233224Z >>> def _record( 2024-12-18T01:09:51.5244569Z ... self, 2024-12-18T01:09:51.5244912Z ... message: str, 2024-12-18T01:09:51.5245247Z ... node_state: NodeState = NodeState.RUNNING, 2024-12-18T01:09:51.5245626Z ... rank: Optional[int] = None, 2024-12-18T01:09:51.5245929Z ... ) -> None: 2024-12-18T01:09:51.5246209Z ... construct_and_record_rdzv_event( 2024-12-18T01:09:51.5246621Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-12-18T01:09:51.5247048Z ... run_id=self._settings.run_id, 2024-12-18T01:09:51.5247392Z ... message=message, 2024-12-18T01:09:51.5247683Z ... node_state=node_state, 2024-12-18T01:09:51.5248021Z ... hostname=self._this_node.addr, 2024-12-18T01:09:51.5248376Z ... pid=self._this_node.pid, 2024-12-18T01:09:51.5248725Z ... local_id=self._this_node.local_id, 2024-12-18T01:09:51.5249063Z ... rank=rank, 2024-12-18T01:09:51.5249312Z ... ) 2024-12-18T01:09:51.5249457Z 2024-12-18T01:09:51.5249712Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.5250094Z 2024-12-18T01:09:51.6812549Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-12-18T01:09:51.6813531Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.6813926Z 2024-12-18T01:09:51.6814132Z This configures FSDP-native mixed precision training. 2024-12-18T01:09:51.6814426Z 2024-12-18T01:09:51.6814521Z Attributes: 2024-12-18T01:09:51.6814761Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-12-18T01:09:51.6814991Z parameters during forward and backward and thus the dtype for 2024-12-18T01:09:51.6815217Z forward and backward computation. Outside forward and backward, the 2024-12-18T01:09:51.6815428Z *sharded* parameters are kept in full precision (e.g. for the 2024-12-18T01:09:51.6815640Z optimizer step), and for model checkpointing, the parameters are 2024-12-18T01:09:51.6815817Z always saved in full precision. (Default: ``None``) 2024-12-18T01:09:51.6816035Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:09:51.6816254Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-12-18T01:09:51.6816445Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-12-18T01:09:51.6816649Z the ``param_dtype`` value, still running gradient reduction in low 2024-12-18T01:09:51.6816874Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-12-18T01:09:51.6817297Z to force gradient reduction to run in full precision. (Default: 2024-12-18T01:09:51.6817401Z ``None``) 2024-12-18T01:09:51.6817616Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:09:51.6817815Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-12-18T01:09:51.6818028Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-12-18T01:09:51.6818239Z dtype thereafter. For model checkpointing, the buffers are saved 2024-12-18T01:09:51.6818434Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-12-18T01:09:51.6818524Z ``None``) 2024-12-18T01:09:51.6818736Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-12-18T01:09:51.6819095Z gradients to full precision after the backward pass in preparation 2024-12-18T01:09:51.6819303Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-12-18T01:09:51.6819522Z in the dtype used for gradient reduction, which can save memory if 2024-12-18T01:09:51.6819728Z using a custom optimizer that supports running in low precision. 2024-12-18T01:09:51.6819842Z (Default: ``False``) 2024-12-18T01:09:51.6820054Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-12-18T01:09:51.6820268Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-12-18T01:09:51.6820481Z that parameter and input dtypes match for forward computation, as 2024-12-18T01:09:51.6820686Z required by many ops. This may need to be set to ``True`` when only 2024-12-18T01:09:51.6820921Z applying mixed precision to some but not all FSDP modules, in which 2024-12-18T01:09:51.6821134Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-12-18T01:09:51.6821250Z (Default: ``False``) 2024-12-18T01:09:51.6821469Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-12-18T01:09:51.6821685Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-12-18T01:09:51.6821872Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-12-18T01:09:51.6822016Z this does not do anything. (Default: ``True``) 2024-12-18T01:09:51.6822246Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-12-18T01:09:51.6822432Z module classes to ignore for mixed precision when using an 2024-12-18T01:09:51.6822627Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-12-18T01:09:51.6822836Z applied to them separately with mixed precision disabled (meaning 2024-12-18T01:09:51.6823052Z that the final FSDP construction would deviate from the specified 2024-12-18T01:09:51.6823248Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-12-18T01:09:51.6823448Z not do anything. This API is experimental and subject to change. 2024-12-18T01:09:51.6823570Z (Default: ``(_BatchNorm,)``) 2024-12-18T01:09:51.6823579Z 2024-12-18T01:09:51.6823764Z .. note:: This API is experimental and subject to change. 2024-12-18T01:09:51.6823769Z 2024-12-18T01:09:51.6824001Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-12-18T01:09:51.6824006Z 2024-12-18T01:09:51.6824189Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-12-18T01:09:51.6824318Z precision, but buffers are not. 2024-12-18T01:09:51.6824323Z 2024-12-18T01:09:51.6824527Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-12-18T01:09:51.6824745Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-12-18T01:09:51.6824975Z Disabling FSDP's mixed precision for those norm modules only means that 2024-12-18T01:09:51.6825193Z the affine parameters are kept in ``float32``. However, this incurs 2024-12-18T01:09:51.6825438Z separate all-gathers and reduce-scatters for those norm modules, which 2024-12-18T01:09:51.6825715Z may be inefficient, so if the workload permits, the user should prefer 2024-12-18T01:09:51.6825877Z to still apply mixed precision to those modules. 2024-12-18T01:09:51.6825882Z 2024-12-18T01:09:51.6826086Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-12-18T01:09:51.6826305Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-12-18T01:09:51.6826530Z modules will have FSDP applied to them separately with mixed precision 2024-12-18T01:09:51.6826720Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-12-18T01:09:51.6826725Z 2024-12-18T01:09:51.6826934Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-12-18T01:09:51.6827204Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-12-18T01:09:51.6827394Z its ``cast_root_forward_inputs`` takes precedence over its 2024-12-18T01:09:51.6827573Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-12-18T01:09:51.6827805Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-12-18T01:09:51.6828028Z sufficient for the typical case where each FSDP instance has the same 2024-12-18T01:09:51.6828377Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-12-18T01:09:51.6828565Z ``param_dtype`` at the beginning of the model's forward pass. 2024-12-18T01:09:51.6828569Z 2024-12-18T01:09:51.6828777Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-12-18T01:09:51.6829026Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-12-18T01:09:51.6829233Z values to configure casting inputs or not before each instance's 2024-12-18T01:09:51.6829448Z forward. In such a case, since the casts happen before each FSDP 2024-12-18T01:09:51.6829668Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-12-18T01:09:51.6829917Z submodules run before its FSDP submodules to avoid the activation dtype 2024-12-18T01:09:51.6830128Z being changed due to a different ``MixedPrecision`` configuration. 2024-12-18T01:09:51.6830132Z 2024-12-18T01:09:51.6830242Z Example:: 2024-12-18T01:09:51.6830246Z 2024-12-18T01:09:51.6830383Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.6830554Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-12-18T01:09:51.6830669Z >>> model[1] = FSDP( 2024-12-18T01:09:51.6830765Z >>> model[1], 2024-12-18T01:09:51.6831084Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-12-18T01:09:51.6831175Z >>> ) 2024-12-18T01:09:51.6831284Z >>> model = FSDP( 2024-12-18T01:09:51.6831375Z >>> model, 2024-12-18T01:09:51.6831685Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-12-18T01:09:51.6831788Z >>> ) 2024-12-18T01:09:51.6831792Z 2024-12-18T01:09:51.6832008Z The above shows a working example. On the other hand, if ``model[1]`` 2024-12-18T01:09:51.6832227Z were replaced with ``model[0]``, meaning that the submodule using 2024-12-18T01:09:51.6832451Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-12-18T01:09:51.6832685Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-12-18T01:09:51.6832776Z ones. 2024-12-18T01:09:51.6832781Z 2024-12-18T01:09:51.6832785Z 2024-12-18T01:09:51.6833039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.6833057Z 2024-12-18T01:09:51.6955508Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=649. 2024-12-18T01:09:51.6955818Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.6956067Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:09:51.6956237Z 2024-12-18T01:09:51.6956521Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-12-18T01:09:51.6956728Z The target module does not have to be a FSDP module. If the target 2024-12-18T01:09:51.6956938Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-12-18T01:09:51.6956943Z 2024-12-18T01:09:51.6957156Z .. note:: This API should be called for only the top-level (root) 2024-12-18T01:09:51.6957249Z module. 2024-12-18T01:09:51.6957253Z 2024-12-18T01:09:51.6957477Z .. note:: This API enables users to transparently use the conventional 2024-12-18T01:09:51.6957666Z ``state_dict`` API to take model checkpoints in cases where the 2024-12-18T01:09:51.6957963Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-12-18T01:09:51.6958174Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-12-18T01:09:51.6958423Z instances, while dispatching into `sharded_state_dict` implementation 2024-12-18T01:09:51.6958517Z for FSDP: 2024-12-18T01:09:51.6958521Z 2024-12-18T01:09:51.6958623Z Example:: 2024-12-18T01:09:51.6958630Z 2024-12-18T01:09:51.6958831Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.6958942Z >>> model = DDP(FSDP(...)) 2024-12-18T01:09:51.6959067Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:51.6959160Z >>> model, 2024-12-18T01:09:51.6959343Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:09:51.6959562Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-12-18T01:09:51.6959845Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-12-18T01:09:51.6959991Z >>> ) 2024-12-18T01:09:51.6960167Z >>> param_state_dict = model.state_dict() 2024-12-18T01:09:51.6960361Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:09:51.6960366Z 2024-12-18T01:09:51.6960455Z Args: 2024-12-18T01:09:51.6960594Z module (torch.nn.Module): Root module. 2024-12-18T01:09:51.6960828Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:09:51.6961065Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-12-18T01:09:51.6961193Z target ``state_dict_type``. 2024-12-18T01:09:51.6961444Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-12-18T01:09:51.6961575Z for the optimizer state dict. 2024-12-18T01:09:51.6961579Z 2024-12-18T01:09:51.6961671Z Returns: 2024-12-18T01:09:51.6961912Z A StateDictSettings that include the previous state_dict type and 2024-12-18T01:09:51.6962029Z configuration for the module. 2024-12-18T01:09:51.6962123Z 2024-12-18T01:09:51.6962388Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.6962393Z 2024-12-18T01:09:51.6963207Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=805. 2024-12-18T01:09:51.6963487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.6963733Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:09:51.6963737Z 2024-12-18T01:09:51.6964051Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-12-18T01:09:51.6964198Z :meth:`set_state_dict_type` for the detail. 2024-12-18T01:09:51.6964203Z 2024-12-18T01:09:51.6964296Z Example:: 2024-12-18T01:09:51.6964300Z 2024-12-18T01:09:51.6964445Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.6964631Z >>> model = DDP(FSDP(...)) 2024-12-18T01:09:51.6964764Z >>> with FSDP.state_dict_type( 2024-12-18T01:09:51.6964857Z >>> model, 2024-12-18T01:09:51.6964990Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:09:51.6965091Z >>> ): 2024-12-18T01:09:51.6965214Z >>> checkpoint = model.state_dict() 2024-12-18T01:09:51.6965218Z 2024-12-18T01:09:51.6965319Z Args: 2024-12-18T01:09:51.6965442Z module (torch.nn.Module): Root module. 2024-12-18T01:09:51.6965691Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:09:51.6965921Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-12-18T01:09:51.6966137Z configuration for the target ``state_dict_type``. 2024-12-18T01:09:51.6966387Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-12-18T01:09:51.6966589Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-12-18T01:09:51.6966690Z 2024-12-18T01:09:51.6966942Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.6966946Z 2024-12-18T01:09:51.7015881Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1818. 2024-12-18T01:09:51.7016149Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7016155Z 2024-12-18T01:09:51.7016409Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-12-18T01:09:51.7016413Z 2024-12-18T01:09:51.7016617Z The given state-dict can be transformed to one of three types: 2024-12-18T01:09:51.7016930Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-12-18T01:09:51.7016934Z 2024-12-18T01:09:51.7017173Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-12-18T01:09:51.7017400Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-12-18T01:09:51.7017494Z avoid OOM. 2024-12-18T01:09:51.7017498Z 2024-12-18T01:09:51.7017731Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-12-18T01:09:51.7017947Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-12-18T01:09:51.7018036Z memory. 2024-12-18T01:09:51.7018041Z 2024-12-18T01:09:51.7018270Z For local state_dict, no transformation will be performed. But a state 2024-12-18T01:09:51.7018506Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-12-18T01:09:51.7018631Z nature (this is not supported yet). 2024-12-18T01:09:51.7018636Z 2024-12-18T01:09:51.7018738Z Example:: 2024-12-18T01:09:51.7018743Z 2024-12-18T01:09:51.7018870Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.7019135Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:09:51.7019298Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:09:51.7019550Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:09:51.7019849Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:09:51.7020012Z >>> # Save a checkpoint 2024-12-18T01:09:51.7020113Z >>> model, optim = ... 2024-12-18T01:09:51.7020235Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:51.7020336Z >>> model, 2024-12-18T01:09:51.7020531Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:51.7020786Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7021056Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7021185Z >>> ) 2024-12-18T01:09:51.7021301Z >>> state_dict = model.state_dict() 2024-12-18T01:09:51.7021469Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:09:51.7021623Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:09:51.7021847Z >>> # Load a checkpoint 2024-12-18T01:09:51.7021961Z >>> model, optim = ... 2024-12-18T01:09:51.7022113Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:09:51.7022236Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:51.7022325Z >>> model, 2024-12-18T01:09:51.7022443Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:51.7022586Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7022730Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7022830Z >>> ) 2024-12-18T01:09:51.7022947Z >>> model.load_state_dict(state_dict) 2024-12-18T01:09:51.7023096Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:09:51.7023278Z >>> model, optim, optim_state_dict 2024-12-18T01:09:51.7023366Z >>> ) 2024-12-18T01:09:51.7023503Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:09:51.7023508Z 2024-12-18T01:09:51.7023599Z Args: 2024-12-18T01:09:51.7023809Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:09:51.7024011Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:09:51.7024145Z were passed into the optimizer ``optim``. 2024-12-18T01:09:51.7024340Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:09:51.7024433Z parameters. 2024-12-18T01:09:51.7024658Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-12-18T01:09:51.7024866Z transform. If the value is None, optim.state_dict() will be used. ( 2024-12-18T01:09:51.7024977Z Default: ``None``) 2024-12-18T01:09:51.7025215Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:09:51.7025405Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:09:51.7025514Z Default: ``None``) 2024-12-18T01:09:51.7025519Z 2024-12-18T01:09:51.7025611Z Returns: 2024-12-18T01:09:51.7025814Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-12-18T01:09:51.7025980Z ``model``. The sharding of the optimizer state is based on 2024-12-18T01:09:51.7026091Z ``state_dict_type``. 2024-12-18T01:09:51.7026095Z 2024-12-18T01:09:51.7026349Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7026353Z 2024-12-18T01:09:51.7027214Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1916. 2024-12-18T01:09:51.7027474Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7027478Z 2024-12-18T01:09:51.7027846Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-12-18T01:09:51.7027851Z 2024-12-18T01:09:51.7028018Z Given a ``optim_state_dict`` that is transformed through 2024-12-18T01:09:51.7028249Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-12-18T01:09:51.7028552Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-12-18T01:09:51.7028761Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-12-18T01:09:51.7028765Z 2024-12-18T01:09:51.7028890Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.7029127Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:09:51.7029298Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:09:51.7029477Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:09:51.7029692Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:09:51.7029799Z >>> # Save a checkpoint 2024-12-18T01:09:51.7029914Z >>> model, optim = ... 2024-12-18T01:09:51.7030022Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:51.7030115Z >>> model, 2024-12-18T01:09:51.7030313Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:51.7030443Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7030603Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7030691Z >>> ) 2024-12-18T01:09:51.7030805Z >>> state_dict = model.state_dict() 2024-12-18T01:09:51.7030936Z >>> original_osd = optim.state_dict() 2024-12-18T01:09:51.7031067Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-12-18T01:09:51.7031172Z >>> model, 2024-12-18T01:09:51.7031260Z >>> optim, 2024-12-18T01:09:51.7031373Z >>> optim_state_dict=original_osd 2024-12-18T01:09:51.7031475Z >>> ) 2024-12-18T01:09:51.7031620Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:09:51.7031781Z >>> # Load a checkpoint 2024-12-18T01:09:51.7031882Z >>> model, optim = ... 2024-12-18T01:09:51.7032045Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:09:51.7032156Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:51.7032249Z >>> model, 2024-12-18T01:09:51.7032379Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:51.7032512Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7032670Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:51.7032757Z >>> ) 2024-12-18T01:09:51.7032872Z >>> model.load_state_dict(state_dict) 2024-12-18T01:09:51.7033034Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:09:51.7033149Z >>> model, optim, optim_state_dict 2024-12-18T01:09:51.7033251Z >>> ) 2024-12-18T01:09:51.7033378Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:09:51.7033383Z 2024-12-18T01:09:51.7033481Z Args: 2024-12-18T01:09:51.7033680Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:09:51.7033880Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:09:51.7034028Z were passed into the optimizer ``optim``. 2024-12-18T01:09:51.7034213Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:09:51.7034318Z parameters. 2024-12-18T01:09:51.7034532Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-12-18T01:09:51.7034743Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-12-18T01:09:51.7034933Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-12-18T01:09:51.7035110Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-12-18T01:09:51.7035317Z load_directly (bool): If this is set to True, this API will also 2024-12-18T01:09:51.7035516Z call optim.load_state_dict(result) before returning the result. 2024-12-18T01:09:51.7035752Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-12-18T01:09:51.7035851Z (Default: ``False``) 2024-12-18T01:09:51.7036306Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:09:51.7036501Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:09:51.7036601Z Default: ``None``) 2024-12-18T01:09:51.7036605Z 2024-12-18T01:09:51.7036874Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7036879Z 2024-12-18T01:09:51.7466850Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-12-18T01:09:51.7467911Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7468384Z 2024-12-18T01:09:51.7468613Z RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:09:51.7468965Z 2024-12-18T01:09:51.7469177Z It creates a user-specified module on a specified remote node. 2024-12-18T01:09:51.7469726Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:09:51.7470192Z executed on the remote node. 2024-12-18T01:09:51.7470810Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:09:51.7471352Z gradients back to the corresponding remote module. 2024-12-18T01:09:51.7471981Z It can be shared across processors using `RPC framework `__, 2024-12-18T01:09:51.7472646Z without incurring any overheads of copying the actual module, 2024-12-18T01:09:51.7473148Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-12-18T01:09:51.7473571Z pointing to the remote module. 2024-12-18T01:09:51.7473776Z 2024-12-18T01:09:51.7473976Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-12-18T01:09:51.7474491Z the ``forward`` method of the module returned by the ``module_cls``. 2024-12-18T01:09:51.7474802Z 2024-12-18T01:09:51.7475292Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-12-18T01:09:51.7475853Z 2024-12-18T01:09:51.7476118Z Particularly, to create a hybrid model, typically the local modules should be 2024-12-18T01:09:51.7477102Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-12-18T01:09:51.7477907Z Hybrid Example: 2024-12-18T01:09:51.7478169Z >>> class HybridModel(nn.Module): 2024-12-18T01:09:51.7478515Z >>> def __init__(self) -> None: 2024-12-18T01:09:51.7478853Z >>> nn.Module.__init__(self) 2024-12-18T01:09:51.7479208Z >>> self.remote_embedding = RemoteModule(...) 2024-12-18T01:09:51.7479600Z >>> self.local_linear = nn.Linear(...) 2024-12-18T01:09:51.7479849Z 2024-12-18T01:09:51.7480046Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:09:51.7480616Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:09:51.7481195Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-12-18T01:09:51.7481651Z ``def forward(input: Tensor) -> Tensor:`` and 2024-12-18T01:09:51.7482054Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-12-18T01:09:51.7482339Z 2024-12-18T01:09:51.7482441Z .. note:: 2024-12-18T01:09:51.7482715Z If the remote module is placed on a cuda device, 2024-12-18T01:09:51.7483207Z any input CPU tensors will be automatically moved to the same cuda device, 2024-12-18T01:09:51.7483956Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-12-18T01:09:51.7484497Z 2024-12-18T01:09:51.7484641Z Args: 2024-12-18T01:09:51.7485249Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:09:51.7486334Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:09:51.7487042Z formats: 2024-12-18T01:09:51.7487197Z 2024-12-18T01:09:51.7487342Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:09:51.7487767Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:09:51.7488029Z 2024-12-18T01:09:51.7488283Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:09:51.7488757Z module_cls (nn.Module): For example, 2024-12-18T01:09:51.7489093Z >>> class MyModule(nn.Module): 2024-12-18T01:09:51.7489413Z >>> def forward(input): 2024-12-18T01:09:51.7489719Z >>> return input + 1 2024-12-18T01:09:51.7490009Z >>> 2024-12-18T01:09:51.7490227Z >>> module_cls = MyModule 2024-12-18T01:09:51.7490622Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:09:51.7491126Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:09:51.7491715Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:09:51.7492348Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:09:51.7493037Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:09:51.7493600Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:09:51.7493964Z 2024-12-18T01:09:51.7494052Z Returns: 2024-12-18T01:09:51.7494426Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:09:51.7495024Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:09:51.7495640Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:09:51.7496162Z on the user-provided module on the remote side. 2024-12-18T01:09:51.7496424Z 2024-12-18T01:09:51.7496520Z Example:: 2024-12-18T01:09:51.7496869Z Run the following code in two different processes: 2024-12-18T01:09:51.7497142Z 2024-12-18T01:09:51.7497257Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:51.7497575Z >>> # On worker 0: 2024-12-18T01:09:51.7497835Z >>> import torch 2024-12-18T01:09:51.7498111Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7498455Z >>> from torch import nn, Tensor 2024-12-18T01:09:51.7498884Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:09:51.7499306Z >>> 2024-12-18T01:09:51.7499575Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:09:51.7499938Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:09:51.7500302Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:09:51.7500631Z >>> ) 2024-12-18T01:09:51.7500863Z >>> input = torch.randn(128, 20) 2024-12-18T01:09:51.7501229Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:09:51.7501588Z >>> ret = ret_fut.wait() 2024-12-18T01:09:51.7501872Z >>> rpc.shutdown() 2024-12-18T01:09:51.7502040Z 2024-12-18T01:09:51.7502131Z >>> # On worker 1: 2024-12-18T01:09:51.7502380Z >>> import torch 2024-12-18T01:09:51.7502661Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7502977Z >>> 2024-12-18T01:09:51.7503239Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:09:51.7503588Z >>> rpc.shutdown() 2024-12-18T01:09:51.7503743Z 2024-12-18T01:09:51.7504007Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7504369Z 2024-12-18T01:09:51.7505111Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-12-18T01:09:51.7506171Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7506562Z 2024-12-18T01:09:51.7506888Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-12-18T01:09:51.7507326Z 2024-12-18T01:09:51.7507661Z This alternate initialization method can be particularly useful if we want to create multiple 2024-12-18T01:09:51.7508515Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-12-18T01:09:51.7508948Z 2024-12-18T01:09:51.7509239Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-12-18T01:09:51.7509803Z which is not supported. The recommended way is as follows: 2024-12-18T01:09:51.7510106Z 2024-12-18T01:09:51.7510226Z 1. the sender creates a RemoteModule; 2024-12-18T01:09:51.7510607Z 2. the sender sends its ``module_rref`` over RPC; 2024-12-18T01:09:51.7511207Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-12-18T01:09:51.7511656Z 2024-12-18T01:09:51.7511765Z Example:: 2024-12-18T01:09:51.7512055Z Run the following code in two different processes: 2024-12-18T01:09:51.7512316Z 2024-12-18T01:09:51.7512437Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:51.7512760Z >>> # On worker 0: 2024-12-18T01:09:51.7513021Z >>> import torch 2024-12-18T01:09:51.7513453Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7513802Z >>> from torch import nn, Tensor 2024-12-18T01:09:51.7514224Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:09:51.7514653Z >>> 2024-12-18T01:09:51.7514922Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:09:51.7515288Z >>> remote_module = RemoteModule( 2024-12-18T01:09:51.7515636Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:09:51.7515950Z >>> ) 2024-12-18T01:09:51.7516160Z >>> 2024-12-18T01:09:51.7516389Z >>> remote_module1 = rpc.rpc_sync( 2024-12-18T01:09:51.7516703Z >>> "worker1/cpu", 2024-12-18T01:09:51.7517006Z >>> RemoteModule.init_from_module_rref, 2024-12-18T01:09:51.7517443Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-12-18T01:09:51.7517801Z >>> ) 2024-12-18T01:09:51.7518026Z >>> rpc.shutdown() 2024-12-18T01:09:51.7518183Z 2024-12-18T01:09:51.7518289Z >>> # On worker 1: 2024-12-18T01:09:51.7518545Z >>> import torch 2024-12-18T01:09:51.7518817Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7519140Z >>> 2024-12-18T01:09:51.7519404Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:09:51.7519757Z >>> rpc.shutdown() 2024-12-18T01:09:51.7519912Z 2024-12-18T01:09:51.7520009Z Args: 2024-12-18T01:09:51.7520412Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:09:51.7521116Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:09:51.7521614Z formats: 2024-12-18T01:09:51.7521755Z 2024-12-18T01:09:51.7521910Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:09:51.7522325Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:09:51.7522585Z 2024-12-18T01:09:51.7522841Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:09:51.7523450Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-12-18T01:09:51.7523932Z the created remote module. 2024-12-18T01:09:51.7524419Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:09:51.7525050Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:09:51.7525628Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:09:51.7526205Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:09:51.7526550Z 2024-12-18T01:09:51.7526638Z Returns: 2024-12-18T01:09:51.7527007Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:09:51.7527604Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-12-18T01:09:51.7528224Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:09:51.7528764Z on the user-provided module on the remote side. 2024-12-18T01:09:51.7529015Z 2024-12-18T01:09:51.7529276Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7529639Z 2024-12-18T01:09:51.7530239Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-12-18T01:09:51.7531213Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7531596Z 2024-12-18T01:09:51.7531825Z A RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:09:51.7532179Z 2024-12-18T01:09:51.7532374Z It creates a user-specified module on a specified remote node. 2024-12-18T01:09:51.7532920Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:09:51.7533376Z executed on the remote node. 2024-12-18T01:09:51.7533813Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:09:51.7534374Z gradients back to the corresponding remote module. 2024-12-18T01:09:51.7534651Z 2024-12-18T01:09:51.7534870Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-12-18T01:09:51.7535428Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-12-18T01:09:51.7536014Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-12-18T01:09:51.7536798Z and ``forward`` are the same as the ``forward`` method of the module 2024-12-18T01:09:51.7537212Z returned by the ``module_cls``. 2024-12-18T01:09:51.7537427Z 2024-12-18T01:09:51.7537626Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:09:51.7538338Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:09:51.7538945Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-12-18T01:09:51.7539289Z 2024-12-18T01:09:51.7539428Z | ``def forward(input: Tensor) -> Tensor:`` 2024-12-18T01:09:51.7539831Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-12-18T01:09:51.7540105Z 2024-12-18T01:09:51.7540204Z Args: 2024-12-18T01:09:51.7540607Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:09:51.7541359Z The format should be "/", where the device field can be parsed as torch.device type. 2024-12-18T01:09:51.7541961Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-12-18T01:09:51.7542465Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:09:51.7543083Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-12-18T01:09:51.7543452Z 2024-12-18T01:09:51.7543577Z >>> class MyModule(nn.Module): 2024-12-18T01:09:51.7543887Z >>> def forward(input): 2024-12-18T01:09:51.7544199Z >>> return input + 1 2024-12-18T01:09:51.7544489Z >>> 2024-12-18T01:09:51.7544723Z >>> module_cls = MyModule 2024-12-18T01:09:51.7544914Z 2024-12-18T01:09:51.7545122Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:09:51.7545618Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:09:51.7545935Z 2024-12-18T01:09:51.7546023Z Returns: 2024-12-18T01:09:51.7546393Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:09:51.7546986Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:09:51.7547607Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:09:51.7548134Z on the user-provided module on the remote side. 2024-12-18T01:09:51.7548476Z 2024-12-18T01:09:51.7548596Z Example:: 2024-12-18T01:09:51.7548884Z Run the following code in two different processes: 2024-12-18T01:09:51.7549150Z 2024-12-18T01:09:51.7549281Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:51.7549592Z >>> # On worker 0: 2024-12-18T01:09:51.7549853Z >>> import torch 2024-12-18T01:09:51.7550145Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7550495Z >>> from torch import nn, Tensor 2024-12-18T01:09:51.7550932Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:09:51.7551346Z >>> 2024-12-18T01:09:51.7551614Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:09:51.7551996Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:09:51.7552366Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:09:51.7552702Z >>> ) 2024-12-18T01:09:51.7552928Z >>> input = torch.randn(128, 20) 2024-12-18T01:09:51.7553301Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:09:51.7553673Z >>> ret = ret_fut.wait() 2024-12-18T01:09:51.7553961Z >>> rpc.shutdown() 2024-12-18T01:09:51.7554206Z 2024-12-18T01:09:51.7554314Z >>> # On worker 1: 2024-12-18T01:09:51.7554553Z >>> import torch 2024-12-18T01:09:51.7554838Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7555169Z >>> 2024-12-18T01:09:51.7555432Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:09:51.7555783Z >>> rpc.shutdown() 2024-12-18T01:09:51.7555938Z 2024-12-18T01:09:51.7556135Z Furthermore, a more practical example that is combined with 2024-12-18T01:09:51.7556927Z `DistributedDataParallel `__ (DDP) 2024-12-18T01:09:51.7557836Z can be found in this `tutorial `__. 2024-12-18T01:09:51.7558279Z 2024-12-18T01:09:51.7558596Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7558959Z 2024-12-18T01:09:51.7643076Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-12-18T01:09:51.7644109Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7644482Z 2024-12-18T01:09:51.7644733Z DistributedOptimizer takes remote references to parameters scattered 2024-12-18T01:09:51.7645326Z across workers and applies the given optimizer locally for each parameter. 2024-12-18T01:09:51.7645678Z 2024-12-18T01:09:51.7645911Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-12-18T01:09:51.7646426Z to retrieve the gradients for specific parameters. 2024-12-18T01:09:51.7646696Z 2024-12-18T01:09:51.7646795Z Concurrent calls to 2024-12-18T01:09:51.7647158Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-12-18T01:09:51.7647627Z either from the same or different clients, will 2024-12-18T01:09:51.7648106Z be serialized on each worker -- as each worker's optimizer can only work 2024-12-18T01:09:51.7648646Z on one set of gradients at a time. However, there is no guarantee that 2024-12-18T01:09:51.7649218Z the full forward-backward-optimizer sequence will execute for one client 2024-12-18T01:09:51.7649798Z at a time. This means that the gradients being applied may not correspond 2024-12-18T01:09:51.7650354Z to the latest forward pass executed on a given worker. Also, there is no 2024-12-18T01:09:51.7650811Z guaranteed ordering across workers. 2024-12-18T01:09:51.7651023Z 2024-12-18T01:09:51.7651282Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-12-18T01:09:51.7651894Z by default, so that optimizer updates are not blocked by the Python Global 2024-12-18T01:09:51.7652484Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-12-18T01:09:51.7653088Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-12-18T01:09:51.7653705Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-12-18T01:09:51.7654195Z for your own custom optimizers. 2024-12-18T01:09:51.7654387Z 2024-12-18T01:09:51.7654475Z Args: 2024-12-18T01:09:51.7654804Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-12-18T01:09:51.7655404Z instantiate on each worker. 2024-12-18T01:09:51.7655876Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-12-18T01:09:51.7656406Z to optimize. 2024-12-18T01:09:51.7657080Z args: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:09:51.7657638Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:09:51.7657988Z 2024-12-18T01:09:51.7658085Z Example:: 2024-12-18T01:09:51.7658328Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:51.7658721Z >>> import torch.distributed.autograd as dist_autograd 2024-12-18T01:09:51.7659123Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:51.7659452Z >>> from torch import optim 2024-12-18T01:09:51.7659838Z >>> from torch.distributed.optim import DistributedOptimizer 2024-12-18T01:09:51.7660371Z >>> 2024-12-18T01:09:51.7660630Z >>> with dist_autograd.context() as context_id: 2024-12-18T01:09:51.7660986Z >>> # Forward pass. 2024-12-18T01:09:51.7661350Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-12-18T01:09:51.7661871Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-12-18T01:09:51.7662317Z >>> loss = rref1.to_here() + rref2.to_here() 2024-12-18T01:09:51.7662645Z >>> 2024-12-18T01:09:51.7662862Z >>> # Backward pass. 2024-12-18T01:09:51.7663175Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-12-18T01:09:51.7663525Z >>> 2024-12-18T01:09:51.7663743Z >>> # Optimizer. 2024-12-18T01:09:51.7664101Z >>> dist_optim = DistributedOptimizer( 2024-12-18T01:09:51.7664433Z >>> optim.SGD, 2024-12-18T01:09:51.7664679Z >>> [rref1, rref2], 2024-12-18T01:09:51.7664950Z >>> lr=0.05, 2024-12-18T01:09:51.7665200Z >>> ) 2024-12-18T01:09:51.7665441Z >>> dist_optim.step(context_id) 2024-12-18T01:09:51.7665651Z 2024-12-18T01:09:51.7665824Z __ https://github.com/pytorch/tutorials/pull/1465 2024-12-18T01:09:51.7666087Z 2024-12-18T01:09:51.7666339Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7666720Z 2024-12-18T01:09:51.7667433Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-12-18T01:09:51.7668585Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7668976Z 2024-12-18T01:09:51.7669364Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-12-18T01:09:51.7670024Z This optimizer runs local optimizer at every step. 2024-12-18T01:09:51.7670621Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-12-18T01:09:51.7671070Z 2024-12-18T01:09:51.7671167Z Args: 2024-12-18T01:09:51.7671397Z optim: The local optimizer. 2024-12-18T01:09:51.7671802Z averager: A model averager instance to run post-localSGD algorithm. 2024-12-18T01:09:51.7672144Z 2024-12-18T01:09:51.7672238Z Example:: 2024-12-18T01:09:51.7672371Z 2024-12-18T01:09:51.7672498Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:51.7672833Z >>> import torch 2024-12-18T01:09:51.7673113Z >>> import torch.distributed as dist 2024-12-18T01:09:51.7673600Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:09:51.7674103Z >>> import torch.nn as nn 2024-12-18T01:09:51.7674498Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-12-18T01:09:51.7675086Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:09:51.7675589Z >>> PostLocalSGDState, 2024-12-18T01:09:51.7675889Z >>> post_localSGD_hook, 2024-12-18T01:09:51.7676161Z >>> ) 2024-12-18T01:09:51.7676379Z >>> 2024-12-18T01:09:51.7676662Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:09:51.7677079Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:09:51.7677415Z >>> ) 2024-12-18T01:09:51.7677634Z >>> 2024-12-18T01:09:51.7677908Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:09:51.7678471Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:09:51.7679061Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:51.7679421Z >>> 2024-12-18T01:09:51.7679753Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-12-18T01:09:51.7680334Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-12-18T01:09:51.7680874Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:09:51.7681444Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-12-18T01:09:51.7681893Z >>> opt = PostLocalSGDOptimizer( 2024-12-18T01:09:51.7682201Z >>> optim=local_optim, 2024-12-18T01:09:51.7682640Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:09:51.7683093Z >>> ) 2024-12-18T01:09:51.7683304Z >>> 2024-12-18T01:09:51.7683648Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-12-18T01:09:51.7684281Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-12-18T01:09:51.7685070Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-12-18T01:09:51.7685726Z >>> for step in range(0, 200): 2024-12-18T01:09:51.7686033Z >>> opt.zero_grad() 2024-12-18T01:09:51.7686328Z >>> loss = loss_fn(output, labels) 2024-12-18T01:09:51.7686641Z >>> loss.backward() 2024-12-18T01:09:51.7686915Z >>> opt.step() 2024-12-18T01:09:51.7687081Z 2024-12-18T01:09:51.7687336Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7687715Z 2024-12-18T01:09:51.7763681Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-12-18T01:09:51.7764824Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7765214Z 2024-12-18T01:09:51.7765612Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-12-18T01:09:51.7766139Z 2024-12-18T01:09:51.7766266Z The sharing is done as described by ZeRO_. 2024-12-18T01:09:51.7766507Z 2024-12-18T01:09:51.7766666Z The local optimizer instance in each rank is only 2024-12-18T01:09:51.7767166Z responsible for updating approximately ``1 / world_size`` parameters and 2024-12-18T01:09:51.7767716Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-12-18T01:09:51.7768279Z parameters are updated locally, each rank will broadcast its parameters to 2024-12-18T01:09:51.7768827Z all other peers to keep all model replicas in the same state. 2024-12-18T01:09:51.7769325Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-12-18T01:09:51.7769899Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-12-18T01:09:51.7770379Z memory consumption. 2024-12-18T01:09:51.7770528Z 2024-12-18T01:09:51.7770785Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-12-18T01:09:51.7771392Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-12-18T01:09:51.7771982Z not divided among ranks. The partition is arbitrary and might not match the 2024-12-18T01:09:51.7772469Z the parameter registration or usage order. 2024-12-18T01:09:51.7772701Z 2024-12-18T01:09:51.7772808Z Arguments: 2024-12-18T01:09:51.7773130Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-12-18T01:09:51.7773618Z or :class:`dict` s giving all parameters, which will be sharded 2024-12-18T01:09:51.7774023Z across ranks. 2024-12-18T01:09:51.7774187Z 2024-12-18T01:09:51.7774277Z Keyword Args: 2024-12-18T01:09:51.7774639Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-12-18T01:09:51.7775075Z optimizer. 2024-12-18T01:09:51.7775423Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-12-18T01:09:51.7775951Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-12-18T01:09:51.7776417Z :meth:`torch.distributed.init_process_group`). 2024-12-18T01:09:51.7776916Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-12-18T01:09:51.7777476Z packed into buckets to speed up communication, and ``param.data`` 2024-12-18T01:09:51.7778009Z fields point to bucket views at different offsets; if ``False``, 2024-12-18T01:09:51.7778702Z each individual parameter is communicated separately, and each 2024-12-18T01:09:51.7779182Z ``params.data`` stays intact (default: ``False``). 2024-12-18T01:09:51.7779650Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-12-18T01:09:51.7780163Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-12-18T01:09:51.7780693Z synchronization; this requires (1) either a functional optimizer 2024-12-18T01:09:51.7781196Z for the ``optimizer_class`` argument or one with a functional 2024-12-18T01:09:51.7781673Z equivalent and (2) registering a DDP communication hook 2024-12-18T01:09:51.7782166Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-12-18T01:09:51.7782726Z parameters are packed into buckets matching those in 2024-12-18T01:09:51.7783169Z :class:`DistributedDataParallel`, meaning that the 2024-12-18T01:09:51.7783602Z ``parameters_as_bucket_view`` argument is ignored. 2024-12-18T01:09:51.7784045Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-12-18T01:09:51.7784456Z (per normal). 2024-12-18T01:09:51.7784721Z (default: ``False``) 2024-12-18T01:09:51.7785121Z **defaults: any trailing arguments, which are forwarded to the local 2024-12-18T01:09:51.7785541Z optimizer. 2024-12-18T01:09:51.7785685Z 2024-12-18T01:09:51.7785789Z Example:: 2024-12-18T01:09:51.7785923Z 2024-12-18T01:09:51.7786022Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.7786298Z >>> import torch.nn as nn 2024-12-18T01:09:51.7786692Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-12-18T01:09:51.7787218Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-12-18T01:09:51.7787750Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-12-18T01:09:51.7788219Z >>> ddp = DDP(model, device_ids=[rank]) 2024-12-18T01:09:51.7788683Z >>> opt = ZeroRedundancyOptimizer( 2024-12-18T01:09:51.7789028Z >>> ddp.parameters(), 2024-12-18T01:09:51.7789349Z >>> optimizer_class=torch.optim.Adam, 2024-12-18T01:09:51.7789666Z >>> lr=0.01 2024-12-18T01:09:51.7789906Z >>> ) 2024-12-18T01:09:51.7790142Z >>> ddp(inputs).sum().backward() 2024-12-18T01:09:51.7790450Z >>> opt.step() 2024-12-18T01:09:51.7790593Z 2024-12-18T01:09:51.7790700Z .. warning:: 2024-12-18T01:09:51.7791035Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-12-18T01:09:51.7791511Z passed-in parameters are the same dense type. 2024-12-18T01:09:51.7791778Z 2024-12-18T01:09:51.7791870Z .. warning:: 2024-12-18T01:09:51.7792228Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-12-18T01:09:51.7792769Z the way that overlapping :class:`DistributedDataParallel` with 2024-12-18T01:09:51.7793326Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-12-18T01:09:51.7793889Z two or three training iterations do not perform parameter updates in 2024-12-18T01:09:51.7794422Z the optimizer step, depending on if ``static_graph=False`` or 2024-12-18T01:09:51.7794919Z ``static_graph=True``, respectively. This is because it needs 2024-12-18T01:09:51.7795415Z information about the gradient bucketing strategy used by 2024-12-18T01:09:51.7795943Z :class:`DistributedDataParallel`, which is not finalized until the 2024-12-18T01:09:51.7796488Z second forward pass if ``static_graph=False`` or until the third 2024-12-18T01:09:51.7797009Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-12-18T01:09:51.7797447Z is to prepend dummy inputs. 2024-12-18T01:09:51.7797652Z 2024-12-18T01:09:51.7797906Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-12-18T01:09:51.7798269Z 2024-12-18T01:09:51.7798413Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-12-18T01:09:51.7798644Z 2024-12-18T01:09:51.7798722Z 2024-12-18T01:09:51.7798984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7799347Z 2024-12-18T01:09:51.7944616Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-12-18T01:09:51.7945622Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.7945996Z 2024-12-18T01:09:51.7946224Z Custom reducer class that can be used to specify a custom operation that 2024-12-18T01:09:51.7946744Z reduces losses of multiple microbatches into one value. 2024-12-18T01:09:51.7947034Z 2024-12-18T01:09:51.7947124Z Example: 2024-12-18T01:09:51.7947350Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.7947771Z >>> sum_reducer = _CustomReducer( 2024-12-18T01:09:51.7948080Z >>> torch.tensor(0.0), 2024-12-18T01:09:51.7948430Z >>> lambda a, b: a + b 2024-12-18T01:09:51.7948697Z >>> ) 2024-12-18T01:09:51.7948837Z 2024-12-18T01:09:51.7949091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.7949457Z 2024-12-18T01:09:51.8385996Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-12-18T01:09:51.8386965Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.8387343Z 2024-12-18T01:09:51.8387597Z A decorator for a function indicating that the return value of the function 2024-12-18T01:09:51.8388152Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-12-18T01:09:51.8388775Z function can run asynchronously on the RPC callee. More specifically, the 2024-12-18T01:09:51.8389394Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-12-18T01:09:51.8390024Z function and installs subsequent processing steps as a callback to that 2024-12-18T01:09:51.8390614Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-12-18T01:09:51.8391181Z from the :class:`~torch.futures.Future` when completed and send the 2024-12-18T01:09:51.8391689Z value back as the RPC response. That also means the returned 2024-12-18T01:09:51.8392219Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-12-18T01:09:51.8392794Z sent through RPC. This decorator is useful when the wrapped function's 2024-12-18T01:09:51.8393329Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-12-18T01:09:51.8393876Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-12-18T01:09:51.8394222Z 2024-12-18T01:09:51.8394448Z .. note:: To enable asynchronous execution, applications must pass the 2024-12-18T01:09:51.8395014Z function object returned by this decorator to RPC APIs. If RPC detected 2024-12-18T01:09:51.8395586Z attributes installed by this decorator, it knows that this function 2024-12-18T01:09:51.8396110Z returns a ``Future`` object and will handle that accordingly. 2024-12-18T01:09:51.8396616Z However, this does not mean this decorator has to be outmost one when 2024-12-18T01:09:51.8397176Z defining a function. For example, when combined with ``@staticmethod`` 2024-12-18T01:09:51.8397727Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-12-18T01:09:51.8398280Z inner decorator to allow the target function be recognized as a static 2024-12-18T01:09:51.8398852Z or class function. This target function can still execute asynchronously 2024-12-18T01:09:51.8399430Z because, when accessed, the static or class method preserves attributes 2024-12-18T01:09:51.8399918Z installed by ``@rpc.functions.async_execution``. 2024-12-18T01:09:51.8400191Z 2024-12-18T01:09:51.8400195Z 2024-12-18T01:09:51.8400292Z Example:: 2024-12-18T01:09:51.8400631Z The returned :class:`~torch.futures.Future` object can come from 2024-12-18T01:09:51.8401083Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-12-18T01:09:51.8401735Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-12-18T01:09:51.8402247Z constructor. The example below shows directly using the 2024-12-18T01:09:51.8402676Z :class:`~torch.futures.Future` returned by 2024-12-18T01:09:51.8403043Z :meth:`~torch.futures.Future.then`. 2024-12-18T01:09:51.8403275Z 2024-12-18T01:09:51.8403396Z >>> from torch.distributed import rpc 2024-12-18T01:09:51.8403713Z >>> 2024-12-18T01:09:51.8403953Z >>> # omitting setup and shutdown RPC 2024-12-18T01:09:51.8404250Z >>> 2024-12-18T01:09:51.8404466Z >>> # On all workers 2024-12-18T01:09:51.8404752Z >>> @rpc.functions.async_execution 2024-12-18T01:09:51.8405088Z >>> def async_add_chained(to, x, y, z): 2024-12-18T01:09:51.8405579Z >>> # This function runs on "worker1" and returns immediately when 2024-12-18T01:09:51.8406084Z >>> # the callback is installed through the `then(cb)` API. In the 2024-12-18T01:09:51.8406584Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-12-18T01:09:51.8407051Z >>> # When the return value of that `rpc_async` arrives at 2024-12-18T01:09:51.8407521Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-12-18T01:09:51.8408029Z >>> # and set the value for the previously returned `Future`, which 2024-12-18T01:09:51.8408513Z >>> # will then trigger RPC to send the result back to "worker0". 2024-12-18T01:09:51.8408991Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:51.8409392Z >>> lambda fut: fut.wait() + z 2024-12-18T01:09:51.8409702Z >>> ) 2024-12-18T01:09:51.8409920Z >>> 2024-12-18T01:09:51.8410126Z >>> # On worker0 2024-12-18T01:09:51.8410383Z >>> # xdoctest: +SKIP 2024-12-18T01:09:51.8410658Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:51.8410933Z >>> "worker1", 2024-12-18T01:09:51.8411193Z >>> async_add_chained, 2024-12-18T01:09:51.8411495Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-12-18T01:09:51.8411821Z >>> ) 2024-12-18T01:09:51.8412069Z >>> print(ret) # prints tensor([3., 3.]) 2024-12-18T01:09:51.8412294Z 2024-12-18T01:09:51.8412538Z When combined with TorchScript decorators, this decorator must be the 2024-12-18T01:09:51.8412980Z outmost one. 2024-12-18T01:09:51.8413122Z 2024-12-18T01:09:51.8413227Z >>> from torch import Tensor 2024-12-18T01:09:51.8413545Z >>> from torch.futures import Future 2024-12-18T01:09:51.8413894Z >>> from torch.distributed import rpc 2024-12-18T01:09:51.8414216Z >>> 2024-12-18T01:09:51.8414458Z >>> # omitting setup and shutdown RPC 2024-12-18T01:09:51.8414760Z >>> 2024-12-18T01:09:51.8414983Z >>> # On all workers 2024-12-18T01:09:51.8415263Z >>> @torch.jit.script 2024-12-18T01:09:51.8415589Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-12-18T01:09:51.8415947Z >>> return x + y 2024-12-18T01:09:51.8416191Z >>> 2024-12-18T01:09:51.8416437Z >>> @rpc.functions.async_execution 2024-12-18T01:09:51.8416761Z >>> @torch.jit.script 2024-12-18T01:09:51.8417128Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-12-18T01:09:51.8417575Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-12-18T01:09:51.8417901Z >>> 2024-12-18T01:09:51.8418113Z >>> # On worker0 2024-12-18T01:09:51.8418363Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:51.8418629Z >>> "worker1", 2024-12-18T01:09:51.8418876Z >>> async_add, 2024-12-18T01:09:51.8419141Z >>> args=("worker2", torch.ones(2), 1) 2024-12-18T01:09:51.8419458Z >>> ) 2024-12-18T01:09:51.8419701Z >>> print(ret) # prints tensor([2., 2.]) 2024-12-18T01:09:51.8419923Z 2024-12-18T01:09:51.8420157Z When combined with static or class method, this decorator must be the 2024-12-18T01:09:51.8420585Z inner one. 2024-12-18T01:09:51.8420717Z 2024-12-18T01:09:51.8420836Z >>> from torch.distributed import rpc 2024-12-18T01:09:51.8421240Z >>> 2024-12-18T01:09:51.8421480Z >>> # omitting setup and shutdown RPC 2024-12-18T01:09:51.8421794Z >>> 2024-12-18T01:09:51.8422015Z >>> # On all workers 2024-12-18T01:09:51.8422287Z >>> class AsyncExecutionClass: 2024-12-18T01:09:51.8422588Z >>> 2024-12-18T01:09:51.8422803Z >>> @staticmethod 2024-12-18T01:09:51.8423092Z >>> @rpc.functions.async_execution 2024-12-18T01:09:51.8423467Z >>> def static_async_add(to, x, y, z): 2024-12-18T01:09:51.8423981Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:51.8424453Z >>> lambda fut: fut.wait() + z 2024-12-18T01:09:51.8424775Z >>> ) 2024-12-18T01:09:51.8425096Z >>> 2024-12-18T01:09:51.8425313Z >>> @classmethod 2024-12-18T01:09:51.8425672Z >>> @rpc.functions.async_execution 2024-12-18T01:09:51.8426031Z >>> def class_async_add(cls, to, x, y, z): 2024-12-18T01:09:51.8426402Z >>> ret_fut = torch.futures.Future() 2024-12-18T01:09:51.8426805Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:51.8427230Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-12-18T01:09:51.8427576Z >>> ) 2024-12-18T01:09:51.8427820Z >>> return ret_fut 2024-12-18T01:09:51.8428090Z >>> 2024-12-18T01:09:51.8428407Z >>> @rpc.functions.async_execution 2024-12-18T01:09:51.8428766Z >>> def bound_async_add(self, to, x, y, z): 2024-12-18T01:09:51.8429166Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:51.8429574Z >>> lambda fut: fut.wait() + z 2024-12-18T01:09:51.8429891Z >>> ) 2024-12-18T01:09:51.8430123Z >>> 2024-12-18T01:09:51.8430334Z >>> # On worker0 2024-12-18T01:09:51.8430579Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:51.8430853Z >>> "worker1", 2024-12-18T01:09:51.8430997Z >>> AsyncExecutionClass.static_async_add, 2024-12-18T01:09:51.8431118Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:09:51.8431204Z >>> ) 2024-12-18T01:09:51.8431334Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:51.8431418Z >>> 2024-12-18T01:09:51.8431529Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:51.8431620Z >>> "worker1", 2024-12-18T01:09:51.8431747Z >>> AsyncExecutionClass.class_async_add, 2024-12-18T01:09:51.8431877Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:09:51.8431961Z >>> ) 2024-12-18T01:09:51.8432090Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:51.8432095Z 2024-12-18T01:09:51.8432255Z This decorator also works with RRef helpers, i.e., . 2024-12-18T01:09:51.8432406Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-12-18T01:09:51.8432564Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-12-18T01:09:51.8432701Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-12-18T01:09:51.8432716Z 2024-12-18T01:09:51.8432834Z >>> from torch.distributed import rpc 2024-12-18T01:09:51.8432923Z >>> 2024-12-18T01:09:51.8433067Z >>> # reuse the AsyncExecutionClass class above 2024-12-18T01:09:51.8433219Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:09:51.8433440Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-12-18T01:09:51.8433557Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:51.8433642Z >>> 2024-12-18T01:09:51.8433804Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:09:51.8434038Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-12-18T01:09:51.8434168Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:51.8434253Z >>> 2024-12-18T01:09:51.8434418Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:09:51.8434650Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-12-18T01:09:51.8434767Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:51.8434872Z 2024-12-18T01:09:51.8435138Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.8435143Z 2024-12-18T01:09:51.8435881Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-12-18T01:09:51.8436337Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:51.8436343Z 2024-12-18T01:09:51.8436552Z Set device mapping between each RPC caller and callee pair. This 2024-12-18T01:09:51.8436750Z function can be called multiple times to incrementally add 2024-12-18T01:09:51.8436866Z device placement configurations. 2024-12-18T01:09:51.8436870Z 2024-12-18T01:09:51.8437069Z Args: 2024-12-18T01:09:51.8437171Z to (str): Callee name. 2024-12-18T01:09:51.8437385Z device_map (Dict of int, str, or torch.device): Device placement 2024-12-18T01:09:51.8437569Z mappings from this worker to the callee. This map must be 2024-12-18T01:09:51.8437666Z invertible. 2024-12-18T01:09:51.8437671Z 2024-12-18T01:09:51.8437776Z Example: 2024-12-18T01:09:51.8437892Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:51.8437997Z >>> # both workers 2024-12-18T01:09:51.8438088Z >>> def add(x, y): 2024-12-18T01:09:51.8438225Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-12-18T01:09:51.8438341Z >>> return x + y, (x + y).to(2) 2024-12-18T01:09:51.8438428Z >>> 2024-12-18T01:09:51.8438534Z >>> # on worker 0 2024-12-18T01:09:51.8438670Z >>> options = TensorPipeRpcBackendOptions( 2024-12-18T01:09:51.8438783Z >>> num_worker_threads=8, 2024-12-18T01:09:51.8438896Z >>> device_maps={"worker1": {0: 1}} 2024-12-18T01:09:51.8439034Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-12-18T01:09:51.8439131Z >>> ) 2024-12-18T01:09:51.8439260Z >>> options.set_device_map("worker1", {1: 2}) 2024-12-18T01:09:51.8439402Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-12-18T01:09:51.8439505Z >>> 2024-12-18T01:09:51.8439600Z >>> rpc.init_rpc( 2024-12-18T01:09:51.8439702Z >>> "worker0", 2024-12-18T01:09:51.8439792Z >>> rank=0, 2024-12-18T01:09:51.8439899Z >>> world_size=2, 2024-12-18T01:09:51.8440028Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-12-18T01:09:51.8440154Z >>> rpc_backend_options=options 2024-12-18T01:09:51.8440241Z >>> ) 2024-12-18T01:09:51.8440328Z >>> 2024-12-18T01:09:51.8440439Z >>> x = torch.ones(2) 2024-12-18T01:09:51.8440599Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-12-18T01:09:51.8440799Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-12-18T01:09:51.8440989Z >>> # sending the return value back, it will follow the invert of 2024-12-18T01:09:51.8441167Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-12-18T01:09:51.8441279Z >>> # cuda:1 on worker0 2024-12-18T01:09:51.8441432Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-12-18T01:09:51.8441590Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-12-18T01:09:51.8441594Z 2024-12-18T01:09:51.8441849Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:51.8441854Z 2024-12-18T01:09:52.0554851Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_func_map.py line=32. 2024-12-18T01:09:52.0555132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.0555182Z 2024-12-18T01:09:52.0555454Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2024-12-18T01:09:52.0555754Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2024-12-18T01:09:52.0556036Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2024-12-18T01:09:52.0556436Z :class:`DTensor` according to the ``out_placements``. 2024-12-18T01:09:52.0556441Z 2024-12-18T01:09:52.0556543Z Args: 2024-12-18T01:09:52.0556753Z func (Callable): the function to be applied on each local shard of 2024-12-18T01:09:52.0556869Z :class:`DTensor` s. 2024-12-18T01:09:52.0557099Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-12-18T01:09:52.0557354Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2024-12-18T01:09:52.0557611Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-12-18T01:09:52.0557855Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-12-18T01:09:52.0558206Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-12-18T01:09:52.0558333Z mapping to the flattened ``output``. 2024-12-18T01:09:52.0558553Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-12-18T01:09:52.0558838Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2024-12-18T01:09:52.0558938Z should be `None`. 2024-12-18T01:09:52.0559188Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-12-18T01:09:52.0559406Z in. In this case, even if `out_placements` is not `None`, the result function 2024-12-18T01:09:52.0559670Z should ignore the desired placements because the function is not running with 2024-12-18T01:09:52.0559768Z :class:`DTensor` s. 2024-12-18T01:09:52.0559946Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-12-18T01:09:52.0560223Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2024-12-18T01:09:52.0560457Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2024-12-18T01:09:52.0560697Z placements of each :class:`DTensor` argument is the same as the required 2024-12-18T01:09:52.0560882Z placements or not. If the placements are not the same and 2024-12-18T01:09:52.0561135Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-12-18T01:09:52.0561375Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2024-12-18T01:09:52.0561641Z the required sharding placements before passing its local tensor to ``func``. 2024-12-18T01:09:52.0561863Z The only exception is when required placements are not ``None`` and the 2024-12-18T01:09:52.0562113Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-12-18T01:09:52.0562326Z will be skipped and the argument will be directly passed to ``func``. 2024-12-18T01:09:52.0562550Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-12-18T01:09:52.0562655Z Default: None 2024-12-18T01:09:52.0562790Z device_mesh (:class:`DeviceMesh`, optional): 2024-12-18T01:09:52.0563019Z the device mesh that all the :class:`DTensor` s are placed on. If not 2024-12-18T01:09:52.0563252Z specified, this will be inferred from the input :class:`DTensor` s' device 2024-12-18T01:09:52.0563501Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2024-12-18T01:09:52.0563609Z device mesh. Default: None. 2024-12-18T01:09:52.0563730Z redistribute_inputs (bool, optional): 2024-12-18T01:09:52.0564000Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2024-12-18T01:09:52.0564245Z their placements are different from the required input placements. If this 2024-12-18T01:09:52.0564485Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-12-18T01:09:52.0564624Z an exception will be raised. Default: False. 2024-12-18T01:09:52.0564629Z 2024-12-18T01:09:52.0564729Z Returns: 2024-12-18T01:09:52.0564984Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-12-18T01:09:52.0565284Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-12-18T01:09:52.0565288Z 2024-12-18T01:09:52.0565388Z Raises: 2024-12-18T01:09:52.0565639Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2024-12-18T01:09:52.0565892Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-12-18T01:09:52.0566020Z argument passed in. 2024-12-18T01:09:52.0566025Z 2024-12-18T01:09:52.0566348Z AssertionError: For any non-DTensor output, we require its corresponding 2024-12-18T01:09:52.0566605Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-12-18T01:09:52.0566766Z if this is not the case. 2024-12-18T01:09:52.0566783Z 2024-12-18T01:09:52.0567113Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-12-18T01:09:52.0567321Z a redistribution according to ``in_placements``. 2024-12-18T01:09:52.0567326Z 2024-12-18T01:09:52.0567429Z Example: 2024-12-18T01:09:52.0567548Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:52.0567701Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-12-18T01:09:52.0567821Z >>> partial_sum_tensor = torch.mm(W, X) 2024-12-18T01:09:52.0568169Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-12-18T01:09:52.0568275Z >>> return reduced_tensor 2024-12-18T01:09:52.0568362Z >>> 2024-12-18T01:09:52.0568501Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-12-18T01:09:52.0568628Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-12-18T01:09:52.0568741Z >>> Y = torch.mm(W, X) 2024-12-18T01:09:52.0568936Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-12-18T01:09:52.0569122Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-12-18T01:09:52.0569230Z >>> 2024-12-18T01:09:52.0569501Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-12-18T01:09:52.0569640Z >>> local_mm_allreduce_forward = local_map( 2024-12-18T01:09:52.0569747Z >>> mm_allreduce_forward, 2024-12-18T01:09:52.0569876Z >>> out_placements=[Replicate()], 2024-12-18T01:09:52.0569996Z >>> in_placements=[col_wise, row_wise], 2024-12-18T01:09:52.0570101Z >>> device_mesh=device_mesh, 2024-12-18T01:09:52.0570200Z >>> ) 2024-12-18T01:09:52.0570284Z >>> 2024-12-18T01:09:52.0570552Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-12-18T01:09:52.0570801Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-12-18T01:09:52.0571144Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-12-18T01:09:52.0571149Z 2024-12-18T01:09:52.0571370Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:09:52.0571380Z 2024-12-18T01:09:52.0571632Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.0571648Z 2024-12-18T01:09:52.0572386Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2024-12-18T01:09:52.0572658Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.0572662Z 2024-12-18T01:09:52.0572938Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2024-12-18T01:09:52.0573174Z strategies for an operator when the tensor inputs and outputs are DTensor. 2024-12-18T01:09:52.0573439Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-12-18T01:09:52.0573684Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-12-18T01:09:52.0574046Z when users would like to overwrite default sharding strategies of existing operators. 2024-12-18T01:09:52.0574051Z 2024-12-18T01:09:52.0574137Z Args: 2024-12-18T01:09:52.0574283Z op (Union[OpOverload, List[OpOverload]]): 2024-12-18T01:09:52.0574478Z An op or a list of ops to register the customized sharding function. 2024-12-18T01:09:52.0574482Z 2024-12-18T01:09:52.0574586Z Returns: 2024-12-18T01:09:52.0574856Z A function decorator which can be used to wrap a function that defines the sharding 2024-12-18T01:09:52.0575127Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-12-18T01:09:52.0575415Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-12-18T01:09:52.0575791Z already implemented the operator. The customized sharding function takes the same inputs 2024-12-18T01:09:52.0576044Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-12-18T01:09:52.0576317Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-12-18T01:09:52.0576598Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-12-18T01:09:52.0576716Z corresponding intput placements. 2024-12-18T01:09:52.0576720Z 2024-12-18T01:09:52.0576809Z Example: 2024-12-18T01:09:52.0576939Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:52.0577072Z >>> @register_sharding(aten._softmax.default) 2024-12-18T01:09:52.0577243Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-12-18T01:09:52.0577385Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-12-18T01:09:52.0577514Z >>> acceptable_shardings = [] 2024-12-18T01:09:52.0577599Z >>> 2024-12-18T01:09:52.0577781Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-12-18T01:09:52.0577936Z >>> acceptable_shardings.append(all_replicate) 2024-12-18T01:09:52.0578020Z >>> 2024-12-18T01:09:52.0578154Z >>> for sharding_dim in range(x.ndim): 2024-12-18T01:09:52.0578269Z >>> if sharding_dim != softmax_dim: 2024-12-18T01:09:52.0578367Z >>> all_sharded = ( 2024-12-18T01:09:52.0578488Z >>> [Shard(sharding_dim)], 2024-12-18T01:09:52.0578612Z >>> [Shard(sharding_dim), None, None], 2024-12-18T01:09:52.0578711Z >>> ) 2024-12-18T01:09:52.0578852Z >>> acceptable_shardings.append(all_sharded) 2024-12-18T01:09:52.0578952Z >>> 2024-12-18T01:09:52.0579061Z >>> return acceptable_shardings 2024-12-18T01:09:52.0579066Z 2024-12-18T01:09:52.0579258Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:09:52.0579274Z 2024-12-18T01:09:52.0579531Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.0579535Z 2024-12-18T01:09:52.0777235Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-12-18T01:09:52.0777521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.0777527Z 2024-12-18T01:09:52.0777918Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:09:52.0778239Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-12-18T01:09:52.0778244Z 2024-12-18T01:09:52.0778337Z Keyword Args: 2024-12-18T01:09:52.0778552Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:09:52.0778878Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-12-18T01:09:52.0779254Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-12-18T01:09:52.0779376Z as a placeholder. default: None. 2024-12-18T01:09:52.0779799Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:09:52.0780172Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:09:52.0780579Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-12-18T01:09:52.0780714Z input_kwarg_layouts (Dict[str, Placement]): 2024-12-18T01:09:52.0781085Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-12-18T01:09:52.0781195Z default: None 2024-12-18T01:09:52.0781359Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-12-18T01:09:52.0781956Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:09:52.0782132Z have the desired DTensor layouts. default: None. 2024-12-18T01:09:52.0782268Z use_local_output (bool, optional): 2024-12-18T01:09:52.0782624Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-12-18T01:09:52.0782712Z Returns: 2024-12-18T01:09:52.0783257Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-12-18T01:09:52.0783266Z 2024-12-18T01:09:52.0783444Z Example:: 2024-12-18T01:09:52.0783636Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:09:52.0783946Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-12-18T01:09:52.0784152Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:09:52.0784239Z >>> ... 2024-12-18T01:09:52.0784546Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:09:52.0784684Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:09:52.0784771Z >>> 2024-12-18T01:09:52.0785121Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-12-18T01:09:52.0785267Z >>> # and then redistributed to Replicated DTensor. 2024-12-18T01:09:52.0785385Z >>> parallelize_module( 2024-12-18T01:09:52.0785519Z >>> block, # this can be a submodule or module 2024-12-18T01:09:52.0785610Z >>> tp_mesh, 2024-12-18T01:09:52.0785727Z >>> parallelize_plan={ 2024-12-18T01:09:52.0785847Z >>> "attn": PrepareModuleInput( 2024-12-18T01:09:52.0786000Z >>> input_layouts=(Shard(0), None, None, ...), 2024-12-18T01:09:52.0786173Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-12-18T01:09:52.0786262Z >>> ), 2024-12-18T01:09:52.0786362Z >>> } 2024-12-18T01:09:52.0786455Z >>> ) 2024-12-18T01:09:52.0786460Z 2024-12-18T01:09:52.0786729Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.0786734Z 2024-12-18T01:09:52.0787404Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-12-18T01:09:52.0787680Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.0787685Z 2024-12-18T01:09:52.0788076Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:09:52.0788495Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-12-18T01:09:52.0788500Z 2024-12-18T01:09:52.0788594Z Keyword Args: 2024-12-18T01:09:52.0788778Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:09:52.0789118Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-12-18T01:09:52.0789515Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-12-18T01:09:52.0789734Z ``None`` need to be specified as a placeholder. 2024-12-18T01:09:52.0789934Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:09:52.0801189Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-12-18T01:09:52.0801412Z have the desired DTensor layouts. 2024-12-18T01:09:52.0801534Z use_local_output (bool, optional): 2024-12-18T01:09:52.0801928Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-12-18T01:09:52.0802021Z Returns: 2024-12-18T01:09:52.0802317Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-12-18T01:09:52.0802324Z 2024-12-18T01:09:52.0802582Z Example:: 2024-12-18T01:09:52.0802696Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:09:52.0803026Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-12-18T01:09:52.0803233Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:09:52.0803335Z >>> ... 2024-12-18T01:09:52.0803642Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:09:52.0803771Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:09:52.0803879Z >>> 2024-12-18T01:09:52.0804281Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-12-18T01:09:52.0804432Z >>> # and then redistributed to Sharded DTensor. 2024-12-18T01:09:52.0804538Z >>> parallelize_module( 2024-12-18T01:09:52.0804672Z >>> block, # this can be a submodule or module 2024-12-18T01:09:52.0804781Z >>> tp_mesh, 2024-12-18T01:09:52.0804921Z >>> parallelize_plan = PrepareModuleOutput( 2024-12-18T01:09:52.0805051Z >>> output_layouts=Replicate(), 2024-12-18T01:09:52.0805176Z >>> desired_output_layouts=Shard(0) 2024-12-18T01:09:52.0805278Z >>> ) 2024-12-18T01:09:52.0805363Z >>> ) 2024-12-18T01:09:52.0805368Z 2024-12-18T01:09:52.0805627Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.0805632Z 2024-12-18T01:09:52.1353742Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-12-18T01:09:52.1354025Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.1354032Z 2024-12-18T01:09:52.1354258Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-12-18T01:09:52.1354504Z distribution where all component are from different parameterizations of 2024-12-18T01:09:52.1354747Z the same distribution type. It is parameterized by a `Categorical` 2024-12-18T01:09:52.1354945Z "selecting distribution" (over `k` component) and a component 2024-12-18T01:09:52.1355177Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-12-18T01:09:52.1355339Z (equal to `[k]`) which indexes each (batch of) component. 2024-12-18T01:09:52.1355343Z 2024-12-18T01:09:52.1355460Z Examples:: 2024-12-18T01:09:52.1355464Z 2024-12-18T01:09:52.1355587Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:52.1355805Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-12-18T01:09:52.1355920Z >>> # weighted normal distributions 2024-12-18T01:09:52.1356042Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:09:52.1356203Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-12-18T01:09:52.1356323Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:09:52.1356328Z 2024-12-18T01:09:52.1356548Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-12-18T01:09:52.1356680Z >>> # weighted bivariate normal distributions 2024-12-18T01:09:52.1356811Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:09:52.1357121Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:09:52.1357253Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-12-18T01:09:52.1357388Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:09:52.1357393Z 2024-12-18T01:09:52.1357577Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-12-18T01:09:52.1357795Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-12-18T01:09:52.1357914Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-12-18T01:09:52.1358043Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:09:52.1358178Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-12-18T01:09:52.1358296Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:09:52.1358301Z 2024-12-18T01:09:52.1358401Z Args: 2024-12-18T01:09:52.1358694Z mixture_distribution: `torch.distributions.Categorical`-like 2024-12-18T01:09:52.1358899Z instance. Manages the probability of selecting component. 2024-12-18T01:09:52.1359074Z The number of categories must match the rightmost batch 2024-12-18T01:09:52.1359276Z dimension of the `component_distribution`. Must have either 2024-12-18T01:09:52.1359416Z scalar `batch_shape` or `batch_shape` matching 2024-12-18T01:09:52.1359551Z `component_distribution.batch_shape[:-1]` 2024-12-18T01:09:52.1359784Z component_distribution: `torch.distributions.Distribution`-like 2024-12-18T01:09:52.1359962Z instance. Right-most batch dimension indexes component. 2024-12-18T01:09:52.1359966Z 2024-12-18T01:09:52.1360242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.1360246Z 2024-12-18T01:09:52.1466287Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-12-18T01:09:52.1466567Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.1466578Z 2024-12-18T01:09:52.1466765Z Creates a RelaxedBernoulli distribution, parametrized by 2024-12-18T01:09:52.1466969Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-12-18T01:09:52.1467186Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-12-18T01:09:52.1467384Z so the values are in (0, 1), and has reparametrizable samples. 2024-12-18T01:09:52.1467388Z 2024-12-18T01:09:52.1467489Z Example:: 2024-12-18T01:09:52.1467493Z 2024-12-18T01:09:52.1467634Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:52.1467779Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-12-18T01:09:52.1467906Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-12-18T01:09:52.1468015Z >>> m.sample() 2024-12-18T01:09:52.1468136Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-12-18T01:09:52.1468141Z 2024-12-18T01:09:52.1468241Z Args: 2024-12-18T01:09:52.1468429Z temperature (Tensor): relaxation temperature 2024-12-18T01:09:52.1468609Z probs (Number, Tensor): the probability of sampling `1` 2024-12-18T01:09:52.1468784Z logits (Number, Tensor): the log-odds of sampling `1` 2024-12-18T01:09:52.1468794Z 2024-12-18T01:09:52.1469046Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.1469051Z 2024-12-18T01:09:52.1481065Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-12-18T01:09:52.1481330Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.1481335Z 2024-12-18T01:09:52.1481564Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-12-18T01:09:52.1481766Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-12-18T01:09:52.1482020Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-12-18T01:09:52.1482184Z its samples are on simplex, and are reparametrizable. 2024-12-18T01:09:52.1482400Z 2024-12-18T01:09:52.1482524Z Example:: 2024-12-18T01:09:52.1482529Z 2024-12-18T01:09:52.1482671Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:52.1482827Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-12-18T01:09:52.1482973Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-12-18T01:09:52.1483066Z >>> m.sample() 2024-12-18T01:09:52.1483198Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-12-18T01:09:52.1483202Z 2024-12-18T01:09:52.1483291Z Args: 2024-12-18T01:09:52.1483445Z temperature (Tensor): relaxation temperature 2024-12-18T01:09:52.1483565Z probs (Tensor): event probabilities 2024-12-18T01:09:52.1483754Z logits (Tensor): unnormalized log probability for each event 2024-12-18T01:09:52.1483877Z 2024-12-18T01:09:52.1484132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.1484137Z 2024-12-18T01:09:52.4658122Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-12-18T01:09:52.4659315Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.4659962Z Return a new dict with new, potentially nested, key value pair 2024-12-18T01:09:52.4660267Z 2024-12-18T01:09:52.4660379Z >>> purchase = { 2024-12-18T01:09:52.4660635Z ... "name": "Alice", 2024-12-18T01:09:52.4661001Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:09:52.4661425Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:09:52.4661870Z ... } 2024-12-18T01:09:52.4662231Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2024-12-18T01:09:52.4662715Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:09:52.4663120Z 'name': 'Alice', 2024-12-18T01:09:52.4663451Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-12-18T01:09:52.4663839Z 2024-12-18T01:09:52.4664217Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.4664581Z 2024-12-18T01:09:52.4665259Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2024-12-18T01:09:52.4666376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.4667223Z Update value in a (potentially) nested dictionary 2024-12-18T01:09:52.4667576Z 2024-12-18T01:09:52.4667668Z inputs: 2024-12-18T01:09:52.4667926Z d - dictionary on which to operate 2024-12-18T01:09:52.4668438Z keys - list or tuple giving the location of the value to be changed in d 2024-12-18T01:09:52.4668922Z func - function to operate on that value 2024-12-18T01:09:52.4669154Z 2024-12-18T01:09:52.4669363Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-12-18T01:09:52.4669906Z original dictionary with v replaced by func(v), but does not mutate the 2024-12-18T01:09:52.4670373Z original dictionary. 2024-12-18T01:09:52.4670569Z 2024-12-18T01:09:52.4670777Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-12-18T01:09:52.4671325Z specified by the keys, with the innermost value set to func(default). 2024-12-18T01:09:52.4671657Z 2024-12-18T01:09:52.4671769Z >>> inc = lambda x: x + 1 2024-12-18T01:09:52.4672064Z >>> update_in({"a": 0}, ["a"], inc) 2024-12-18T01:09:52.4672356Z {'a': 1} 2024-12-18T01:09:52.4672497Z 2024-12-18T01:09:52.4672595Z >>> transaction = { 2024-12-18T01:09:52.4672863Z ... "name": "Alice", 2024-12-18T01:09:52.4673237Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:09:52.4673668Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:09:52.4673977Z ... } 2024-12-18T01:09:52.4674322Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2024-12-18T01:09:52.4675031Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:09:52.4675345Z 'name': 'Alice', 2024-12-18T01:09:52.4675671Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-12-18T01:09:52.4675951Z 2024-12-18T01:09:52.4676072Z >>> # updating a value when k0 is not in d 2024-12-18T01:09:52.4676443Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-12-18T01:09:52.4676789Z {1: {2: {3: 'bar'}}} 2024-12-18T01:09:52.4677071Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2024-12-18T01:09:52.4677402Z {1: 'foo', 2: {3: {4: 1}}} 2024-12-18T01:09:52.4677655Z 2024-12-18T01:09:52.4678024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.4678399Z 2024-12-18T01:09:52.4679175Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2024-12-18T01:09:52.4680189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.4680738Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-12-18T01:09:52.4681024Z 2024-12-18T01:09:52.4681205Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-12-18T01:09:52.4681709Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-12-18T01:09:52.4682019Z 2024-12-18T01:09:52.4682243Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-12-18T01:09:52.4682688Z structures such as dictionaries and lists. 2024-12-18T01:09:52.4682938Z 2024-12-18T01:09:52.4683035Z >>> transaction = { 2024-12-18T01:09:52.4683297Z ... "name": "Alice", 2024-12-18T01:09:52.4683671Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:09:52.4684101Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:09:52.4684423Z ... } 2024-12-18T01:09:52.4684682Z >>> get_in(["purchase", "items", 0], transaction) 2024-12-18T01:09:52.4685021Z 'Apple' 2024-12-18T01:09:52.4685259Z >>> get_in(["name"], transaction) 2024-12-18T01:09:52.4685556Z 'Alice' 2024-12-18T01:09:52.4685800Z >>> get_in(["purchase", "total"], transaction) 2024-12-18T01:09:52.4686196Z >>> get_in(["purchase", "items", "apple"], transaction) 2024-12-18T01:09:52.4686596Z >>> get_in(["purchase", "items", 10], transaction) 2024-12-18T01:09:52.4686981Z >>> get_in(["purchase", "total"], transaction, 0) 2024-12-18T01:09:52.4687315Z 0 2024-12-18T01:09:52.4687537Z >>> get_in(["y"], {}, no_default=True) 2024-12-18T01:09:52.4687873Z Traceback (most recent call last): 2024-12-18T01:09:52.4688179Z ... 2024-12-18T01:09:52.4688404Z KeyError: 'y' 2024-12-18T01:09:52.4688553Z 2024-12-18T01:09:52.4688657Z See Also: 2024-12-18T01:09:52.4688877Z itertoolz.get 2024-12-18T01:09:52.4689141Z operator.getitem 2024-12-18T01:09:52.4689405Z 2024-12-18T01:09:52.4689785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.4690150Z 2024-12-18T01:09:52.4690806Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2024-12-18T01:09:52.4691825Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.4692307Z Group a collection by a key function 2024-12-18T01:09:52.4692529Z 2024-12-18T01:09:52.4692695Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2024-12-18T01:09:52.4693098Z >>> groupby(len, names) # doctest: +SKIP 2024-12-18T01:09:52.4693495Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-12-18T01:09:52.4693771Z 2024-12-18T01:09:52.4693894Z >>> iseven = lambda x: x % 2 == 0 2024-12-18T01:09:52.4694278Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-12-18T01:09:52.4694756Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-12-18T01:09:52.4694990Z 2024-12-18T01:09:52.4695134Z Non-callable keys imply grouping on a member. 2024-12-18T01:09:52.4695399Z 2024-12-18T01:09:52.4695492Z >>> groupby( 2024-12-18T01:09:52.4695732Z ... "gender", 2024-12-18T01:09:52.4695980Z ... [ 2024-12-18T01:09:52.4696225Z ... {"name": "Alice", "gender": "F"}, 2024-12-18T01:09:52.4696583Z ... {"name": "Bob", "gender": "M"}, 2024-12-18T01:09:52.4696933Z ... {"name": "Charlie", "gender": "M"}, 2024-12-18T01:09:52.4697260Z ... ], 2024-12-18T01:09:52.4697496Z ... ) # doctest:+SKIP 2024-12-18T01:09:52.4697771Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-12-18T01:09:52.4698106Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-12-18T01:09:52.4698498Z {'gender': 'M', 'name': 'Charlie'}]} 2024-12-18T01:09:52.4698723Z 2024-12-18T01:09:52.4698877Z Not to be confused with ``itertools.groupby`` 2024-12-18T01:09:52.4699126Z 2024-12-18T01:09:52.4699227Z See Also: 2024-12-18T01:09:52.4699451Z countby 2024-12-18T01:09:52.4699665Z 2024-12-18T01:09:52.4700037Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.4700414Z 2024-12-18T01:09:52.7970781Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-12-18T01:09:52.7971966Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.7972521Z Applies Batch Normalization over a N-Dimensional input. 2024-12-18T01:09:52.7972830Z 2024-12-18T01:09:52.7973176Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-12-18T01:09:52.7973901Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-12-18T01:09:52.7974572Z Internal Covariate Shift `__ . 2024-12-18T01:09:52.7974911Z 2024-12-18T01:09:52.7975032Z .. math:: 2024-12-18T01:09:52.7975171Z 2024-12-18T01:09:52.7975596Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-12-18T01:09:52.7976115Z 2024-12-18T01:09:52.7976359Z The mean and standard-deviation are calculated per-dimension over all 2024-12-18T01:09:52.7976932Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-12-18T01:09:52.7977523Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-12-18T01:09:52.7978063Z By default, the elements of :math:`\gamma` are sampled from 2024-12-18T01:09:52.7978559Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-12-18T01:09:52.7979143Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-12-18T01:09:52.7979637Z `torch.var(input, unbiased=False)`. 2024-12-18T01:09:52.7979858Z 2024-12-18T01:09:52.7980097Z Also by default, during training this layer keeps running estimates of its 2024-12-18T01:09:52.7980697Z computed mean and variance, which are then used for normalization during 2024-12-18T01:09:52.7981301Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-12-18T01:09:52.7981755Z of 0.1. 2024-12-18T01:09:52.7981877Z 2024-12-18T01:09:52.7982118Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-12-18T01:09:52.7982688Z keep running estimates, and batch statistics are instead used during 2024-12-18T01:09:52.7983115Z evaluation time as well. 2024-12-18T01:09:52.7983306Z 2024-12-18T01:09:52.7983398Z .. note:: 2024-12-18T01:09:52.7983757Z This :attr:`momentum` argument is different from one used in optimizer 2024-12-18T01:09:52.7984333Z classes and the conventional notion of momentum. Mathematically, the 2024-12-18T01:09:52.7984809Z update rule for running statistics here is 2024-12-18T01:09:52.7985312Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-12-18T01:09:52.7986126Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-12-18T01:09:52.7986560Z new observed value. 2024-12-18T01:09:52.7986753Z 2024-12-18T01:09:52.7987053Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-12-18T01:09:52.7987736Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-12-18T01:09:52.7988365Z Normalization or Spatio-temporal Batch Normalization. 2024-12-18T01:09:52.7988653Z 2024-12-18T01:09:52.7988802Z Currently :class:`SyncBatchNorm` only supports 2024-12-18T01:09:52.7989353Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-12-18T01:09:52.7990079Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-12-18T01:09:52.7990628Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-12-18T01:09:52.7991069Z Network with DDP. 2024-12-18T01:09:52.7991228Z 2024-12-18T01:09:52.7991330Z Args: 2024-12-18T01:09:52.7991617Z num_features: :math:`C` from an expected input of size 2024-12-18T01:09:52.7992009Z :math:`(N, C, +)` 2024-12-18T01:09:52.7992397Z eps: a value added to the denominator for numerical stability. 2024-12-18T01:09:52.7992804Z Default: ``1e-5`` 2024-12-18T01:09:52.7993184Z momentum: the value used for the running_mean and running_var 2024-12-18T01:09:52.7993696Z computation. Can be set to ``None`` for cumulative moving average 2024-12-18T01:09:52.7994143Z (i.e. simple average). Default: 0.1 2024-12-18T01:09:52.7994580Z affine: a boolean value that when set to ``True``, this module has 2024-12-18T01:09:52.7995060Z learnable affine parameters. Default: ``True`` 2024-12-18T01:09:52.7995542Z track_running_stats: a boolean value that when set to ``True``, this 2024-12-18T01:09:52.7996108Z module tracks the running mean and variance, and when set to ``False``, 2024-12-18T01:09:52.7996670Z this module does not track such statistics, and initializes statistics 2024-12-18T01:09:52.7997217Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-12-18T01:09:52.7997766Z When these buffers are ``None``, this module always uses batch statistics. 2024-12-18T01:09:52.7998267Z in both training and eval modes. Default: ``True`` 2024-12-18T01:09:52.7998784Z process_group: synchronization of stats happen within each process group 2024-12-18T01:09:52.7999377Z individually. Default behavior is synchronization across the whole 2024-12-18T01:09:52.7999799Z world 2024-12-18T01:09:52.7999949Z 2024-12-18T01:09:52.8000042Z Shape: 2024-12-18T01:09:52.8000281Z - Input: :math:`(N, C, +)` 2024-12-18T01:09:52.8000635Z - Output: :math:`(N, C, +)` (same shape as input) 2024-12-18T01:09:52.8000894Z 2024-12-18T01:09:52.8000998Z .. note:: 2024-12-18T01:09:52.8001367Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-12-18T01:09:52.8001936Z synchronization is disabled when ``model.eval()`` is set or if 2024-12-18T01:09:52.8002388Z ``self.training`` is otherwise ``False``. 2024-12-18T01:09:52.8002623Z 2024-12-18T01:09:52.8002731Z Examples:: 2024-12-18T01:09:52.8002870Z 2024-12-18T01:09:52.8002984Z >>> # xdoctest: +SKIP 2024-12-18T01:09:52.8003286Z >>> # With Learnable Parameters 2024-12-18T01:09:52.8003612Z >>> m = nn.SyncBatchNorm(100) 2024-12-18T01:09:52.8003948Z >>> # creating process group (optional) 2024-12-18T01:09:52.8004328Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:09:52.8004692Z >>> ranks = list(range(8)) 2024-12-18T01:09:52.8005002Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:09:52.8005356Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:09:52.8005883Z >>> # process group created, even if that rank is not 2024-12-18T01:09:52.8006260Z >>> # part of the group. 2024-12-18T01:09:52.8006713Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:09:52.8007295Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:09:52.8007730Z >>> # Without Learnable Parameters 2024-12-18T01:09:52.8008167Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-12-18T01:09:52.8008626Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-12-18T01:09:52.8008967Z >>> output = m(input) 2024-12-18T01:09:52.8009148Z 2024-12-18T01:09:52.8009279Z >>> # network is nn.BatchNorm layer 2024-12-18T01:09:52.8009849Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-12-18T01:09:52.8010398Z >>> # only single gpu per process is currently supported 2024-12-18T01:09:52.8010909Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:09:52.8011366Z >>> sync_bn_network, 2024-12-18T01:09:52.8011733Z >>> device_ids=[args.local_rank], 2024-12-18T01:09:52.8012121Z >>> output_device=args.local_rank) 2024-12-18T01:09:52.8012449Z 2024-12-18T01:09:52.8012825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.8013205Z 2024-12-18T01:09:52.8013873Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-12-18T01:09:52.8014897Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.8015584Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-12-18T01:09:52.8016007Z 2024-12-18T01:09:52.8016114Z Args: 2024-12-18T01:09:52.8016499Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-12-18T01:09:52.8017093Z process_group (optional): process group to scope synchronization, 2024-12-18T01:09:52.8017531Z default is the whole world 2024-12-18T01:09:52.8017764Z 2024-12-18T01:09:52.8017851Z Returns: 2024-12-18T01:09:52.8018247Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-12-18T01:09:52.8018837Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-12-18T01:09:52.8019392Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-12-18T01:09:52.8019803Z instead. 2024-12-18T01:09:52.8019966Z 2024-12-18T01:09:52.8020063Z Example:: 2024-12-18T01:09:52.8020216Z 2024-12-18T01:09:52.8020338Z >>> # Network with nn.BatchNorm layer 2024-12-18T01:09:52.8020717Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:52.8021100Z >>> module = torch.nn.Sequential( 2024-12-18T01:09:52.8021451Z >>> torch.nn.Linear(20, 100), 2024-12-18T01:09:52.8021802Z >>> torch.nn.BatchNorm1d(100), 2024-12-18T01:09:52.8022138Z >>> ).cuda() 2024-12-18T01:09:52.8022455Z >>> # creating process group (optional) 2024-12-18T01:09:52.8022845Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:09:52.8023214Z >>> ranks = list(range(8)) 2024-12-18T01:09:52.8023527Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:09:52.8023899Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:09:52.8024322Z >>> # process group created, even if that rank is not 2024-12-18T01:09:52.8024694Z >>> # part of the group. 2024-12-18T01:09:52.8025022Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:52.8025565Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:09:52.8026145Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:09:52.8026771Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-12-18T01:09:52.8027195Z 2024-12-18T01:09:52.8027285Z 2024-12-18T01:09:52.8027666Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.8028030Z 2024-12-18T01:09:52.8221934Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-12-18T01:09:52.8222856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.8223500Z 2024-12-18T01:09:52.8223824Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-12-18T01:09:52.8224251Z 2024-12-18T01:09:52.8224537Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-12-18T01:09:52.8225163Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-12-18T01:09:52.8225521Z 2024-12-18T01:09:52.8225835Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-12-18T01:09:52.8226527Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-12-18T01:09:52.8227072Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-12-18T01:09:52.8227352Z 2024-12-18T01:09:52.8227455Z Shape: 2024-12-18T01:09:52.8227799Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-12-18T01:09:52.8228458Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-12-18T01:09:52.8229053Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-12-18T01:09:52.8229533Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-12-18T01:09:52.8229770Z 2024-12-18T01:09:52.8229870Z Args: 2024-12-18T01:09:52.8230148Z dim (Union[int, str]): Dimension to be unflattened 2024-12-18T01:09:52.8230744Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-12-18T01:09:52.8231217Z 2024-12-18T01:09:52.8231309Z Examples: 2024-12-18T01:09:52.8231546Z >>> input = torch.randn(2, 50) 2024-12-18T01:09:52.8231855Z >>> # With tuple of ints 2024-12-18T01:09:52.8232142Z >>> m = nn.Sequential( 2024-12-18T01:09:52.8232404Z >>> nn.Linear(50, 50), 2024-12-18T01:09:52.8232695Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-12-18T01:09:52.8232995Z >>> ) 2024-12-18T01:09:52.8233218Z >>> output = m(input) 2024-12-18T01:09:52.8233489Z >>> output.size() 2024-12-18T01:09:52.8233738Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:09:52.8234015Z >>> # With torch.Size 2024-12-18T01:09:52.8234282Z >>> m = nn.Sequential( 2024-12-18T01:09:52.8234560Z >>> nn.Linear(50, 50), 2024-12-18T01:09:52.8234869Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-12-18T01:09:52.8235186Z >>> ) 2024-12-18T01:09:52.8235414Z >>> output = m(input) 2024-12-18T01:09:52.8235686Z >>> output.size() 2024-12-18T01:09:52.8235950Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:09:52.8236430Z >>> # With namedshape (tuple of tuples) 2024-12-18T01:09:52.8236803Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-12-18T01:09:52.8237279Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-12-18T01:09:52.8237713Z >>> output = unflatten(input) 2024-12-18T01:09:52.8238017Z >>> output.size() 2024-12-18T01:09:52.8238280Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:09:52.8238458Z 2024-12-18T01:09:52.8238715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.8239093Z 2024-12-18T01:09:52.8570240Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1698. 2024-12-18T01:09:52.8571556Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.8572139Z Creates a criterion that measures the triplet loss given input 2024-12-18T01:09:52.8572661Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-12-18T01:09:52.8573193Z positive, and negative examples, respectively), and a nonnegative, 2024-12-18T01:09:52.8573813Z real-valued function ("distance function") used to compute the relationship 2024-12-18T01:09:52.8574405Z between the anchor and positive example ("positive distance") and the 2024-12-18T01:09:52.8574906Z anchor and negative example ("negative distance"). 2024-12-18T01:09:52.8575270Z 2024-12-18T01:09:52.8575483Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-12-18T01:09:52.8575918Z can be described as: 2024-12-18T01:09:52.8576108Z 2024-12-18T01:09:52.8576215Z .. math:: 2024-12-18T01:09:52.8576498Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-12-18T01:09:52.8576917Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-12-18T01:09:52.8577185Z 2024-12-18T01:09:52.8577439Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-12-18T01:09:52.8578092Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-12-18T01:09:52.8578749Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-12-18T01:09:52.8579363Z between the positive and negative distances that is required for the loss to 2024-12-18T01:09:52.8579969Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-12-18T01:09:52.8580445Z that the distance function can handle. 2024-12-18T01:09:52.8580672Z 2024-12-18T01:09:52.8580803Z If :attr:`reduction` is not ``'none'`` 2024-12-18T01:09:52.8581128Z (default ``'mean'``), then: 2024-12-18T01:09:52.8581329Z 2024-12-18T01:09:52.8581420Z .. math:: 2024-12-18T01:09:52.8581653Z \ell(x, y) = 2024-12-18T01:09:52.8581906Z \begin{cases} 2024-12-18T01:09:52.8582272Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-12-18T01:09:52.8582779Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-12-18T01:09:52.8583181Z \end{cases} 2024-12-18T01:09:52.8583338Z 2024-12-18T01:09:52.8583572Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-12-18T01:09:52.8584177Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-12-18T01:09:52.8584541Z 2024-12-18T01:09:52.8584639Z Args: 2024-12-18T01:09:52.8585038Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-12-18T01:09:52.8585611Z quantifies the closeness of two tensors. If not specified, 2024-12-18T01:09:52.8586095Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-12-18T01:09:52.8586649Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-12-18T01:09:52.8587310Z between the positive and negative distances required for the loss to be 0. Larger 2024-12-18T01:09:52.8587978Z margins penalize cases where the negative examples are not distant enough from the 2024-12-18T01:09:52.8588638Z anchors, relative to the positives. Default: :math:`1`. 2024-12-18T01:09:52.8589167Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-12-18T01:09:52.8589801Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-12-18T01:09:52.8590419Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-12-18T01:09:52.8591044Z negative example than the anchor is, swaps the positive example and the anchor in 2024-12-18T01:09:52.8591661Z the loss computation. Default: ``False``. 2024-12-18T01:09:52.8592195Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-12-18T01:09:52.8592772Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-12-18T01:09:52.8593264Z ``'mean'``: the sum of the output will be divided by the number of 2024-12-18T01:09:52.8593816Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-12-18T01:09:52.8594185Z 2024-12-18T01:09:52.8594189Z 2024-12-18T01:09:52.8594280Z Shape: 2024-12-18T01:09:52.8594661Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-12-18T01:09:52.8595218Z as supported by the distance function. 2024-12-18T01:09:52.8595707Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-12-18T01:09:52.8596178Z otherwise. 2024-12-18T01:09:52.8596347Z 2024-12-18T01:09:52.8596444Z Examples:: 2024-12-18T01:09:52.8596581Z 2024-12-18T01:09:52.8596702Z >>> # Initialize embeddings 2024-12-18T01:09:52.8597025Z >>> embedding = nn.Embedding(1000, 128) 2024-12-18T01:09:52.8597369Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:09:52.8597741Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:09:52.8598118Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:09:52.8598473Z >>> anchor = embedding(anchor_ids) 2024-12-18T01:09:52.8598811Z >>> positive = embedding(positive_ids) 2024-12-18T01:09:52.8599157Z >>> negative = embedding(negative_ids) 2024-12-18T01:09:52.8599459Z >>> 2024-12-18T01:09:52.8599689Z >>> # Built-in Distance Function 2024-12-18T01:09:52.8600003Z >>> triplet_loss = \ 2024-12-18T01:09:52.8600452Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-12-18T01:09:52.8601011Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:09:52.8601374Z >>> output.backward() 2024-12-18T01:09:52.8601638Z >>> 2024-12-18T01:09:52.8601864Z >>> # Custom Distance Function 2024-12-18T01:09:52.8602170Z >>> def l_infinity(x1, x2): 2024-12-18T01:09:52.8602505Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-12-18T01:09:52.8602863Z >>> 2024-12-18T01:09:52.8603177Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-12-18T01:09:52.8603583Z >>> triplet_loss = ( 2024-12-18T01:09:52.8604031Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-12-18T01:09:52.8604586Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:09:52.8604945Z >>> output.backward() 2024-12-18T01:09:52.8605213Z >>> 2024-12-18T01:09:52.8605452Z >>> # Custom Distance Function (Lambda) 2024-12-18T01:09:52.8605776Z >>> triplet_loss = ( 2024-12-18T01:09:52.8606080Z >>> nn.TripletMarginWithDistanceLoss( 2024-12-18T01:09:52.8606532Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-12-18T01:09:52.8607021Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:09:52.8607393Z >>> output.backward() 2024-12-18T01:09:52.8607560Z 2024-12-18T01:09:52.8607665Z Reference: 2024-12-18T01:09:52.8608113Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-12-18T01:09:52.8608755Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2024-12-18T01:09:52.8609186Z 2024-12-18T01:09:52.8609560Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-12-18T01:09:52.8609927Z 2024-12-18T01:09:52.9157507Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-12-18T01:09:52.9158589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.9159360Z Computes a partial inverse of :class:`MaxPool2d`. 2024-12-18T01:09:52.9159624Z 2024-12-18T01:09:52.9159895Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-12-18T01:09:52.9160298Z 2024-12-18T01:09:52.9160536Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-12-18T01:09:52.9161125Z including the indices of the maximal values and computes a partial inverse 2024-12-18T01:09:52.9161619Z in which all non-maximal values are set to zero. 2024-12-18T01:09:52.9161886Z 2024-12-18T01:09:52.9161976Z Note: 2024-12-18T01:09:52.9162419Z This operation may behave nondeterministically when the input indices has repeat values. 2024-12-18T01:09:52.9163346Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-12-18T01:09:52.9163837Z 2024-12-18T01:09:52.9164095Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-12-18T01:09:52.9164626Z sizes. Hence, the inversion process can get ambiguous. 2024-12-18T01:09:52.9165101Z To accommodate this, you can provide the needed output size 2024-12-18T01:09:52.9165624Z as an additional argument :attr:`output_size` in the forward call. 2024-12-18T01:09:52.9166081Z See the Inputs and Example below. 2024-12-18T01:09:52.9166313Z 2024-12-18T01:09:52.9166418Z Args: 2024-12-18T01:09:52.9166735Z kernel_size (int or tuple): Size of the max pooling window. 2024-12-18T01:09:52.9167201Z stride (int or tuple): Stride of the max pooling window. 2024-12-18T01:09:52.9167633Z It is set to :attr:`kernel_size` by default. 2024-12-18T01:09:52.9168076Z padding (int or tuple): Padding that was added to the input 2024-12-18T01:09:52.9168387Z 2024-12-18T01:09:52.9168480Z Inputs: 2024-12-18T01:09:52.9168740Z - `input`: the input Tensor to invert 2024-12-18T01:09:52.9169186Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-12-18T01:09:52.9169668Z - `output_size` (optional): the targeted output size 2024-12-18T01:09:52.9169948Z 2024-12-18T01:09:52.9170042Z Shape: 2024-12-18T01:09:52.9170364Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-12-18T01:09:52.9170884Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-12-18T01:09:52.9171215Z 2024-12-18T01:09:52.9171320Z .. math:: 2024-12-18T01:09:52.9171743Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-12-18T01:09:52.9172123Z 2024-12-18T01:09:52.9172216Z .. math:: 2024-12-18T01:09:52.9172618Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-12-18T01:09:52.9172999Z 2024-12-18T01:09:52.9173162Z or as given by :attr:`output_size` in the call operator 2024-12-18T01:09:52.9173449Z 2024-12-18T01:09:52.9173544Z Example:: 2024-12-18T01:09:52.9173674Z 2024-12-18T01:09:52.9173850Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-12-18T01:09:52.9174252Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-12-18T01:09:52.9174613Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-12-18T01:09:52.9174973Z [ 5., 6., 7., 8.], 2024-12-18T01:09:52.9175310Z [ 9., 10., 11., 12.], 2024-12-18T01:09:52.9175647Z [13., 14., 15., 16.]]]]) 2024-12-18T01:09:52.9175994Z >>> output, indices = pool(input) 2024-12-18T01:09:52.9176318Z >>> unpool(output, indices) 2024-12-18T01:09:52.9176632Z tensor([[[[ 0., 0., 0., 0.], 2024-12-18T01:09:52.9176962Z [ 0., 6., 0., 8.], 2024-12-18T01:09:52.9177271Z [ 0., 0., 0., 0.], 2024-12-18T01:09:52.9177584Z [ 0., 14., 0., 16.]]]]) 2024-12-18T01:09:52.9178095Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-12-18T01:09:52.9178557Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-12-18T01:09:52.9178934Z [ 6., 7., 8., 9., 10.], 2024-12-18T01:09:52.9179279Z [11., 12., 13., 14., 15.], 2024-12-18T01:09:52.9179627Z [16., 17., 18., 19., 20.]]]]) 2024-12-18T01:09:52.9179979Z >>> output, indices = pool(input) 2024-12-18T01:09:52.9180367Z >>> # This call will not work without specifying output_size 2024-12-18T01:09:52.9180819Z >>> unpool(output, indices, output_size=input.size()) 2024-12-18T01:09:52.9181264Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-12-18T01:09:52.9181588Z [ 0., 7., 0., 9., 0.], 2024-12-18T01:09:52.9181901Z [ 0., 0., 0., 0., 0.], 2024-12-18T01:09:52.9182206Z [ 0., 17., 0., 19., 0.]]]]) 2024-12-18T01:09:52.9182432Z 2024-12-18T01:09:52.9182436Z 2024-12-18T01:09:52.9182521Z 2024-12-18T01:09:52.9182887Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.9183262Z 2024-12-18T01:09:52.9444282Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-12-18T01:09:52.9445353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.9446049Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-12-18T01:09:52.9446489Z 2024-12-18T01:09:52.9446820Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-12-18T01:09:52.9447367Z and with 2D inputs, this class 2024-12-18T01:09:52.9447568Z 2024-12-18T01:09:52.9447887Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-12-18T01:09:52.9448618Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-12-18T01:09:52.9449357Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-12-18T01:09:52.9449786Z 2024-12-18T01:09:52.9450139Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-12-18T01:09:52.9450708Z operations. 2024-12-18T01:09:52.9450847Z 2024-12-18T01:09:52.9451116Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-12-18T01:09:52.9451727Z pass. This scales the output of the Embedding before performing a weighted 2024-12-18T01:09:52.9452340Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-12-18T01:09:52.9452933Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-12-18T01:09:52.9453401Z :attr:`per_sample_weights`. 2024-12-18T01:09:52.9453604Z 2024-12-18T01:09:52.9453690Z Args: 2024-12-18T01:09:52.9454003Z num_embeddings (int): size of the dictionary of embeddings 2024-12-18T01:09:52.9454472Z embedding_dim (int): the size of each embedding vector 2024-12-18T01:09:52.9455071Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-12-18T01:09:52.9455649Z is renormalized to have norm :attr:`max_norm`. 2024-12-18T01:09:52.9456278Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-12-18T01:09:52.9457067Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-12-18T01:09:52.9457683Z the words in the mini-batch. Default ``False``. 2024-12-18T01:09:52.9458158Z Note: this option is not supported when ``mode="max"``. 2024-12-18T01:09:52.9458960Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-12-18T01:09:52.9459546Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-12-18T01:09:52.9460118Z into consideration. ``"mean"`` computes the average of the values 2024-12-18T01:09:52.9460643Z in the bag, ``"max"`` computes the max value over each bag. 2024-12-18T01:09:52.9461066Z Default: ``"mean"`` 2024-12-18T01:09:52.9461620Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-12-18T01:09:52.9462418Z Notes for more details regarding sparse gradients. Note: this option is not 2024-12-18T01:09:52.9462917Z supported when ``mode="max"``. 2024-12-18T01:09:52.9463548Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-12-18T01:09:52.9464272Z is equivalent to the size of `indices`. This matches the CSR format. 2024-12-18T01:09:52.9464952Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-12-18T01:09:52.9465677Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-12-18T01:09:52.9466324Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-12-18T01:09:52.9466980Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-12-18T01:09:52.9467610Z zeros, but can be updated to another value to be used as the padding vector. 2024-12-18T01:09:52.9468240Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-12-18T01:09:52.9468806Z reduction. 2024-12-18T01:09:52.9469039Z 2024-12-18T01:09:52.9469134Z Attributes: 2024-12-18T01:09:52.9469604Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-12-18T01:09:52.9470190Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-12-18T01:09:52.9470446Z 2024-12-18T01:09:52.9470560Z Examples:: 2024-12-18T01:09:52.9470712Z 2024-12-18T01:09:52.9470887Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-12-18T01:09:52.9471340Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-12-18T01:09:52.9471757Z >>> # a batch of 2 samples of 4 indices each 2024-12-18T01:09:52.9472194Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:09:52.9472663Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:09:52.9473067Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:52.9473452Z >>> embedding_sum(input, offsets) 2024-12-18T01:09:52.9473795Z tensor([[-0.8861, -5.4350, -0.0523], 2024-12-18T01:09:52.9474125Z [ 1.1306, -2.5798, -1.0044]]) 2024-12-18T01:09:52.9474338Z 2024-12-18T01:09:52.9474464Z >>> # Example with padding_idx 2024-12-18T01:09:52.9474875Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-12-18T01:09:52.9475387Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:09:52.9475850Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:09:52.9476237Z >>> embedding_sum(input, offsets) 2024-12-18T01:09:52.9476565Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-12-18T01:09:52.9476882Z [-0.7082, 3.2145, -2.6251]]) 2024-12-18T01:09:52.9477161Z 2024-12-18T01:09:52.9477338Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-12-18T01:09:52.9477783Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-12-18T01:09:52.9478205Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-12-18T01:09:52.9478583Z embedding.weight, 2024-12-18T01:09:52.9478914Z padding_idx=embedding.padding_idx, 2024-12-18T01:09:52.9479245Z mode='sum') 2024-12-18T01:09:52.9479513Z 2024-12-18T01:09:52.9479880Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.9480241Z 2024-12-18T01:09:52.9794067Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-12-18T01:09:52.9795113Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.9795626Z 2024-12-18T01:09:52.9796107Z Context manager for training with uneven inputs across processes in DDP. 2024-12-18T01:09:52.9796457Z 2024-12-18T01:09:52.9796692Z This context manager will keep track of already-joined DDP processes, 2024-12-18T01:09:52.9797225Z and "shadow" the forward and backward passes by inserting collective 2024-12-18T01:09:52.9797784Z communication operations to match with the ones created by non-joined 2024-12-18T01:09:52.9798365Z DDP processes. This will ensure each collective call has a corresponding 2024-12-18T01:09:52.9798935Z call by already-joined DDP processes, preventing hangs or errors that 2024-12-18T01:09:52.9799498Z would otherwise happen when training with uneven inputs across 2024-12-18T01:09:52.9800051Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-12-18T01:09:52.9800621Z specified to be ``True``, all trainers will throw an error once one rank 2024-12-18T01:09:52.9801134Z runs out of inputs, allowing these errors to be caught and handled 2024-12-18T01:09:52.9801568Z according to application logic. 2024-12-18T01:09:52.9801779Z 2024-12-18T01:09:52.9801997Z Once all DDP processes have joined, the context manager will broadcast 2024-12-18T01:09:52.9802563Z the model corresponding to the last joined process to all processes to 2024-12-18T01:09:52.9803054Z ensure the model is the same across all processes 2024-12-18T01:09:52.9803428Z (which is guaranteed by DDP). 2024-12-18T01:09:52.9803615Z 2024-12-18T01:09:52.9803816Z To use this to enable training with uneven inputs across processes, 2024-12-18T01:09:52.9804359Z simply wrap this context manager around your training loop. No further 2024-12-18T01:09:52.9804869Z modifications to the model or data loading is required. 2024-12-18T01:09:52.9805160Z 2024-12-18T01:09:52.9805271Z .. warning:: 2024-12-18T01:09:52.9805621Z If the model or training loop this context manager is wrapped around 2024-12-18T01:09:52.9806133Z has additional distributed collective operations, such as 2024-12-18T01:09:52.9806610Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-12-18T01:09:52.9807125Z ``throw_on_early_termination`` must be enabled. This is because this 2024-12-18T01:09:52.9807662Z context manager is not aware of non-DDP collective communication. 2024-12-18T01:09:52.9808161Z This flag will cause all ranks to throw when any one rank 2024-12-18T01:09:52.9808664Z exhausts inputs, allowing these errors to be caught and recovered 2024-12-18T01:09:52.9809083Z from across all ranks. 2024-12-18T01:09:52.9809267Z 2024-12-18T01:09:52.9809353Z Args: 2024-12-18T01:09:52.9809662Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-12-18T01:09:52.9810178Z gradients by the initial ``world_size`` DDP training was launched 2024-12-18T01:09:52.9810761Z with. If ``False``, will compute the effective world size 2024-12-18T01:09:52.9811232Z (number of ranks that have not depleted their inputs yet) and 2024-12-18T01:09:52.9811676Z divide gradients by that during allreduce. Set 2024-12-18T01:09:52.9812255Z ``divide_by_initial_world_size=True`` to ensure every input 2024-12-18T01:09:52.9812769Z sample including the uneven inputs have equal weight in terms of 2024-12-18T01:09:52.9813269Z how much they contribute to the global gradient. This is 2024-12-18T01:09:52.9813731Z achieved by always dividing the gradient by the initial 2024-12-18T01:09:52.9814199Z ``world_size`` even when we encounter uneven inputs. If you set 2024-12-18T01:09:52.9814679Z this to ``False``, we divide the gradient by the remaining 2024-12-18T01:09:52.9815170Z number of nodes. This ensures parity with training on a smaller 2024-12-18T01:09:52.9815671Z ``world_size`` although it also means the uneven inputs would 2024-12-18T01:09:52.9816223Z contribute more towards the global gradient. Typically, you 2024-12-18T01:09:52.9816722Z would want to set this to ``True`` for cases where the last few 2024-12-18T01:09:52.9817221Z inputs of your training job are uneven. In extreme cases, where 2024-12-18T01:09:52.9817730Z there is a large discrepancy in the number of inputs, setting 2024-12-18T01:09:52.9818185Z this to ``False`` might provide better results. 2024-12-18T01:09:52.9818656Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-12-18T01:09:52.9819160Z in ``enable=False`` to disable in cases where you know that 2024-12-18T01:09:52.9819643Z inputs are even across participating processes. Default is 2024-12-18T01:09:52.9820027Z ``True``. 2024-12-18T01:09:52.9820358Z throw_on_early_termination (bool): Whether to throw an error 2024-12-18T01:09:52.9820842Z or continue training when at least one rank has exhausted 2024-12-18T01:09:52.9821472Z inputs. If ``True``, will throw upon the first rank reaching end 2024-12-18T01:09:52.9821954Z of data. If ``False``, will continue training with a smaller 2024-12-18T01:09:52.9822657Z effective world size until all ranks are joined. Note that if 2024-12-18T01:09:52.9823250Z this flag is specified, then the flag 2024-12-18T01:09:52.9823738Z ``divide_by_initial_world_size`` would be ignored. Default 2024-12-18T01:09:52.9824413Z is ``False``. 2024-12-18T01:09:52.9824686Z 2024-12-18T01:09:52.9824693Z 2024-12-18T01:09:52.9824816Z Example:: 2024-12-18T01:09:52.9824940Z 2024-12-18T01:09:52.9825072Z >>> # xdoctest: +SKIP("Distributed") 2024-12-18T01:09:52.9825382Z >>> import torch 2024-12-18T01:09:52.9825662Z >>> import torch.distributed as dist 2024-12-18T01:09:52.9825990Z >>> import os 2024-12-18T01:09:52.9826267Z >>> import torch.multiprocessing as mp 2024-12-18T01:09:52.9826613Z >>> import torch.nn as nn 2024-12-18T01:09:52.9826909Z >>> # On each spawned worker 2024-12-18T01:09:52.9827207Z >>> def worker(rank): 2024-12-18T01:09:52.9827572Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-12-18T01:09:52.9827983Z >>> torch.cuda.set_device(rank) 2024-12-18T01:09:52.9828420Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:09:52.9828838Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:09:52.9829273Z >>> model, device_ids=[rank], output_device=rank 2024-12-18T01:09:52.9829622Z >>> ) 2024-12-18T01:09:52.9829889Z >>> # Rank 1 gets one more input than rank 0. 2024-12-18T01:09:52.9830322Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-12-18T01:09:52.9830723Z >>> with model.join(): 2024-12-18T01:09:52.9831016Z >>> for _ in range(5): 2024-12-18T01:09:52.9831323Z >>> for inp in inputs: 2024-12-18T01:09:52.9831649Z >>> loss = model(inp).sum() 2024-12-18T01:09:52.9831986Z >>> loss.backward() 2024-12-18T01:09:52.9832374Z >>> # Without the join() API, the below synchronization will hang 2024-12-18T01:09:52.9832827Z >>> # blocking for rank 1's allreduce to complete. 2024-12-18T01:09:52.9833331Z >>> torch.cuda.synchronize(device=rank) 2024-12-18T01:09:52.9833564Z 2024-12-18T01:09:52.9833829Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.9834191Z 2024-12-18T01:09:52.9834939Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-12-18T01:09:52.9836018Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:52.9836578Z 2024-12-18T01:09:52.9836898Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-12-18T01:09:52.9837321Z 2024-12-18T01:09:52.9837735Z Registers an optimizer with DDP such that the optimization for a 2024-12-18T01:09:52.9838261Z parameter will run immediately when that parameter's gradient is 2024-12-18T01:09:52.9838791Z finished with reduction, instead of waiting for all parameters' 2024-12-18T01:09:52.9839340Z gradients to finish reduction. This can result in a training speedup 2024-12-18T01:09:52.9839899Z depending on your workload since the optimizer can run while gradient 2024-12-18T01:09:52.9840470Z reduction for other parameters are still ongoing. In addition, this has 2024-12-18T01:09:52.9841043Z the potential to reduce peak memory consumption during training, as it 2024-12-18T01:09:52.9841575Z only needs to load the per-parameter optimizer states of a single 2024-12-18T01:09:52.9842102Z parameter at a time, instead of loading all per-parameter optimizer 2024-12-18T01:09:52.9842523Z states at once. 2024-12-18T01:09:52.9842661Z 2024-12-18T01:09:52.9842760Z Args: 2024-12-18T01:09:52.9843086Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-12-18T01:09:52.9843489Z as a fused optimizer. 2024-12-18T01:09:52.9843836Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-12-18T01:09:52.9844337Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-12-18T01:09:52.9844897Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-12-18T01:09:52.9845445Z Optimizers. If this is omitted, all DDP model parameters will be 2024-12-18T01:09:52.9845847Z optimized. 2024-12-18T01:09:52.9846191Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-12-18T01:09:52.9846514Z 2024-12-18T01:09:52.9846610Z .. warning :: 2024-12-18T01:09:52.9846961Z _register_fused_optim should only be called once on a DDP instance, 2024-12-18T01:09:52.9847506Z and registering multiple fused optimizers for the same DDP model 2024-12-18T01:09:52.9847963Z is not currently supported. Please ping 2024-12-18T01:09:52.9848428Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:09:52.9848872Z for your use case. 2024-12-18T01:09:52.9849040Z 2024-12-18T01:09:52.9849132Z .. warning :: 2024-12-18T01:09:52.9849463Z _register_fused_optim and register_comm_hook currently do not 2024-12-18T01:09:52.9849991Z compose together, meaning that custom DDP communication hooks are 2024-12-18T01:09:52.9850483Z not supported with overlapped optimizers. Please ping 2024-12-18T01:09:52.9850992Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:09:52.9851436Z for your use case. 2024-12-18T01:09:52.9851604Z 2024-12-18T01:09:52.9851712Z .. warning :: 2024-12-18T01:09:52.9852147Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-12-18T01:09:52.9852619Z with overlapped optimizer. Please ping 2024-12-18T01:09:52.9853071Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:09:52.9853511Z for your use case. 2024-12-18T01:09:52.9853682Z 2024-12-18T01:09:52.9853781Z Example:: 2024-12-18T01:09:52.9853903Z 2024-12-18T01:09:52.9854048Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-12-18T01:09:52.9854588Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-12-18T01:09:52.9855313Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-12-18T01:09:52.9855724Z >>> lr = 1e-2 2024-12-18T01:09:52.9855969Z >>> betas = (0.9, 0.99) 2024-12-18T01:09:52.9856241Z >>> eps = 1e-6 2024-12-18T01:09:52.9856613Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-12-18T01:09:52.9857068Z >>> # Example with subset of parameters 2024-12-18T01:09:52.9857439Z >>> params_to_opt = [list(net.parameters())[0]] 2024-12-18T01:09:52.9857805Z >>> net._register_fused_optim( 2024-12-18T01:09:52.9858246Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-12-18T01:09:52.9858693Z ... ) 2024-12-18T01:09:52.9858812Z 2024-12-18T01:09:52.9859133Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:52.9859500Z 2024-12-18T01:09:53.0088865Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-12-18T01:09:53.0089899Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.0090499Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-12-18T01:09:53.0090840Z 2024-12-18T01:09:53.0091136Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:09:53.0091804Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:09:53.0092457Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:09:53.0093144Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:09:53.0093561Z 2024-12-18T01:09:53.0093707Z .. note:: 2024-12-18T01:09:53.0094263Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-12-18T01:09:53.0095136Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-12-18T01:09:53.0096082Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:09:53.0096787Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:09:53.0097364Z One place we are confident in is that NHWC(channels_last) conversion for 2024-12-18T01:09:53.0097933Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-12-18T01:09:53.0098468Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:09:53.0098801Z 2024-12-18T01:09:53.0099030Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:09:53.0099503Z channels_last. This ensures that; 2024-12-18T01:09:53.0099957Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:09:53.0100534Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:09:53.0101133Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:09:53.0101590Z from memory_format conversion. 2024-12-18T01:09:53.0101818Z 2024-12-18T01:09:53.0102044Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:09:53.0102624Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:09:53.0103213Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:09:53.0103802Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:09:53.0104152Z 2024-12-18T01:09:53.0104387Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:09:53.0104939Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:09:53.0105496Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:09:53.0106245Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:09:53.0106802Z another convolution layer. There's no point in propagating that 2024-12-18T01:09:53.0107351Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:09:53.0107792Z ``memory_format``. 2024-12-18T01:09:53.0107960Z 2024-12-18T01:09:53.0108190Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:09:53.0108848Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:09:53.0109307Z immediately before a convolution. 2024-12-18T01:09:53.0109530Z 2024-12-18T01:09:53.0109634Z Args: 2024-12-18T01:09:53.0110084Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-12-18T01:09:53.0110532Z ``nn.Module`` 2024-12-18T01:09:53.0110888Z memory_format: user specified ``memory_format``, 2024-12-18T01:09:53.0111343Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:09:53.0111655Z 2024-12-18T01:09:53.0111748Z Returns: 2024-12-18T01:09:53.0112033Z The original module with updated ``nn.Conv2d`` 2024-12-18T01:09:53.0112286Z 2024-12-18T01:09:53.0112391Z Example: 2024-12-18T01:09:53.0112654Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:53.0113067Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:09:53.0113567Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:09:53.0114024Z >>> model = nn.Sequential( 2024-12-18T01:09:53.0114342Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-12-18T01:09:53.0114668Z >>> # This is identical to: 2024-12-18T01:09:53.0115117Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:09:53.0115745Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:09:53.0116240Z >>> out = model(input) 2024-12-18T01:09:53.0116517Z 2024-12-18T01:09:53.0116892Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.0117257Z 2024-12-18T01:09:53.0117924Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-12-18T01:09:53.0118917Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.0119514Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-12-18T01:09:53.0120120Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:09:53.0120792Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:09:53.0121440Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:09:53.0122125Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:09:53.0122540Z 2024-12-18T01:09:53.0122635Z .. note:: 2024-12-18T01:09:53.0123020Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-12-18T01:09:53.0123609Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-12-18T01:09:53.0124161Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:09:53.0124714Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:09:53.0125296Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-12-18T01:09:53.0125869Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-12-18T01:09:53.0126413Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:09:53.0126809Z 2024-12-18T01:09:53.0127036Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:09:53.0127506Z channels_last_3d. This ensures that; 2024-12-18T01:09:53.0127957Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:09:53.0128532Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:09:53.0129109Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:09:53.0129575Z from memory_format conversion. 2024-12-18T01:09:53.0129801Z 2024-12-18T01:09:53.0130023Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:09:53.0130604Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:09:53.0131245Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:09:53.0131834Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:09:53.0132190Z 2024-12-18T01:09:53.0132411Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:09:53.0132965Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:09:53.0133522Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:09:53.0134095Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:09:53.0134654Z another convolution layer. There's no point in propagating that 2024-12-18T01:09:53.0135202Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:09:53.0135632Z ``memory_format``. 2024-12-18T01:09:53.0135818Z 2024-12-18T01:09:53.0136279Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:09:53.0136867Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:09:53.0137332Z immediately before a convolution. 2024-12-18T01:09:53.0137557Z 2024-12-18T01:09:53.0137658Z Args: 2024-12-18T01:09:53.0137999Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-12-18T01:09:53.0138426Z ``nn.Module`` 2024-12-18T01:09:53.0138793Z memory_format: user specified ``memory_format``, 2024-12-18T01:09:53.0139238Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:09:53.0139530Z 2024-12-18T01:09:53.0139632Z Returns: 2024-12-18T01:09:53.0139910Z The original module with updated ``nn.Conv3d`` 2024-12-18T01:09:53.0140163Z 2024-12-18T01:09:53.0140264Z Example: 2024-12-18T01:09:53.0140527Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:53.0140945Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:09:53.0141450Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:09:53.0141913Z >>> model = nn.Sequential( 2024-12-18T01:09:53.0142237Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-12-18T01:09:53.0142562Z >>> # This is identical to: 2024-12-18T01:09:53.0143023Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:09:53.0143672Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:09:53.0144172Z >>> out = model(input) 2024-12-18T01:09:53.0144453Z 2024-12-18T01:09:53.0144822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.0145254Z 2024-12-18T01:09:53.0312484Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936. 2024-12-18T01:09:53.0325876Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.0326515Z Prune tensor by removing random channels along the specified dimension. 2024-12-18T01:09:53.0327107Z 2024-12-18T01:09:53.0327405Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:09:53.0327984Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:09:53.0328572Z along the specified ``dim`` selected at random. 2024-12-18T01:09:53.0329084Z Modifies module in place (and also return the modified module) 2024-12-18T01:09:53.0329544Z by: 2024-12-18T01:09:53.0329676Z 2024-12-18T01:09:53.0329889Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:53.0330448Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:53.0331003Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:53.0331665Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:53.0332088Z ``name+'_orig'``. 2024-12-18T01:09:53.0332268Z 2024-12-18T01:09:53.0332361Z Args: 2024-12-18T01:09:53.0332675Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:09:53.0333164Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:09:53.0333568Z will act. 2024-12-18T01:09:53.0333914Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:09:53.0334372Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:09:53.0334875Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:09:53.0335330Z absolute number of parameters to prune. 2024-12-18T01:09:53.0335784Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:09:53.0336285Z 2024-12-18T01:09:53.0336394Z Returns: 2024-12-18T01:09:53.0336749Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:09:53.0337100Z 2024-12-18T01:09:53.0337195Z Examples: 2024-12-18T01:09:53.0337442Z >>> # xdoctest: +SKIP 2024-12-18T01:09:53.0337753Z >>> m = prune.random_structured( 2024-12-18T01:09:53.0338117Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-12-18T01:09:53.0338460Z ... ) 2024-12-18T01:09:53.0338765Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-12-18T01:09:53.0339169Z >>> print(columns_pruned) 2024-12-18T01:09:53.0339457Z 3 2024-12-18T01:09:53.0339669Z 2024-12-18T01:09:53.0340026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.0340403Z 2024-12-18T01:09:53.0340904Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977. 2024-12-18T01:09:53.0341782Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.0342469Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-12-18T01:09:53.0342904Z 2024-12-18T01:09:53.0343138Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:09:53.0343706Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:09:53.0344221Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-12-18T01:09:53.0344704Z Modifies module in place (and also return the modified module) 2024-12-18T01:09:53.0345111Z by: 2024-12-18T01:09:53.0345243Z 2024-12-18T01:09:53.0345448Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:53.0345996Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:53.0346548Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:53.0347094Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:53.0347509Z ``name+'_orig'``. 2024-12-18T01:09:53.0347686Z 2024-12-18T01:09:53.0347775Z Args: 2024-12-18T01:09:53.0348211Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:09:53.0348787Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:09:53.0349192Z will act. 2024-12-18T01:09:53.0349524Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:09:53.0349996Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:09:53.0350498Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:09:53.0350963Z absolute number of parameters to prune. 2024-12-18T01:09:53.0351412Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-12-18T01:09:53.0351887Z entries for argument ``p`` in :func:`torch.norm`. 2024-12-18T01:09:53.0352429Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:09:53.0352993Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-12-18T01:09:53.0353548Z shape as module parameter) used to compute mask for pruning. 2024-12-18T01:09:53.0354085Z The values in this tensor indicate the importance of the corresponding 2024-12-18T01:09:53.0354566Z elements in the parameter being pruned. 2024-12-18T01:09:53.0355029Z If unspecified or None, the module parameter will be used in its place. 2024-12-18T01:09:53.0355386Z 2024-12-18T01:09:53.0355477Z Returns: 2024-12-18T01:09:53.0355831Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:09:53.0356182Z 2024-12-18T01:09:53.0356275Z Examples: 2024-12-18T01:09:53.0356540Z >>> from torch.nn.utils import prune 2024-12-18T01:09:53.0356887Z >>> m = prune.ln_structured( 2024-12-18T01:09:53.0357275Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-12-18T01:09:53.0357673Z ... ) 2024-12-18T01:09:53.0357895Z 2024-12-18T01:09:53.0358276Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.0358640Z 2024-12-18T01:09:53.0359190Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024. 2024-12-18T01:09:53.0360098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.0360471Z 2024-12-18T01:09:53.0360893Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-12-18T01:09:53.0361438Z 2024-12-18T01:09:53.0361551Z Modifies modules in place by: 2024-12-18T01:09:53.0361756Z 2024-12-18T01:09:53.0361965Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:53.0362504Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:53.0363048Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:53.0363585Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:53.0363996Z ``name+'_orig'``. 2024-12-18T01:09:53.0364162Z 2024-12-18T01:09:53.0364250Z Args: 2024-12-18T01:09:53.0364574Z parameters (Iterable of (module, name) tuples): parameters of 2024-12-18T01:09:53.0365084Z the model to prune in a global fashion, i.e. by aggregating all 2024-12-18T01:09:53.0365610Z weights prior to deciding which ones to prune. module must be of 2024-12-18T01:09:53.0366081Z type :class:`nn.Module`, and name must be a string. 2024-12-18T01:09:53.0366575Z pruning_method (function): a valid pruning function from this module, 2024-12-18T01:09:53.0367091Z or a custom one implemented by the user that satisfies the 2024-12-18T01:09:53.0367618Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-12-18T01:09:53.0368186Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-12-18T01:09:53.0368760Z the corresponding parameter's importance scores tensor. The tensor 2024-12-18T01:09:53.0369384Z should be the same shape as the parameter, and is used for computing 2024-12-18T01:09:53.0369823Z mask for pruning. 2024-12-18T01:09:53.0370211Z If unspecified or None, the parameter will be used in place of its 2024-12-18T01:09:53.0370629Z importance scores. 2024-12-18T01:09:53.0370934Z kwargs: other keyword arguments such as: 2024-12-18T01:09:53.0371372Z amount (int or float): quantity of parameters to prune across the 2024-12-18T01:09:53.0371798Z specified parameters. 2024-12-18T01:09:53.0372168Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:09:53.0372650Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:09:53.0373152Z absolute number of parameters to prune. 2024-12-18T01:09:53.0373400Z 2024-12-18T01:09:53.0373487Z Raises: 2024-12-18T01:09:53.0373764Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-12-18T01:09:53.0374024Z 2024-12-18T01:09:53.0374122Z Note: 2024-12-18T01:09:53.0374462Z Since global structured pruning doesn't make much sense unless the 2024-12-18T01:09:53.0374987Z norm is normalized by the size of the parameter, we now limit the 2024-12-18T01:09:53.0375463Z scope of global pruning to unstructured methods. 2024-12-18T01:09:53.0375736Z 2024-12-18T01:09:53.0375825Z Examples: 2024-12-18T01:09:53.0376073Z >>> from torch.nn.utils import prune 2024-12-18T01:09:53.0376423Z >>> from collections import OrderedDict 2024-12-18T01:09:53.0376758Z >>> net = nn.Sequential(OrderedDict([ 2024-12-18T01:09:53.0377089Z ... ('first', nn.Linear(10, 4)), 2024-12-18T01:09:53.0377412Z ... ('second', nn.Linear(4, 1)), 2024-12-18T01:09:53.0377714Z ... ])) 2024-12-18T01:09:53.0377951Z >>> parameters_to_prune = ( 2024-12-18T01:09:53.0378236Z ... (net.first, 'weight'), 2024-12-18T01:09:53.0378540Z ... (net.second, 'weight'), 2024-12-18T01:09:53.0378833Z ... ) 2024-12-18T01:09:53.0379067Z >>> prune.global_unstructured( 2024-12-18T01:09:53.0379383Z ... parameters_to_prune, 2024-12-18T01:09:53.0379702Z ... pruning_method=prune.L1Unstructured, 2024-12-18T01:09:53.0380042Z ... amount=10, 2024-12-18T01:09:53.0380287Z ... ) 2024-12-18T01:09:53.0380625Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-12-18T01:09:53.0381055Z tensor(10) 2024-12-18T01:09:53.0381187Z 2024-12-18T01:09:53.0381191Z 2024-12-18T01:09:53.0381441Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.0381819Z 2024-12-18T01:09:53.0382356Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143. 2024-12-18T01:09:53.0383243Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.0384006Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-12-18T01:09:53.0384522Z 2024-12-18T01:09:53.0384737Z Modifies module in place (and also return the modified module) by: 2024-12-18T01:09:53.0385060Z 2024-12-18T01:09:53.0385283Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:53.0385835Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:53.0386382Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:53.0386913Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:53.0387334Z ``name+'_orig'``. 2024-12-18T01:09:53.0387510Z 2024-12-18T01:09:53.0387596Z Args: 2024-12-18T01:09:53.0387907Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:09:53.0388483Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:09:53.0388869Z will act. 2024-12-18T01:09:53.0389284Z mask (Tensor): binary mask to be applied to the parameter. 2024-12-18T01:09:53.0389589Z 2024-12-18T01:09:53.0389680Z Returns: 2024-12-18T01:09:53.0390039Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:09:53.0390374Z 2024-12-18T01:09:53.0390478Z Examples: 2024-12-18T01:09:53.0390738Z >>> from torch.nn.utils import prune 2024-12-18T01:09:53.0391073Z >>> m = prune.custom_from_mask( 2024-12-18T01:09:53.0391465Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-12-18T01:09:53.0391842Z ... ) 2024-12-18T01:09:53.0392080Z >>> print(m.bias_mask) 2024-12-18T01:09:53.0392366Z tensor([0., 1., 0.]) 2024-12-18T01:09:53.0392540Z 2024-12-18T01:09:53.0392623Z 2024-12-18T01:09:53.0393050Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.0393428Z 2024-12-18T01:09:53.1448361Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=116. 2024-12-18T01:09:53.1449341Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.1450087Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-12-18T01:09:53.1450575Z 2024-12-18T01:09:53.1450820Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-12-18T01:09:53.1451407Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-12-18T01:09:53.1451960Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-12-18T01:09:53.1452376Z (UAI 2018). 2024-12-18T01:09:53.1452526Z 2024-12-18T01:09:53.1452756Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-12-18T01:09:53.1453329Z but using exponential weights instead of equal weights across iterations. 2024-12-18T01:09:53.1453694Z 2024-12-18T01:09:53.1453931Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-12-18T01:09:53.1454513Z on the device :attr:`device` and allows to compute running averages of the 2024-12-18T01:09:53.1454971Z parameters of the :attr:`model`. 2024-12-18T01:09:53.1455180Z 2024-12-18T01:09:53.1455280Z Args: 2024-12-18T01:09:53.1455567Z model (torch.nn.Module): model to use with SWA/EMA 2024-12-18T01:09:53.1456082Z device (torch.device, optional): if provided, the averaged model will be 2024-12-18T01:09:53.1456555Z stored on the :attr:`device` 2024-12-18T01:09:53.1456972Z avg_fn (function, optional): the averaging function used to update 2024-12-18T01:09:53.1457674Z parameters; the function must take in the current value of the 2024-12-18T01:09:53.1458284Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-12-18T01:09:53.1458937Z parameter, and the number of models already averaged; if None, 2024-12-18T01:09:53.1459424Z an equally weighted average is used (default: None) 2024-12-18T01:09:53.1460009Z multi_avg_fn (function, optional): the averaging function used to update 2024-12-18T01:09:53.1460639Z parameters inplace; the function must take in the current values of the 2024-12-18T01:09:53.1461331Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-12-18T01:09:53.1462020Z parameters as a list, and the number of models already averaged; if None, 2024-12-18T01:09:53.1462540Z an equally weighted average is used (default: None) 2024-12-18T01:09:53.1463083Z use_buffers (bool): if ``True``, it will compute running averages for 2024-12-18T01:09:53.1463688Z both the parameters and the buffers of the model. (default: ``False``) 2024-12-18T01:09:53.1464032Z 2024-12-18T01:09:53.1464130Z Example: 2024-12-18T01:09:53.1464446Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:53.1464825Z >>> loader, optimizer, model, loss_fn = ... 2024-12-18T01:09:53.1465564Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-12-18T01:09:53.1466137Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-12-18T01:09:53.1466639Z >>> T_max=300) 2024-12-18T01:09:53.1466972Z >>> swa_start = 160 2024-12-18T01:09:53.1467351Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-12-18T01:09:53.1467715Z >>> for i in range(300): 2024-12-18T01:09:53.1468070Z >>> for input, target in loader: 2024-12-18T01:09:53.1468499Z >>> optimizer.zero_grad() 2024-12-18T01:09:53.1468914Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:09:53.1469405Z >>> optimizer.step() 2024-12-18T01:09:53.1469780Z >>> if i > swa_start: 2024-12-18T01:09:53.1470116Z >>> swa_model.update_parameters(model) 2024-12-18T01:09:53.1470518Z >>> swa_scheduler.step() 2024-12-18T01:09:53.1470851Z >>> else: 2024-12-18T01:09:53.1471170Z >>> scheduler.step() 2024-12-18T01:09:53.1471478Z >>> 2024-12-18T01:09:53.1471770Z >>> # Update bn statistics for the swa_model at the end 2024-12-18T01:09:53.1472246Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-12-18T01:09:53.1472543Z 2024-12-18T01:09:53.1472889Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-12-18T01:09:53.1473554Z If no averaging function is provided, the default is to compute 2024-12-18T01:09:53.1474018Z equally-weighted average of the weights (SWA). 2024-12-18T01:09:53.1474330Z 2024-12-18T01:09:53.1474432Z Example: 2024-12-18T01:09:53.1474709Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:53.1475208Z >>> # Compute exponential moving averages of the weights and buffers 2024-12-18T01:09:53.1475763Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-12-18T01:09:53.1476275Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-12-18T01:09:53.1476675Z 2024-12-18T01:09:53.1476786Z .. note:: 2024-12-18T01:09:53.1477169Z When using SWA/EMA with models containing Batch Normalization you may 2024-12-18T01:09:53.1477745Z need to update the activation statistics for Batch Normalization. 2024-12-18T01:09:53.1478347Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-12-18T01:09:53.1478983Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-12-18T01:09:53.1479630Z statistics in a post-training step by passing data through the model. The 2024-12-18T01:09:53.1480284Z second does it during the parameter update phase by averaging all buffers. 2024-12-18T01:09:53.1480893Z Empirical evidence has shown that updating the statistics in normalization 2024-12-18T01:09:53.1481554Z layers increases accuracy, but you may wish to empirically test which 2024-12-18T01:09:53.1482103Z approach yields the best results in your problem. 2024-12-18T01:09:53.1482381Z 2024-12-18T01:09:53.1482496Z .. note:: 2024-12-18T01:09:53.1482916Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-12-18T01:09:53.1483348Z 2024-12-18T01:09:53.1483439Z .. note:: 2024-12-18T01:09:53.1483774Z When :meth:`update_parameters` is called for the first time (i.e. 2024-12-18T01:09:53.1484342Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-12-18T01:09:53.1484894Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-12-18T01:09:53.1485409Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-12-18T01:09:53.1485880Z to update the parameters. 2024-12-18T01:09:53.1486079Z 2024-12-18T01:09:53.1486350Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:09:53.1486933Z https://arxiv.org/abs/1803.05407 2024-12-18T01:09:53.1487458Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-12-18T01:09:53.1487945Z Average: 2024-12-18T01:09:53.1488211Z https://arxiv.org/abs/1806.05594 2024-12-18T01:09:53.1488688Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-12-18T01:09:53.1489127Z https://arxiv.org/abs/1904.11943 2024-12-18T01:09:53.1489625Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-12-18T01:09:53.1490058Z Generalizes Well: 2024-12-18T01:09:53.1490401Z https://arxiv.org/abs/2001.02312 2024-12-18T01:09:53.1490727Z .. _Polyak averaging: 2024-12-18T01:09:53.1491187Z https://paperswithcode.com/method/polyak-averaging 2024-12-18T01:09:53.1491559Z 2024-12-18T01:09:53.1491967Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.1492350Z 2024-12-18T01:09:53.1492899Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=368. 2024-12-18T01:09:53.1493808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.1494460Z Anneals the learning rate in each parameter group to a fixed value. 2024-12-18T01:09:53.1494856Z 2024-12-18T01:09:53.1495091Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-12-18T01:09:53.1495708Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-12-18T01:09:53.1496035Z 2024-12-18T01:09:53.1496138Z Args: 2024-12-18T01:09:53.1496479Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-12-18T01:09:53.1496981Z swa_lrs (float or list): the learning rate value for all param groups 2024-12-18T01:09:53.1497501Z together or separately for each group. 2024-12-18T01:09:53.1497953Z annealing_epochs (int): number of epochs in the annealing phase 2024-12-18T01:09:53.1498377Z (default: 10) 2024-12-18T01:09:53.1498803Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-12-18T01:09:53.1499353Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-12-18T01:09:53.1499786Z (default: "cos") 2024-12-18T01:09:53.1500156Z last_epoch (int): the index of the last epoch (default: -1) 2024-12-18T01:09:53.1500446Z 2024-12-18T01:09:53.1500643Z The :class:`SWALR` scheduler can be used together with other 2024-12-18T01:09:53.1501221Z schedulers to switch to a constant learning rate late in the training 2024-12-18T01:09:53.1501655Z as in the example below. 2024-12-18T01:09:53.1501852Z 2024-12-18T01:09:53.1501943Z Example: 2024-12-18T01:09:53.1502221Z >>> # xdoctest: +SKIP("Undefined variables") 2024-12-18T01:09:53.1502591Z >>> loader, optimizer, model = ... 2024-12-18T01:09:53.1502937Z >>> lr_lambda = lambda epoch: 0.9 2024-12-18T01:09:53.1503376Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-12-18T01:09:53.1503827Z >>> lr_lambda=lr_lambda) 2024-12-18T01:09:53.1504217Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-12-18T01:09:53.1504736Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-12-18T01:09:53.1505142Z >>> swa_start = 160 2024-12-18T01:09:53.1505427Z >>> for i in range(300): 2024-12-18T01:09:53.1505726Z >>> for input, target in loader: 2024-12-18T01:09:53.1506067Z >>> optimizer.zero_grad() 2024-12-18T01:09:53.1506430Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:09:53.1506795Z >>> optimizer.step() 2024-12-18T01:09:53.1507121Z >>> if i > swa_start: 2024-12-18T01:09:53.1507420Z >>> swa_scheduler.step() 2024-12-18T01:09:53.1507734Z >>> else: 2024-12-18T01:09:53.1508087Z >>> scheduler.step() 2024-12-18T01:09:53.1508368Z 2024-12-18T01:09:53.1508606Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:09:53.1508726Z https://arxiv.org/abs/1803.05407 2024-12-18T01:09:53.1508824Z 2024-12-18T01:09:53.1509074Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.1509078Z 2024-12-18T01:09:53.6327560Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274. 2024-12-18T01:09:53.6328885Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:53.6329404Z Asserts that ``actual`` and ``expected`` are close. 2024-12-18T01:09:53.6329676Z 2024-12-18T01:09:53.6330338Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-12-18T01:09:53.6330878Z 2024-12-18T01:09:53.6331017Z .. math:: 2024-12-18T01:09:53.6331149Z 2024-12-18T01:09:53.6331532Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-12-18T01:09:53.6332012Z 2024-12-18T01:09:53.6332370Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-12-18T01:09:53.6333024Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-12-18T01:09:53.6333353Z 2024-12-18T01:09:53.6333552Z In addition, they are only considered close if they have the same 2024-12-18T01:09:53.6333877Z 2024-12-18T01:09:53.6334074Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-12-18T01:09:53.6334520Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-12-18T01:09:53.6334917Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-12-18T01:09:53.6335306Z - stride (if ``check_stride`` is ``True``). 2024-12-18T01:09:53.6335538Z 2024-12-18T01:09:53.6335843Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-12-18T01:09:53.6336457Z 2024-12-18T01:09:53.6336821Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-12-18T01:09:53.6337673Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-12-18T01:09:53.6338396Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-12-18T01:09:53.6339128Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-12-18T01:09:53.6339627Z 2024-12-18T01:09:53.6339926Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-12-18T01:09:53.6340681Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-12-18T01:09:53.6341248Z definition above. 2024-12-18T01:09:53.6341418Z 2024-12-18T01:09:53.6341721Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-12-18T01:09:53.6342522Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-12-18T01:09:53.6343376Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-12-18T01:09:53.6344227Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-12-18T01:09:53.6344947Z their elements are considered close according to the above definition. 2024-12-18T01:09:53.6345289Z 2024-12-18T01:09:53.6345394Z .. note:: 2024-12-18T01:09:53.6345529Z 2024-12-18T01:09:53.6345861Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-12-18T01:09:53.6346638Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-12-18T01:09:53.6347496Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-12-18T01:09:53.6347900Z 2024-12-18T01:09:53.6347989Z Args: 2024-12-18T01:09:53.6348229Z actual (Any): Actual input. 2024-12-18T01:09:53.6348641Z expected (Any): Expected input. 2024-12-18T01:09:53.6349216Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-12-18T01:09:53.6349868Z are allowed. Otherwise type equality is required. 2024-12-18T01:09:53.6350514Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-12-18T01:09:53.6351348Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:09:53.6352095Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-12-18T01:09:53.6352850Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:09:53.6353467Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-12-18T01:09:53.6354130Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-12-18T01:09:53.6354787Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-12-18T01:09:53.6355393Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-12-18T01:09:53.6356104Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-12-18T01:09:53.6356920Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-12-18T01:09:53.6357533Z :func:`torch.promote_types`) before being compared. 2024-12-18T01:09:53.6358173Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-12-18T01:09:53.6358982Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-12-18T01:09:53.6359524Z compared. 2024-12-18T01:09:53.6360043Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-12-18T01:09:53.6360875Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-12-18T01:09:53.6361712Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-12-18T01:09:53.6362312Z should return the new message. 2024-12-18T01:09:53.6362535Z 2024-12-18T01:09:53.6362639Z Raises: 2024-12-18T01:09:53.6362992Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-12-18T01:09:53.6363521Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-12-18T01:09:53.6364133Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-12-18T01:09:53.6364945Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-12-18T01:09:53.6365542Z different types. 2024-12-18T01:09:53.6366081Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-12-18T01:09:53.6366937Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-12-18T01:09:53.6367747Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-12-18T01:09:53.6368487Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-12-18T01:09:53.6369091Z :attr:`~torch.Tensor.layout`. 2024-12-18T01:09:53.6369544Z AssertionError: If only one of corresponding tensors is quantized. 2024-12-18T01:09:53.6370257Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-12-18T01:09:53.6371060Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-12-18T01:09:53.6371600Z :attr:`~torch.Tensor.device`. 2024-12-18T01:09:53.6372163Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-12-18T01:09:53.6372978Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-12-18T01:09:53.6373880Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-12-18T01:09:53.6374352Z 2024-12-18T01:09:53.6374724Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-12-18T01:09:53.6375348Z ``dtype``'s, the maximum of both tolerances is used. 2024-12-18T01:09:53.6375617Z 2024-12-18T01:09:53.6375748Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6376125Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-12-18T01:09:53.6376482Z +===========================+============+==========+ 2024-12-18T01:09:53.6376850Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:09:53.6377226Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6377594Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-12-18T01:09:53.6377970Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6378356Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6378737Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6379117Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:09:53.6379481Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6379860Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:09:53.6380238Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6380619Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6380998Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6381382Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:09:53.6381749Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6382123Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6382500Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6382877Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6383256Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6383628Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6384005Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6384381Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6384757Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6385134Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:53.6385497Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6385861Z | other | ``0.0`` | ``0.0`` | 2024-12-18T01:09:53.6386224Z +---------------------------+------------+----------+ 2024-12-18T01:09:53.6386470Z 2024-12-18T01:09:53.6386567Z .. note:: 2024-12-18T01:09:53.6386694Z 2024-12-18T01:09:53.6387091Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-12-18T01:09:53.6387939Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-12-18T01:09:53.6388844Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-12-18T01:09:53.6389219Z 2024-12-18T01:09:53.6389321Z >>> import functools 2024-12-18T01:09:53.6389767Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-12-18T01:09:53.6390258Z >>> assert_equal(1e-9, 1e-10) 2024-12-18T01:09:53.6390588Z Traceback (most recent call last): 2024-12-18T01:09:53.6390904Z ... 2024-12-18T01:09:53.6391146Z AssertionError: Scalars are not equal! 2024-12-18T01:09:53.6391477Z 2024-12-18T01:09:53.6391738Z Expected 1e-10 but got 1e-09. 2024-12-18T01:09:53.6392137Z Absolute difference: 9.000000000000001e-10 2024-12-18T01:09:53.6392488Z Relative difference: 9.0 2024-12-18T01:09:53.6392680Z 2024-12-18T01:09:53.6392773Z Examples: 2024-12-18T01:09:53.6393023Z >>> # tensor to tensor comparison 2024-12-18T01:09:53.6393390Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-12-18T01:09:53.6393770Z >>> actual = torch.acos(torch.cos(expected)) 2024-12-18T01:09:53.6394164Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:53.6394418Z 2024-12-18T01:09:53.6394542Z >>> # scalar to scalar comparison 2024-12-18T01:09:53.6394845Z >>> import math 2024-12-18T01:09:53.6395115Z >>> expected = math.sqrt(2.0) 2024-12-18T01:09:53.6395433Z >>> actual = 2.0 / math.sqrt(2.0) 2024-12-18T01:09:53.6395797Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:53.6396050Z 2024-12-18T01:09:53.6396189Z >>> # numpy array to numpy array comparison 2024-12-18T01:09:53.6396526Z >>> import numpy as np 2024-12-18T01:09:53.6396837Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-12-18T01:09:53.6397201Z >>> actual = np.arccos(np.cos(expected)) 2024-12-18T01:09:53.6397585Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:53.6397841Z 2024-12-18T01:09:53.6397973Z >>> # sequence to sequence comparison 2024-12-18T01:09:53.6398299Z >>> import numpy as np 2024-12-18T01:09:53.6398746Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-12-18T01:09:53.6399254Z >>> # length and their elements have to match. 2024-12-18T01:09:53.6399662Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-12-18T01:09:53.6400042Z >>> actual = tuple(expected) 2024-12-18T01:09:53.6400403Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:53.6400656Z 2024-12-18T01:09:53.6400774Z >>> # mapping to mapping comparison 2024-12-18T01:09:53.6401129Z >>> from collections import OrderedDict 2024-12-18T01:09:53.6401468Z >>> import numpy as np 2024-12-18T01:09:53.6401763Z >>> foo = torch.tensor(1.0) 2024-12-18T01:09:53.6402062Z >>> bar = 2.0 2024-12-18T01:09:53.6402317Z >>> baz = np.array(3.0) 2024-12-18T01:09:53.6402768Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-12-18T01:09:53.6403347Z >>> # have to have the same set of keys and their elements have to match. 2024-12-18T01:09:53.6403887Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-12-18T01:09:53.6404353Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-12-18T01:09:53.6404741Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:53.6405013Z 2024-12-18T01:09:53.6405143Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:09:53.6405500Z >>> actual = expected.clone() 2024-12-18T01:09:53.6405891Z >>> # By default, directly related instances can be compared 2024-12-18T01:09:53.6406404Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-12-18T01:09:53.6406944Z >>> # This check can be made more strict with allow_subclasses=False 2024-12-18T01:09:53.6407458Z >>> torch.testing.assert_close( 2024-12-18T01:09:53.6407891Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-12-18T01:09:53.6408306Z ... ) 2024-12-18T01:09:53.6408565Z Traceback (most recent call last): 2024-12-18T01:09:53.6408889Z ... 2024-12-18T01:09:53.6409214Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:09:53.6409762Z and . 2024-12-18T01:09:53.6410325Z >>> # If the inputs are not directly related, they are never considered close 2024-12-18T01:09:53.6410849Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-12-18T01:09:53.6411313Z Traceback (most recent call last): 2024-12-18T01:09:53.6411628Z ... 2024-12-18T01:09:53.6412033Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:09:53.6412549Z and . 2024-12-18T01:09:53.6413022Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-12-18T01:09:53.6413528Z >>> # their type if check_dtype=False. 2024-12-18T01:09:53.6413933Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-12-18T01:09:53.6414213Z 2024-12-18T01:09:53.6414316Z >>> # NaN != NaN by default. 2024-12-18T01:09:53.6414644Z >>> expected = torch.tensor(float("Nan")) 2024-12-18T01:09:53.6414994Z >>> actual = expected.clone() 2024-12-18T01:09:53.6415357Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:53.6415735Z Traceback (most recent call last): 2024-12-18T01:09:53.6416036Z ... 2024-12-18T01:09:53.6416300Z AssertionError: Scalars are not close! 2024-12-18T01:09:53.6416635Z 2024-12-18T01:09:53.6416893Z Expected nan but got nan. 2024-12-18T01:09:53.6417239Z Absolute difference: nan (up to 1e-05 allowed) 2024-12-18T01:09:53.6417638Z Relative difference: nan (up to 1.3e-06 allowed) 2024-12-18T01:09:53.6418096Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-12-18T01:09:53.6418418Z 2024-12-18T01:09:53.6418543Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:09:53.6418905Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-12-18T01:09:53.6419285Z >>> # The default error message can be overwritten. 2024-12-18T01:09:53.6419834Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-12-18T01:09:53.6420349Z Traceback (most recent call last): 2024-12-18T01:09:53.6420664Z ... 2024-12-18T01:09:53.6420952Z AssertionError: Argh, the tensors are not close! 2024-12-18T01:09:53.6421442Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-12-18T01:09:53.6421891Z >>> # extra information 2024-12-18T01:09:53.6422186Z >>> torch.testing.assert_close( 2024-12-18T01:09:53.6422610Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-12-18T01:09:53.6423024Z ... ) 2024-12-18T01:09:53.6423274Z Traceback (most recent call last): 2024-12-18T01:09:53.6423589Z ... 2024-12-18T01:09:53.6423814Z AssertionError: Header 2024-12-18T01:09:53.6424097Z 2024-12-18T01:09:53.6424352Z Tensor-likes are not close! 2024-12-18T01:09:53.6424653Z 2024-12-18T01:09:53.6424913Z Mismatched elements: 2 / 3 (66.7%) 2024-12-18T01:09:53.6425349Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-12-18T01:09:53.6425926Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-12-18T01:09:53.6426369Z 2024-12-18T01:09:53.6426605Z Footer 2024-12-18T01:09:53.6426828Z 2024-12-18T01:09:53.6427186Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:53.6427635Z 2024-12-18T01:09:54.7652559Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=110. 2024-12-18T01:09:54.7653530Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.7654065Z Register a container-like type as pytree node. 2024-12-18T01:09:54.7654319Z 2024-12-18T01:09:54.7654424Z Args: 2024-12-18T01:09:54.7654732Z cls (type): A Python type to treat as an internal pytree node. 2024-12-18T01:09:54.7655312Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-12-18T01:09:54.7656249Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-12-18T01:09:54.7656915Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-12-18T01:09:54.7657484Z passed to the ``unflatten_fn``. 2024-12-18T01:09:54.7657996Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-12-18T01:09:54.7658658Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-12-18T01:09:54.7659190Z The function should return an instance of ``cls``. 2024-12-18T01:09:54.7659736Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-12-18T01:09:54.7660296Z qualified name used when serializing the tree spec. 2024-12-18T01:09:54.7660892Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-12-18T01:09:54.7661610Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-12-18T01:09:54.7662287Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-12-18T01:09:54.7662973Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-12-18T01:09:54.7663675Z how to convert the custom json dumpable representation of the context back to the 2024-12-18T01:09:54.7664324Z original context. This is used for json deserialization, which is being used in 2024-12-18T01:09:54.7664830Z :mod:`torch.export` right now. 2024-12-18T01:09:54.7665054Z 2024-12-18T01:09:54.7665183Z Example:: 2024-12-18T01:09:54.7665317Z 2024-12-18T01:09:54.7665434Z >>> # xdoctest: +SKIP 2024-12-18T01:09:54.7665758Z >>> # Registry a Python type with lambda functions 2024-12-18T01:09:54.7666144Z >>> register_pytree_node( 2024-12-18T01:09:54.7666439Z ... set, 2024-12-18T01:09:54.7666726Z ... lambda s: (sorted(s), None, None), 2024-12-18T01:09:54.7667093Z ... lambda children, _: set(children), 2024-12-18T01:09:54.7667407Z ... ) 2024-12-18T01:09:54.7667627Z 2024-12-18T01:09:54.7668008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.7668454Z 2024-12-18T01:09:54.8164982Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-12-18T01:09:54.8166108Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.8166486Z 2024-12-18T01:09:54.8166740Z Context passed to policy function during selective checkpointing. 2024-12-18T01:09:54.8167086Z 2024-12-18T01:09:54.8167325Z This class is used to pass relevant metadata to the policy function during 2024-12-18T01:09:54.8167981Z selective checkpointing. The metadata includes whether the current invocation 2024-12-18T01:09:54.8168608Z of the policy function is during recomputation or not. 2024-12-18T01:09:54.8168890Z 2024-12-18T01:09:54.8168996Z Example: 2024-12-18T01:09:54.8169264Z >>> # xdoctest: +SKIP(stub) 2024-12-18T01:09:54.8169615Z >>> 2024-12-18T01:09:54.8170188Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:09:54.8170548Z >>> print(ctx.is_recompute) 2024-12-18T01:09:54.8170930Z >>> 2024-12-18T01:09:54.8171323Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:09:54.8171867Z >>> 2024-12-18T01:09:54.8172116Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:09:54.8172520Z >>> fn, x, y, 2024-12-18T01:09:54.8172779Z >>> use_reentrant=False, 2024-12-18T01:09:54.8173126Z >>> context_fn=context_fn, 2024-12-18T01:09:54.8173415Z >>> ) 2024-12-18T01:09:54.8173533Z 2024-12-18T01:09:54.8173840Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.8174219Z 2024-12-18T01:09:54.8175126Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335. 2024-12-18T01:09:54.8176233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.8176629Z 2024-12-18T01:09:54.8176924Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-12-18T01:09:54.8177290Z 2024-12-18T01:09:54.8177526Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-12-18T01:09:54.8178051Z operations are recomputed during the backward pass. 2024-12-18T01:09:54.8178367Z 2024-12-18T01:09:54.8178469Z Args: 2024-12-18T01:09:54.8178710Z policy_fn_or_list (Callable or List): 2024-12-18T01:09:54.8179147Z - If a policy function is provided, it should accept a 2024-12-18T01:09:54.8179681Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-12-18T01:09:54.8180314Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-12-18T01:09:54.8180931Z indicating whether the execution of the op should be recomputed or not. 2024-12-18T01:09:54.8181542Z - If a list of operations is provided, it is equivalent to a policy 2024-12-18T01:09:54.8182061Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-12-18T01:09:54.8182623Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-12-18T01:09:54.8183113Z operations. 2024-12-18T01:09:54.8183480Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-12-18T01:09:54.8184087Z raised if any tensors cached by selective activation checkpoint are 2024-12-18T01:09:54.8184678Z mutated in order to ensure correctness. If set to `True`, this check 2024-12-18T01:09:54.8185093Z is disabled. 2024-12-18T01:09:54.8185396Z Returns: 2024-12-18T01:09:54.8185633Z A tuple of two context managers. 2024-12-18T01:09:54.8185841Z 2024-12-18T01:09:54.8185980Z Example: 2024-12-18T01:09:54.8186229Z >>> # xdoctest: +REQUIRES(LINUX) 2024-12-18T01:09:54.8186524Z >>> import functools 2024-12-18T01:09:54.8186825Z >>> 2024-12-18T01:09:54.8187076Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:09:54.8187467Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:09:54.8187817Z >>> 2024-12-18T01:09:54.8188021Z >>> ops_to_save = [ 2024-12-18T01:09:54.8188409Z >>> torch.ops.aten.mm.default, 2024-12-18T01:09:54.8188719Z >>> ] 2024-12-18T01:09:54.8188936Z >>> 2024-12-18T01:09:54.8189238Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:09:54.8189568Z >>> if op in ops_to_save: 2024-12-18T01:09:54.8189940Z >>> return CheckpointPolicy.MUST_SAVE 2024-12-18T01:09:54.8190275Z >>> else: 2024-12-18T01:09:54.8190610Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-12-18T01:09:54.8190957Z >>> 2024-12-18T01:09:54.8191392Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:09:54.8191875Z >>> 2024-12-18T01:09:54.8192148Z >>> # or equivalently 2024-12-18T01:09:54.8192595Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-12-18T01:09:54.8193238Z >>> 2024-12-18T01:09:54.8193441Z >>> def fn(x, y): 2024-12-18T01:09:54.8193855Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-12-18T01:09:54.8194264Z >>> 2024-12-18T01:09:54.8194583Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:09:54.8194928Z >>> fn, x, y, 2024-12-18T01:09:54.8195228Z >>> use_reentrant=False, 2024-12-18T01:09:54.8195536Z >>> context_fn=context_fn, 2024-12-18T01:09:54.8195835Z >>> ) 2024-12-18T01:09:54.8196002Z 2024-12-18T01:09:54.8196268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.8196661Z 2024-12-18T01:09:54.8384206Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=957. 2024-12-18T01:09:54.8385205Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.8385648Z 2024-12-18T01:09:54.8385797Z Create a :class:`setuptools.Extension` for C++. 2024-12-18T01:09:54.8386082Z 2024-12-18T01:09:54.8386363Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:09:54.8387010Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-12-18T01:09:54.8387359Z 2024-12-18T01:09:54.8387568Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:09:54.8388103Z constructor. Full list arguments can be found at 2024-12-18T01:09:54.8388808Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:09:54.8389302Z 2024-12-18T01:09:54.8389431Z .. note:: 2024-12-18T01:09:54.8389782Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:09:54.8390406Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:09:54.8391065Z the user's responsibility in their library to not use APIs from 2024-12-18T01:09:54.8391780Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:09:54.8392471Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:09:54.8393014Z example, to give access to custom ops from python, the library should 2024-12-18T01:09:54.8393481Z register the ops through the dispatcher. 2024-12-18T01:09:54.8393727Z 2024-12-18T01:09:54.8393816Z Example: 2024-12-18T01:09:54.8394044Z >>> # xdoctest: +SKIP 2024-12-18T01:09:54.8394368Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:54.8394730Z >>> from setuptools import setup 2024-12-18T01:09:54.8395167Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-12-18T01:09:54.8395592Z >>> setup( 2024-12-18T01:09:54.8395844Z ... name='extension', 2024-12-18T01:09:54.8396128Z ... ext_modules=[ 2024-12-18T01:09:54.8396388Z ... CppExtension( 2024-12-18T01:09:54.8396723Z ... name='extension', 2024-12-18T01:09:54.8397047Z ... sources=['extension.cpp'], 2024-12-18T01:09:54.8397405Z ... extra_compile_args=['-g'], 2024-12-18T01:09:54.8397787Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2024-12-18T01:09:54.8398151Z ... ], 2024-12-18T01:09:54.8398381Z ... cmdclass={ 2024-12-18T01:09:54.8398643Z ... 'build_ext': BuildExtension 2024-12-18T01:09:54.8398953Z ... }) 2024-12-18T01:09:54.8399095Z 2024-12-18T01:09:54.8399347Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.8399712Z 2024-12-18T01:09:54.8400521Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1019. 2024-12-18T01:09:54.8401879Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.8402256Z 2024-12-18T01:09:54.8402428Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-12-18T01:09:54.8402856Z 2024-12-18T01:09:54.8403098Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:09:54.8403663Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-12-18T01:09:54.8404219Z extension. This includes the CUDA include path, library path and runtime 2024-12-18T01:09:54.8404659Z library. 2024-12-18T01:09:54.8404779Z 2024-12-18T01:09:54.8404999Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:09:54.8405469Z constructor. Full list arguments can be found at 2024-12-18T01:09:54.8406043Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:09:54.8406491Z 2024-12-18T01:09:54.8406585Z .. note:: 2024-12-18T01:09:54.8407006Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:09:54.8407565Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:09:54.8408089Z the user's responsibility in their library to not use APIs from 2024-12-18T01:09:54.8408645Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:09:54.8409202Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:09:54.8409817Z example, to give access to custom ops from python, the library should 2024-12-18T01:09:54.8410492Z register the ops through the dispatcher. 2024-12-18T01:09:54.8410776Z 2024-12-18T01:09:54.8410919Z Example: 2024-12-18T01:09:54.8411153Z >>> # xdoctest: +SKIP 2024-12-18T01:09:54.8411469Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:54.8411843Z >>> from setuptools import setup 2024-12-18T01:09:54.8412284Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-12-18T01:09:54.8412724Z >>> setup( 2024-12-18T01:09:54.8412970Z ... name='cuda_extension', 2024-12-18T01:09:54.8413258Z ... ext_modules=[ 2024-12-18T01:09:54.8413530Z ... CUDAExtension( 2024-12-18T01:09:54.8413837Z ... name='cuda_extension', 2024-12-18T01:09:54.8414230Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:09:54.8414647Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:09:54.8415000Z ... 'nvcc': ['-O2']}, 2024-12-18T01:09:54.8415395Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2024-12-18T01:09:54.8415763Z ... ], 2024-12-18T01:09:54.8415991Z ... cmdclass={ 2024-12-18T01:09:54.8416266Z ... 'build_ext': BuildExtension 2024-12-18T01:09:54.8416568Z ... }) 2024-12-18T01:09:54.8416710Z 2024-12-18T01:09:54.8416813Z Compute capabilities: 2024-12-18T01:09:54.8416989Z 2024-12-18T01:09:54.8417290Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-12-18T01:09:54.8417999Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-12-18T01:09:54.8418707Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-12-18T01:09:54.8419422Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2024-12-18T01:09:54.8420127Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-12-18T01:09:54.8420643Z support (see below for details on PTX). 2024-12-18T01:09:54.8420876Z 2024-12-18T01:09:54.8421182Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-12-18T01:09:54.8421727Z CCs you want the extension to support: 2024-12-18T01:09:54.8421944Z 2024-12-18T01:09:54.8422145Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-12-18T01:09:54.8422689Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-12-18T01:09:54.8423041Z 2024-12-18T01:09:54.8423367Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-12-18T01:09:54.8424242Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-12-18T01:09:54.8424983Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-12-18T01:09:54.8425702Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-12-18T01:09:54.8426438Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-12-18T01:09:54.8427156Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-12-18T01:09:54.8427871Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-12-18T01:09:54.8428767Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-12-18T01:09:54.8429305Z "8.0 8.6" would be better. 2024-12-18T01:09:54.8429492Z 2024-12-18T01:09:54.8429795Z Note that while it's possible to include all supported archs, the more archs get included the 2024-12-18T01:09:54.8430505Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-12-18T01:09:54.8430911Z 2024-12-18T01:09:54.8431254Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-12-18T01:09:54.8431921Z To workaround the issue, move python binding logic to pure C++ file. 2024-12-18T01:09:54.8432246Z 2024-12-18T01:09:54.8432358Z Example use: 2024-12-18T01:09:54.8432588Z #include 2024-12-18T01:09:54.8432927Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-12-18T01:09:54.8433196Z 2024-12-18T01:09:54.8433289Z Instead of: 2024-12-18T01:09:54.8433531Z #include 2024-12-18T01:09:54.8433895Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-12-18T01:09:54.8434159Z 2024-12-18T01:09:54.8434433Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-12-18T01:09:54.8435329Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-12-18T01:09:54.8435953Z 2024-12-18T01:09:54.8436253Z Relocatable device code linking: 2024-12-18T01:09:54.8436472Z 2024-12-18T01:09:54.8436751Z If you want to reference device symbols across compilation units (across object files), 2024-12-18T01:09:54.8437410Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-12-18T01:09:54.8438147Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-12-18T01:09:54.8438951Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-12-18T01:09:54.8439707Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-12-18T01:09:54.8440330Z helps reduce the protentional perf degradation of `-rdc`. 2024-12-18T01:09:54.8440795Z Note that it needs to be used at both steps to be useful. 2024-12-18T01:09:54.8441067Z 2024-12-18T01:09:54.8441444Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-12-18T01:09:54.8442097Z There is also a case where `-dlink` is used without `-rdc`: 2024-12-18T01:09:54.8442643Z when an extension is linked against a static lib containing rdc-compiled objects 2024-12-18T01:09:54.8443217Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-12-18T01:09:54.8443551Z 2024-12-18T01:09:54.8443753Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-12-18T01:09:54.8444076Z 2024-12-18T01:09:54.8444166Z Example: 2024-12-18T01:09:54.8444391Z >>> # xdoctest: +SKIP 2024-12-18T01:09:54.8444725Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:54.8445086Z >>> CUDAExtension( 2024-12-18T01:09:54.8445344Z ... name='cuda_extension', 2024-12-18T01:09:54.8445836Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:09:54.8446212Z ... dlink=True, 2024-12-18T01:09:54.8446510Z ... dlink_libraries=["dlink_lib"], 2024-12-18T01:09:54.8446866Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:09:54.8447222Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-12-18T01:09:54.8447473Z 2024-12-18T01:09:54.8447725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.8448104Z 2024-12-18T01:09:54.8448660Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1300. 2024-12-18T01:09:54.8449517Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.8449985Z 2024-12-18T01:09:54.8450133Z Load a PyTorch C++ extension just-in-time (JIT). 2024-12-18T01:09:54.8450395Z 2024-12-18T01:09:54.8450606Z To load an extension, a Ninja build file is emitted, which is used to 2024-12-18T01:09:54.8451146Z compile the given sources into a dynamic library. This library is 2024-12-18T01:09:54.8451696Z subsequently loaded into the current Python process as a module and 2024-12-18T01:09:54.8452166Z returned from this function, ready for use. 2024-12-18T01:09:54.8452398Z 2024-12-18T01:09:54.8452620Z By default, the directory to which the build file is emitted and the 2024-12-18T01:09:54.8453165Z resulting library compiled to is ``/torch_extensions/``, where 2024-12-18T01:09:54.8453722Z ```` is the temporary folder on the current platform and ```` 2024-12-18T01:09:54.8454258Z the name of the extension. This location can be overridden in two ways. 2024-12-18T01:09:54.8454797Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-12-18T01:09:54.8455350Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-12-18T01:09:54.8455893Z into subfolders of this directory. Second, if the ``build_directory`` 2024-12-18T01:09:54.8456463Z argument to this function is supplied, it overrides the entire path, i.e. 2024-12-18T01:09:54.8456977Z the library will be compiled into that folder directly. 2024-12-18T01:09:54.8457259Z 2024-12-18T01:09:54.8457468Z To compile the sources, the default system compiler (``c++``) is used, 2024-12-18T01:09:54.8458031Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-12-18T01:09:54.8458621Z additional arguments to the compilation process, ``extra_cflags`` or 2024-12-18T01:09:54.8459174Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-12-18T01:09:54.8459732Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-12-18T01:09:54.8460217Z ``extra_cflags`` to pass further include directories. 2024-12-18T01:09:54.8460495Z 2024-12-18T01:09:54.8460731Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-12-18T01:09:54.8461267Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-12-18T01:09:54.8461812Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-12-18T01:09:54.8462371Z passing the CUDA lib64 directory as a library directory, and linking 2024-12-18T01:09:54.8462850Z ``cudart``. You can pass additional flags to nvcc via 2024-12-18T01:09:54.8463320Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-12-18T01:09:54.8463876Z heuristics for finding the CUDA install directory are used, which usually 2024-12-18T01:09:54.8464439Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-12-18T01:09:54.8464862Z safest option. 2024-12-18T01:09:54.8464998Z 2024-12-18T01:09:54.8465084Z Args: 2024-12-18T01:09:54.8465416Z name: The name of the extension to build. This MUST be the same as the 2024-12-18T01:09:54.8465856Z name of the pybind11 module! 2024-12-18T01:09:54.8466271Z sources: A list of relative or absolute paths to C++ source files. 2024-12-18T01:09:54.8466820Z extra_cflags: optional list of compiler flags to forward to the build. 2024-12-18T01:09:54.8467440Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-12-18T01:09:54.8467890Z when building CUDA sources. 2024-12-18T01:09:54.8468408Z extra_ldflags: optional list of linker flags to forward to the build. 2024-12-18T01:09:54.8468971Z extra_include_paths: optional list of include directories to forward 2024-12-18T01:09:54.8469405Z to the build. 2024-12-18T01:09:54.8469737Z build_directory: optional path to use as build workspace. 2024-12-18T01:09:54.8470219Z verbose: If ``True``, turns on verbose logging of load steps. 2024-12-18T01:09:54.8470742Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:09:54.8471303Z the build. If set to ``None`` (default), this value is 2024-12-18T01:09:54.8471787Z automatically determined based on the existence of ``.cu`` or 2024-12-18T01:09:54.8472288Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-12-18T01:09:54.8472682Z and libraries to be included. 2024-12-18T01:09:54.8473109Z is_python_module: If ``True`` (default), imports the produced shared 2024-12-18T01:09:54.8473624Z library as a Python module. If ``False``, behavior depends on 2024-12-18T01:09:54.8474036Z ``is_standalone``. 2024-12-18T01:09:54.8474429Z is_standalone: If ``False`` (default) loads the constructed extension 2024-12-18T01:09:54.8474943Z into the process as a plain dynamic library. If ``True``, build a 2024-12-18T01:09:54.8475369Z standalone executable. 2024-12-18T01:09:54.8475571Z 2024-12-18T01:09:54.8475663Z Returns: 2024-12-18T01:09:54.8475909Z If ``is_python_module`` is ``True``: 2024-12-18T01:09:54.8484768Z Returns the loaded PyTorch extension as a Python module. 2024-12-18T01:09:54.8485194Z 2024-12-18T01:09:54.8485414Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-12-18T01:09:54.8485977Z Returns nothing. (The shared library is loaded into the process as 2024-12-18T01:09:54.8486412Z a side effect.) 2024-12-18T01:09:54.8486574Z 2024-12-18T01:09:54.8486700Z If ``is_standalone`` is ``True``. 2024-12-18T01:09:54.8487109Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-12-18T01:09:54.8487611Z added to the PATH environment variable as a side effect.) 2024-12-18T01:09:54.8487914Z 2024-12-18T01:09:54.8488009Z Example: 2024-12-18T01:09:54.8488237Z >>> # xdoctest: +SKIP 2024-12-18T01:09:54.8488550Z >>> from torch.utils.cpp_extension import load 2024-12-18T01:09:54.8488887Z >>> module = load( 2024-12-18T01:09:54.8489149Z ... name='extension', 2024-12-18T01:09:54.8489502Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:09:54.8489884Z ... extra_cflags=['-O2'], 2024-12-18T01:09:54.8490180Z ... verbose=True) 2024-12-18T01:09:54.8490343Z 2024-12-18T01:09:54.8490607Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.8490976Z 2024-12-18T01:09:54.8491505Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1593. 2024-12-18T01:09:54.8492399Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.8492786Z 2024-12-18T01:09:54.8492996Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-12-18T01:09:54.8493329Z 2024-12-18T01:09:54.8493562Z This function behaves exactly like :func:`load`, but takes its sources as 2024-12-18T01:09:54.8494144Z strings rather than filenames. These strings are stored to files in the 2024-12-18T01:09:54.8494708Z build directory, after which the behavior of :func:`load_inline` is 2024-12-18T01:09:54.8495134Z identical to :func:`load`. 2024-12-18T01:09:54.8495325Z 2024-12-18T01:09:54.8495415Z See `the 2024-12-18T01:09:54.8495872Z tests `_ 2024-12-18T01:09:54.8496566Z for good examples of using this function. 2024-12-18T01:09:54.8496796Z 2024-12-18T01:09:54.8497044Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-12-18T01:09:54.8497647Z the necessary header includes, as well as the (pybind11) binding code. More 2024-12-18T01:09:54.8498239Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-12-18T01:09:54.8498796Z single ``.cpp`` file. This file is then prepended with ``#include 2024-12-18T01:09:54.8499207Z ``. 2024-12-18T01:09:54.8499369Z 2024-12-18T01:09:54.8499605Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-12-18T01:09:54.8500276Z automatically generated for each function specified. ``functions`` can 2024-12-18T01:09:54.8500860Z either be a list of function names, or a dictionary mapping from function 2024-12-18T01:09:54.8501414Z names to docstrings. If a list is given, the name of each function is used 2024-12-18T01:09:54.8501870Z as its docstring. 2024-12-18T01:09:54.8502031Z 2024-12-18T01:09:54.8502246Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-12-18T01:09:54.8502759Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-12-18T01:09:54.8503261Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-12-18T01:09:54.8503810Z separately, but ultimately linked into a single library. Note that no 2024-12-18T01:09:54.8504365Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-12-18T01:09:54.8504930Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-12-18T01:09:54.8505481Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-12-18T01:09:54.8505931Z include its name in ``functions``). 2024-12-18T01:09:54.8506138Z 2024-12-18T01:09:54.8506333Z See :func:`load` for a description of arguments omitted below. 2024-12-18T01:09:54.8506631Z 2024-12-18T01:09:54.8506717Z Args: 2024-12-18T01:09:54.8507058Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-12-18T01:09:54.8507615Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-12-18T01:09:54.8508169Z functions: A list of function names for which to generate function 2024-12-18T01:09:54.8508818Z bindings. If a dictionary is given, it should map function names to 2024-12-18T01:09:54.8509342Z docstrings (which are otherwise just the function names). 2024-12-18T01:09:54.8509857Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:09:54.8510365Z the build. If set to ``None`` (default), this value is 2024-12-18T01:09:54.8510857Z automatically determined based on whether ``cuda_sources`` is 2024-12-18T01:09:54.8511335Z provided. Set it to ``True`` to force CUDA headers 2024-12-18T01:09:54.8511715Z and libraries to be included. 2024-12-18T01:09:54.8512129Z with_pytorch_error_handling: Determines whether pytorch error and 2024-12-18T01:09:54.8512656Z warning macros are handled by pytorch instead of pybind. To do 2024-12-18T01:09:54.8513192Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-12-18T01:09:54.8513739Z function. This redirection might cause issues in obscure cases 2024-12-18T01:09:54.8514247Z of cpp. This flag should be set to ``False`` when this redirect 2024-12-18T01:09:54.8514650Z causes issues. 2024-12-18T01:09:54.8514806Z 2024-12-18T01:09:54.8514895Z Example: 2024-12-18T01:09:54.8515172Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:54.8515596Z >>> from torch.utils.cpp_extension import load_inline 2024-12-18T01:09:54.8515962Z >>> source = """ 2024-12-18T01:09:54.8516284Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-12-18T01:09:54.8516633Z return x.sin() + y.sin(); 2024-12-18T01:09:54.8516914Z } 2024-12-18T01:09:54.8517205Z """ 2024-12-18T01:09:54.8517473Z >>> module = load_inline(name='inline_extension', 2024-12-18T01:09:54.8517852Z ... cpp_sources=[source], 2024-12-18T01:09:54.8518197Z ... functions=['sin_add']) 2024-12-18T01:09:54.8518439Z 2024-12-18T01:09:54.8518531Z .. note:: 2024-12-18T01:09:54.8518899Z Since load_inline will just-in-time compile the source code, please ensure 2024-12-18T01:09:54.8519492Z that you have the right toolchains installed in the runtime. For example, 2024-12-18T01:09:54.8520066Z when loading C++, make sure a C++ compiler is available. If you're loading 2024-12-18T01:09:54.8520653Z a CUDA extension, you will need to additionally install the corresponding CUDA 2024-12-18T01:09:54.8521312Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2024-12-18T01:09:54.8521920Z are not included when you install torch and must be additionally installed. 2024-12-18T01:09:54.8522284Z 2024-12-18T01:09:54.8522542Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2024-12-18T01:09:54.8523141Z the extension. This may use up too many resources on some systems. One 2024-12-18T01:09:54.8523712Z can control the number of workers by setting the `MAX_JOBS` environment 2024-12-18T01:09:54.8524174Z variable to a non-negative number. 2024-12-18T01:09:54.8524391Z 2024-12-18T01:09:54.8524643Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.8525019Z 2024-12-18T01:09:54.9856880Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-12-18T01:09:54.9857902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:54.9858296Z 2024-12-18T01:09:54.9858597Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-12-18T01:09:54.9859013Z 2024-12-18T01:09:54.9859316Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-12-18T01:09:54.9859978Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-12-18T01:09:54.9860585Z server like load. It can emulate multiple calling threads to a single module 2024-12-18T01:09:54.9861171Z provided. In the future we plan to enhance this component to support inter and 2024-12-18T01:09:54.9861763Z intra-op parallelism as well as multiple models running in a single process. 2024-12-18T01:09:54.9862137Z 2024-12-18T01:09:54.9862390Z Please note that even though nn.Module is supported, it might incur an overhead 2024-12-18T01:09:54.9863000Z from the need to hold GIL every time we execute Python code or pass around 2024-12-18T01:09:54.9863707Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-12-18T01:09:54.9864431Z model for inference deployment it is better to switch to using it in this 2024-12-18T01:09:54.9864881Z benchmark. 2024-12-18T01:09:54.9865011Z 2024-12-18T01:09:54.9865117Z Example:: 2024-12-18T01:09:54.9865239Z 2024-12-18T01:09:54.9865370Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:54.9865729Z >>> from torch.utils import ThroughputBenchmark 2024-12-18T01:09:54.9866117Z >>> bench = ThroughputBenchmark(my_module) 2024-12-18T01:09:54.9866521Z >>> # Pre-populate benchmark's data set with the inputs 2024-12-18T01:09:54.9866901Z >>> for input in inputs: 2024-12-18T01:09:54.9867308Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-12-18T01:09:54.9867780Z ... bench.add_input(input[0], x2=input[1]) 2024-12-18T01:09:54.9868210Z >>> # Inputs supplied above are randomly used during the execution 2024-12-18T01:09:54.9868714Z >>> stats = bench.benchmark( 2024-12-18T01:09:54.9869022Z ... num_calling_threads=4, 2024-12-18T01:09:54.9869340Z ... num_warmup_iters = 100, 2024-12-18T01:09:54.9869647Z ... num_iters = 1000, 2024-12-18T01:09:54.9870136Z ... ) 2024-12-18T01:09:54.9870447Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-12-18T01:09:54.9870922Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-12-18T01:09:54.9871209Z 2024-12-18T01:09:54.9871473Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:54.9871838Z 2024-12-18T01:09:55.0816231Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17. 2024-12-18T01:09:55.0817207Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:55.0817777Z Sampler that restricts data loading to a subset of the dataset. 2024-12-18T01:09:55.0818100Z 2024-12-18T01:09:55.0818496Z It is especially useful in conjunction with 2024-12-18T01:09:55.0818998Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-12-18T01:09:55.0819645Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-12-18T01:09:55.0820258Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-12-18T01:09:55.0820740Z original dataset that is exclusive to it. 2024-12-18T01:09:55.0820977Z 2024-12-18T01:09:55.0821082Z .. note:: 2024-12-18T01:09:55.0821455Z Dataset is assumed to be of constant size and that any instance of it always 2024-12-18T01:09:55.0821963Z returns the same elements in the same order. 2024-12-18T01:09:55.0822225Z 2024-12-18T01:09:55.0822331Z Args: 2024-12-18T01:09:55.0822579Z dataset: Dataset used for sampling. 2024-12-18T01:09:55.0823026Z num_replicas (int, optional): Number of processes participating in 2024-12-18T01:09:55.0823613Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-12-18T01:09:55.0824084Z current distributed group. 2024-12-18T01:09:55.0824544Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-12-18T01:09:55.0825107Z By default, :attr:`rank` is retrieved from the current distributed 2024-12-18T01:09:55.0825518Z group. 2024-12-18T01:09:55.0825887Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-12-18T01:09:55.0826310Z indices. 2024-12-18T01:09:55.0826657Z seed (int, optional): random seed used to shuffle the sampler if 2024-12-18T01:09:55.0827171Z :attr:`shuffle=True`. This number should be identical across all 2024-12-18T01:09:55.0827657Z processes in the distributed group. Default: ``0``. 2024-12-18T01:09:55.0828158Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-12-18T01:09:55.0828790Z tail of the data to make it evenly divisible across the number of 2024-12-18T01:09:55.0829298Z replicas. If ``False``, the sampler will add extra indices to make 2024-12-18T01:09:55.0829838Z the data evenly divisible across the replicas. Default: ``False``. 2024-12-18T01:09:55.0830175Z 2024-12-18T01:09:55.0830270Z .. warning:: 2024-12-18T01:09:55.0830605Z In distributed mode, calling the :meth:`set_epoch` method at 2024-12-18T01:09:55.0831160Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-12-18T01:09:55.0831775Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-12-18T01:09:55.0832278Z the same ordering will be always used. 2024-12-18T01:09:55.0832517Z 2024-12-18T01:09:55.0832611Z Example:: 2024-12-18T01:09:55.0832751Z 2024-12-18T01:09:55.0832852Z >>> # xdoctest: +SKIP 2024-12-18T01:09:55.0833262Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-12-18T01:09:55.0833783Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-12-18T01:09:55.0834181Z ... sampler=sampler) 2024-12-18T01:09:55.0834689Z >>> for epoch in range(start_epoch, n_epochs): 2024-12-18T01:09:55.0835051Z ... if is_distributed: 2024-12-18T01:09:55.0835375Z ... sampler.set_epoch(epoch) 2024-12-18T01:09:55.0835706Z ... train(loader) 2024-12-18T01:09:55.0835965Z 2024-12-18T01:09:55.0836521Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:55.0836904Z 2024-12-18T01:09:55.2701200Z gathering tests 2024-12-18T01:09:55.2713554Z running 705 test(s) 2024-12-18T01:09:55.2749524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 1045 <- wrt source file 2024-12-18T01:09:55.2757699Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-12-18T01:09:55.2758808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1081 <- wrt source file 2024-12-18T01:09:55.2762190Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-12-18T01:09:55.2763697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1150 <- wrt source file 2024-12-18T01:09:55.2765417Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-12-18T01:09:55.2766687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1199 <- wrt source file 2024-12-18T01:09:55.2768583Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-12-18T01:09:55.2769964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1236 <- wrt source file 2024-12-18T01:09:55.2771174Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-12-18T01:09:55.2772562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1391 <- wrt source file 2024-12-18T01:09:55.2774204Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-12-18T01:09:55.2775495Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2488 <- wrt source file 2024-12-18T01:09:55.2776771Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-12-18T01:09:55.2778097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2744 <- wrt source file 2024-12-18T01:09:55.2779452Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-12-18T01:09:55.2780775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2024-12-18T01:09:55.2782119Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-12-18T01:09:55.2783431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2024-12-18T01:09:55.2784794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-12-18T01:09:55.2785997Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-12-18T01:09:55.2787117Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-12-18T01:09:55.2788490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-12-18T01:09:55.2789586Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-12-18T01:09:55.2790698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-12-18T01:09:55.3368566Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-12-18T01:09:55.3370111Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-12-18T01:09:55.3371415Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-12-18T01:09:55.3372633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 650 <- wrt source file 2024-12-18T01:09:55.3377680Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-12-18T01:09:55.3378971Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 707 <- wrt source file 2024-12-18T01:09:55.3395431Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-12-18T01:09:55.3396720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1336 <- wrt source file 2024-12-18T01:09:55.3512855Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-12-18T01:09:55.3516245Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1381 <- wrt source file 2024-12-18T01:09:55.3521397Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-12-18T01:09:55.3522538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1454 <- wrt source file 2024-12-18T01:09:55.3529044Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-12-18T01:09:55.3530197Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1484 <- wrt source file 2024-12-18T01:09:55.3535111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-12-18T01:09:55.3536457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-12-18T01:09:55.3555567Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-12-18T01:09:55.3556771Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-12-18T01:09:55.3562520Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-12-18T01:09:55.3563726Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-12-18T01:09:55.3565777Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-12-18T01:09:55.3567001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-12-18T01:09:55.3577782Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-12-18T01:09:55.3578882Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 292 <- wrt source file 2024-12-18T01:09:55.3632045Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-12-18T01:09:55.3633247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1019 <- wrt source file 2024-12-18T01:09:55.3644592Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-12-18T01:09:55.3645810Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1294 <- wrt source file 2024-12-18T01:09:55.3654869Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-12-18T01:09:55.3656031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1378 <- wrt source file 2024-12-18T01:09:55.3662626Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-12-18T01:09:55.3663774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1412 <- wrt source file 2024-12-18T01:09:55.3671529Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-12-18T01:09:55.3672651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1463 <- wrt source file 2024-12-18T01:09:55.3685676Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-12-18T01:09:55.3686833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1504 <- wrt source file 2024-12-18T01:09:55.3701826Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-12-18T01:09:55.3702980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1540 <- wrt source file 2024-12-18T01:09:55.3719252Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-12-18T01:09:55.3720411Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1578 <- wrt source file 2024-12-18T01:09:55.3740629Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-12-18T01:09:55.3741770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1751 <- wrt source file 2024-12-18T01:09:55.3773109Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-12-18T01:09:55.3774263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1918 <- wrt source file 2024-12-18T01:09:55.3801345Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-12-18T01:09:55.3802878Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2018 <- wrt source file 2024-12-18T01:09:55.3804910Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-12-18T01:09:55.3806684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2118 <- wrt source file 2024-12-18T01:09:55.3807815Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-12-18T01:09:55.3808874Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-12-18T01:09:55.3810200Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-12-18T01:09:55.3811662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-12-18T01:09:55.3812869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-12-18T01:09:55.3813919Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-12-18T01:09:55.3815014Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-12-18T01:09:55.3816126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 135 <- wrt source file 2024-12-18T01:09:55.3817270Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2024-12-18T01:09:55.3818485Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 235 <- wrt source file 2024-12-18T01:09:55.3834863Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-12-18T01:09:55.3836269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 290 <- wrt source file 2024-12-18T01:09:55.3839680Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-12-18T01:09:55.3840779Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 483 <- wrt source file 2024-12-18T01:09:55.3851287Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-12-18T01:09:55.3852349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 550 <- wrt source file 2024-12-18T01:09:55.3860557Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-12-18T01:09:55.3862095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 674 <- wrt source file 2024-12-18T01:09:55.3863281Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-12-18T01:09:55.3864479Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 995 <- wrt source file 2024-12-18T01:09:55.3957705Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2024-12-18T01:09:55.3958897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1084 <- wrt source file 2024-12-18T01:09:55.4100083Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2024-12-18T01:09:55.4101296Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-12-18T01:09:55.4106375Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-12-18T01:09:55.4107825Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 417 <- wrt source file 2024-12-18T01:09:55.4141570Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-12-18T01:09:55.4142807Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1570 <- wrt source file 2024-12-18T01:09:55.4144827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-12-18T01:09:55.4146055Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1705 <- wrt source file 2024-12-18T01:09:55.4148218Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-12-18T01:09:55.4149579Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1953 <- wrt source file 2024-12-18T01:09:55.4175786Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-12-18T01:09:55.4177025Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1972 <- wrt source file 2024-12-18T01:09:55.4182767Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-12-18T01:09:55.4184732Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-12-18T01:09:55.4186052Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-12-18T01:09:55.4187344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 289 <- wrt source file 2024-12-18T01:09:55.4188655Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-12-18T01:09:55.4189854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 314 <- wrt source file 2024-12-18T01:09:55.4191511Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2024-12-18T01:09:55.4193607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0, line 388 <- wrt source file 2024-12-18T01:09:55.4194790Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0 2024-12-18T01:09:55.4196343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 460 <- wrt source file 2024-12-18T01:09:55.4197627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-12-18T01:09:55.4198789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 923 <- wrt source file 2024-12-18T01:09:55.4199922Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-12-18T01:09:55.4201064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-12-18T01:09:55.4202270Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-12-18T01:09:55.4203478Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 245 <- wrt source file 2024-12-18T01:09:55.4205327Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-12-18T01:09:55.4206542Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 277 <- wrt source file 2024-12-18T01:09:55.4207777Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-12-18T01:09:55.4209464Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1778 <- wrt source file 2024-12-18T01:09:55.4211131Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-12-18T01:09:55.4212526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 45 <- wrt source file 2024-12-18T01:09:55.4214135Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2024-12-18T01:09:55.4216006Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 105 <- wrt source file 2024-12-18T01:09:55.4217252Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-12-18T01:09:55.4218499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 159 <- wrt source file 2024-12-18T01:09:55.4661939Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2024-12-18T01:09:55.4663212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 345 <- wrt source file 2024-12-18T01:09:55.4664539Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-12-18T01:09:55.4665888Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 376 <- wrt source file 2024-12-18T01:09:55.4667256Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-12-18T01:09:55.4668905Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 397 <- wrt source file 2024-12-18T01:09:55.4670754Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-12-18T01:09:55.4672252Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 409 <- wrt source file 2024-12-18T01:09:55.4673533Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-12-18T01:09:55.4674993Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 493 <- wrt source file 2024-12-18T01:09:55.4676411Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-12-18T01:09:55.4678085Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 591 <- wrt source file 2024-12-18T01:09:55.4679458Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-12-18T01:09:55.4680752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-12-18T01:09:55.4682366Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-12-18T01:09:55.4683665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-12-18T01:09:55.4684983Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-12-18T01:09:55.4686210Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-12-18T01:09:55.4687521Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2024-12-18T01:09:55.4732857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 218 <- wrt source file 2024-12-18T01:09:55.4735245Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-12-18T01:09:55.4737607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 280 <- wrt source file 2024-12-18T01:09:55.4802051Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2024-12-18T01:09:55.4804267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 364 <- wrt source file 2024-12-18T01:09:55.4822570Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-12-18T01:09:55.4824856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0, line 428 <- wrt source file 2024-12-18T01:09:55.4841459Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0 2024-12-18T01:09:55.4843844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file 2024-12-18T01:09:55.4857000Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-12-18T01:09:55.4859398Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file 2024-12-18T01:09:55.4913380Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-12-18T01:09:55.4915747Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 254 <- wrt source file 2024-12-18T01:09:55.4918467Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2024-12-18T01:09:55.4920992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 287 <- wrt source file 2024-12-18T01:09:55.4923672Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-12-18T01:09:55.4926160Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 896 <- wrt source file 2024-12-18T01:09:55.5246977Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-12-18T01:09:55.5249268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file 2024-12-18T01:09:55.5251371Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0 2024-12-18T01:09:55.5253692Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2024-12-18T01:09:55.5256293Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-12-18T01:09:55.5258668Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 232 <- wrt source file 2024-12-18T01:09:55.5288353Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-12-18T01:09:55.5290588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 474 <- wrt source file 2024-12-18T01:09:55.5347891Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-12-18T01:09:55.5350301Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2024-12-18T01:09:55.6241633Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-12-18T01:09:55.6243991Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2024-12-18T01:09:55.6299962Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-12-18T01:09:55.6302299Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2024-12-18T01:09:55.6318135Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-12-18T01:09:55.6320533Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2024-12-18T01:09:55.6323051Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-12-18T01:09:55.6325466Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1705 <- wrt source file 2024-12-18T01:09:55.6483384Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-12-18T01:09:55.6485787Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2024-12-18T01:09:55.6488917Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-12-18T01:09:55.6491232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-12-18T01:09:55.6493503Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-12-18T01:09:55.6496139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 112 <- wrt source file 2024-12-18T01:09:55.6499338Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-12-18T01:09:55.6502090Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 120 <- wrt source file 2024-12-18T01:09:55.6504757Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-12-18T01:09:55.6507429Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 259 <- wrt source file 2024-12-18T01:09:55.6510277Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2024-12-18T01:09:55.6512693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 112 <- wrt source file 2024-12-18T01:09:55.6514866Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-12-18T01:09:55.6517000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 96 <- wrt source file 2024-12-18T01:09:55.6519182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0 2024-12-18T01:09:55.6521413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 86 <- wrt source file 2024-12-18T01:09:55.6523820Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-12-18T01:09:55.6526376Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1348 <- wrt source file 2024-12-18T01:09:55.6529126Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2024-12-18T01:09:55.6531552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 71 <- wrt source file 2024-12-18T01:09:55.6790809Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-12-18T01:09:55.6792134Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 200 <- wrt source file 2024-12-18T01:09:55.6863444Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-12-18T01:09:55.6864915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 269 <- wrt source file 2024-12-18T01:09:55.6866328Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-12-18T01:09:55.6867903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 375 <- wrt source file 2024-12-18T01:09:55.6932649Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-12-18T01:09:55.6934038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 502 <- wrt source file 2024-12-18T01:09:55.7073546Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-12-18T01:09:55.7074940Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 674 <- wrt source file 2024-12-18T01:09:55.7211563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-12-18T01:09:55.7213222Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file 2024-12-18T01:09:55.7214639Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-12-18T01:09:55.7216008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 161 <- wrt source file 2024-12-18T01:09:55.7278121Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-12-18T01:09:55.7279457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 45 <- wrt source file 2024-12-18T01:09:55.7284128Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-12-18T01:09:55.7285794Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 425 <- wrt source file 2024-12-18T01:09:55.7287002Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-12-18T01:09:55.7288218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file 2024-12-18T01:09:55.7345230Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-12-18T01:09:55.7346563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 305 <- wrt source file 2024-12-18T01:09:55.7347894Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-12-18T01:09:55.7349246Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 996 <- wrt source file 2024-12-18T01:09:55.7394326Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-12-18T01:09:55.7395736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1061 <- wrt source file 2024-12-18T01:09:55.7397081Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-12-18T01:09:55.7398367Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1282 <- wrt source file 2024-12-18T01:09:55.7412729Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-12-18T01:09:55.7414083Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1348 <- wrt source file 2024-12-18T01:09:55.7416442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-12-18T01:09:55.7417951Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1411 <- wrt source file 2024-12-18T01:09:55.7421598Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-12-18T01:09:55.7424015Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1456 <- wrt source file 2024-12-18T01:09:55.7426301Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-12-18T01:09:55.7428743Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1566 <- wrt source file 2024-12-18T01:09:55.7431506Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-12-18T01:09:55.7433857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-12-18T01:09:55.7436461Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-12-18T01:09:55.7438735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-12-18T01:09:55.7440996Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-12-18T01:09:55.7443278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2024-12-18T01:09:55.7446207Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-12-18T01:09:55.7449196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-12-18T01:09:55.7452578Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-12-18T01:09:55.7456169Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2024-12-18T01:09:55.7459947Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-12-18T01:09:55.7463691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file 2024-12-18T01:09:55.7467599Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-12-18T01:09:55.7471353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file 2024-12-18T01:09:55.7474479Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-12-18T01:09:55.7477002Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file 2024-12-18T01:09:55.7479579Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-12-18T01:09:55.7481961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 410 <- wrt source file 2024-12-18T01:09:55.7505405Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-12-18T01:09:55.7508213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file 2024-12-18T01:09:55.7510790Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-12-18T01:09:55.7513138Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file 2024-12-18T01:09:55.7515366Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-12-18T01:09:55.7517764Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file 2024-12-18T01:09:55.7520181Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-12-18T01:09:55.7522450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2024-12-18T01:09:55.7525027Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-12-18T01:09:55.7527542Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2024-12-18T01:09:55.7530423Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-12-18T01:09:55.7532993Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file 2024-12-18T01:09:55.7535663Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-12-18T01:09:55.7538333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file 2024-12-18T01:09:55.7541020Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-12-18T01:09:55.7543601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 208 <- wrt source file 2024-12-18T01:09:55.7546362Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-12-18T01:09:55.7548983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 294 <- wrt source file 2024-12-18T01:09:55.7551496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-12-18T01:09:55.7554229Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 376 <- wrt source file 2024-12-18T01:09:55.7556081Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-12-18T01:09:55.7557588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 458 <- wrt source file 2024-12-18T01:09:55.7559128Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-12-18T01:09:55.7560730Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2024-12-18T01:09:55.7562189Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-12-18T01:09:55.7564242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file 2024-12-18T01:09:55.7567112Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-12-18T01:09:55.7570143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2024-12-18T01:09:55.7572909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-12-18T01:09:55.7575242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file 2024-12-18T01:09:55.7577718Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-12-18T01:09:55.7580634Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file 2024-12-18T01:09:55.7583863Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-12-18T01:09:55.7586582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file 2024-12-18T01:09:55.7589343Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-12-18T01:09:55.7591797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2024-12-18T01:09:55.7594381Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-12-18T01:09:55.7596913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file 2024-12-18T01:09:55.7599228Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-12-18T01:09:55.7600788Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 634 <- wrt source file 2024-12-18T01:09:55.7602155Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-12-18T01:09:55.7603512Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 890 <- wrt source file 2024-12-18T01:09:55.7605076Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-12-18T01:09:55.7606564Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1012 <- wrt source file 2024-12-18T01:09:55.7607996Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-12-18T01:09:55.7609393Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1138 <- wrt source file 2024-12-18T01:09:55.7610908Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-12-18T01:09:55.7613324Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file 2024-12-18T01:09:55.7614931Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-12-18T01:09:55.7616366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 276 <- wrt source file 2024-12-18T01:09:55.7617861Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-12-18T01:09:55.7620388Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 24 <- wrt source file 2024-12-18T01:09:55.7622058Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-12-18T01:09:55.7623610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 177 <- wrt source file 2024-12-18T01:09:55.7625162Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-12-18T01:09:55.7626663Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2024-12-18T01:09:55.7628011Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-12-18T01:09:55.7629729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file 2024-12-18T01:09:55.7632686Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-12-18T01:09:55.7634675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file 2024-12-18T01:09:55.7791331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2024-12-18T01:09:55.7794693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2024-12-18T01:09:55.7797958Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-12-18T01:09:55.7801058Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file 2024-12-18T01:09:55.7803730Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-12-18T01:09:55.7806390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2024-12-18T01:09:55.7809173Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-12-18T01:09:55.7811870Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file 2024-12-18T01:09:55.7814634Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-12-18T01:09:55.7817161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2024-12-18T01:09:55.7819861Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-12-18T01:09:55.7822517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2024-12-18T01:09:55.7825289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-12-18T01:09:55.7827982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2024-12-18T01:09:55.7830783Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-12-18T01:09:55.7833530Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2024-12-18T01:09:55.7836550Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-12-18T01:09:55.7839113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 93 <- wrt source file 2024-12-18T01:09:55.7841499Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-12-18T01:09:55.7843920Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 115 <- wrt source file 2024-12-18T01:09:55.7846486Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-12-18T01:09:55.7848909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2024-12-18T01:09:55.7851286Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-12-18T01:09:55.7853651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file 2024-12-18T01:09:55.7856116Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-12-18T01:09:55.7858563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file 2024-12-18T01:09:55.7861227Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-12-18T01:09:55.7863691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 595 <- wrt source file 2024-12-18T01:09:55.7866145Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-12-18T01:09:55.7868829Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 654 <- wrt source file 2024-12-18T01:09:55.7871555Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-12-18T01:09:55.7874323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 706 <- wrt source file 2024-12-18T01:09:55.7877235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-12-18T01:09:55.7879918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 49 <- wrt source file 2024-12-18T01:09:55.7882454Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-12-18T01:09:55.7884985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 128 <- wrt source file 2024-12-18T01:09:55.7887588Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-12-18T01:09:55.7890118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 225 <- wrt source file 2024-12-18T01:09:55.7892640Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-12-18T01:09:55.7895098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file 2024-12-18T01:09:55.7897568Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-12-18T01:09:55.7899985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 517 <- wrt source file 2024-12-18T01:09:55.7902463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-12-18T01:09:55.7904936Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 539 <- wrt source file 2024-12-18T01:09:55.7907473Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-12-18T01:09:55.7910012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 553 <- wrt source file 2024-12-18T01:09:55.7912525Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-12-18T01:09:55.7914948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 575 <- wrt source file 2024-12-18T01:09:55.7917531Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-12-18T01:09:55.7919925Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 702 <- wrt source file 2024-12-18T01:09:55.7922368Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-12-18T01:09:55.7925014Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2024-12-18T01:09:55.7928013Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2024-12-18T01:09:55.7930834Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file 2024-12-18T01:09:55.7933649Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-12-18T01:09:55.7936524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file 2024-12-18T01:09:55.7939378Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2024-12-18T01:09:55.7942274Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 459 <- wrt source file 2024-12-18T01:09:55.7945277Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-12-18T01:09:55.7947892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file 2024-12-18T01:09:55.7950306Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2024-12-18T01:09:55.7952564Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file 2024-12-18T01:09:55.7954795Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0 2024-12-18T01:09:55.7957011Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file 2024-12-18T01:09:55.7959317Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2024-12-18T01:09:55.7961531Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file 2024-12-18T01:09:55.7963793Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0 2024-12-18T01:09:55.7966192Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file 2024-12-18T01:09:55.7968787Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2024-12-18T01:09:55.7971366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file 2024-12-18T01:09:55.7973962Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2024-12-18T01:09:55.7976597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file 2024-12-18T01:09:55.7979085Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2024-12-18T01:09:55.7981675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file 2024-12-18T01:09:55.7984492Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2024-12-18T01:09:55.7987163Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file 2024-12-18T01:09:55.7989908Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2024-12-18T01:09:55.7992313Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file 2024-12-18T01:09:55.7994515Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0 2024-12-18T01:09:55.7996652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 294 <- wrt source file 2024-12-18T01:09:55.7998822Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0 2024-12-18T01:09:55.8000932Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0, line 396 <- wrt source file 2024-12-18T01:09:55.8003087Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0 2024-12-18T01:09:55.8005246Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0, line 631 <- wrt source file 2024-12-18T01:09:55.8007496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0 2024-12-18T01:09:55.8009687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 885 <- wrt source file 2024-12-18T01:09:55.8011912Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0 2024-12-18T01:09:55.8014073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-12-18T01:09:55.8016228Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2024-12-18T01:09:55.8018349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-12-18T01:09:55.8020497Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2024-12-18T01:09:55.8022594Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2024-12-18T01:09:55.8024749Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-12-18T01:09:55.8026929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2024-12-18T01:09:55.8029254Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-12-18T01:09:55.8031599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2024-12-18T01:09:55.8033949Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-12-18T01:09:55.8078591Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 177 <- wrt source file 2024-12-18T01:09:55.8080787Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0 2024-12-18T01:09:55.8083004Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0, line 714 <- wrt source file 2024-12-18T01:09:55.8085330Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0 2024-12-18T01:09:55.8087667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 848 <- wrt source file 2024-12-18T01:09:55.8089855Z * SKIPPED: 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: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3740 <- wrt source file 2024-12-18T01:09:55.8183972Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 2024-12-18T01:09:55.8186620Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3865 <- wrt source file 2024-12-18T01:09:55.8189333Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0 2024-12-18T01:09:55.8191817Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 3967 <- wrt source file 2024-12-18T01:09:55.8194224Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0 2024-12-18T01:09:55.8196601Z * DOCTEST : 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file 2024-12-18T01:09:55.8260985Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2024-12-18T01:09:55.8263907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2024-12-18T01:09:55.8266803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0 2024-12-18T01:09:55.8269762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file 2024-12-18T01:09:55.8272652Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0 2024-12-18T01:09:55.8352906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1323 <- wrt source file 2024-12-18T01:09:55.8355731Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0 2024-12-18T01:09:55.8358554Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1382 <- wrt source file 2024-12-18T01:09:55.8361476Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0 2024-12-18T01:09:55.8364406Z * DOCTEST : 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 237 <- wrt source file 2024-12-18T01:09:55.8518062Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0 2024-12-18T01:09:55.8520904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0, line 235 <- wrt source file 2024-12-18T01:09:55.8523626Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0 2024-12-18T01:09:55.8526329Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 49 <- wrt source file 2024-12-18T01:09:55.8529052Z * SKIPPED: 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file 2024-12-18T01:09:55.8561234Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-12-18T01:09:55.8563410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 20 <- wrt source file 2024-12-18T01:09:55.8565556Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2024-12-18T01:09:55.8567756Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file 2024-12-18T01:09:55.8570209Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-12-18T01:09:55.8572564Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-12-18T01:09:55.8575030Z * SUCCESS: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0 2024-12-18T01:09:55.8591217Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 181 <- wrt source file 2024-12-18T01:09:55.8593847Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2024-12-18T01:09:55.8596516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file 2024-12-18T01:09:55.8599358Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2024-12-18T01:09:55.8601881Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 39 <- wrt source file 2024-12-18T01:09:55.8604235Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2024-12-18T01:09:55.8606608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 19 <- wrt source file 2024-12-18T01:09:55.8609071Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0 2024-12-18T01:09:55.8611660Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2024-12-18T01:09:55.8614340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2024-12-18T01:09:55.8616750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0, line 23 <- wrt source file 2024-12-18T01:09:55.8619001Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0 2024-12-18T01:09:55.8621314Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0, line 34 <- wrt source file 2024-12-18T01:09:55.8623785Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0 2024-12-18T01:09:55.8626102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 21 <- wrt source file 2024-12-18T01:09:55.8628655Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0 2024-12-18T01:09:55.8631008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file 2024-12-18T01:09:55.8633507Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2024-12-18T01:09:55.8635948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file 2024-12-18T01:09:55.8638736Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2024-12-18T01:09:55.8641140Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file 2024-12-18T01:09:55.8643607Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0 2024-12-18T01:09:55.8646023Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 21 <- wrt source file 2024-12-18T01:09:55.8648531Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2024-12-18T01:09:55.8650968Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file 2024-12-18T01:09:55.8653415Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2024-12-18T01:09:55.8655713Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 19 <- wrt source file 2024-12-18T01:09:55.8657983Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2024-12-18T01:09:55.8660266Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file 2024-12-18T01:09:55.8662704Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2024-12-18T01:09:55.8665058Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 20 <- wrt source file 2024-12-18T01:09:55.8667422Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0 2024-12-18T01:09:55.8669929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 25 <- wrt source file 2024-12-18T01:09:55.8672529Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2024-12-18T01:09:55.8675319Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 61 <- wrt source file 2024-12-18T01:09:55.8678401Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0 2024-12-18T01:09:55.8696216Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 31 <- wrt source file 2024-12-18T01:09:55.8698940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2024-12-18T01:09:55.8701364Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 17 <- wrt source file 2024-12-18T01:09:55.8703582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0 2024-12-18T01:09:55.8705789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file 2024-12-18T01:09:55.8708070Z * SKIPPED: 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2024-12-18T01:09:55.8753669Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 602 <- wrt source file 2024-12-18T01:09:55.8756147Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2024-12-18T01:09:55.8758317Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0, line 105 <- wrt source file 2024-12-18T01:09:55.8760370Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0 2024-12-18T01:09:55.8762540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1833 <- wrt source file 2024-12-18T01:09:55.8764878Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0 2024-12-18T01:09:55.8767138Z * DOCTEST : 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135 <- wrt source file 2024-12-18T01:09:55.8782765Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0 2024-12-18T01:09:55.8784987Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file 2024-12-18T01:09:55.8787129Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0 2024-12-18T01:09:55.8789283Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 65 <- wrt source file 2024-12-18T01:09:55.8791485Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0 2024-12-18T01:09:55.8793641Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file 2024-12-18T01:09:55.8795870Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2024-12-18T01:09:55.9848480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file 2024-12-18T01:09:55.9858836Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0 2024-12-18T01:09:55.9861526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file 2024-12-18T01:09:55.9868916Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0 2024-12-18T01:09:55.9871386Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file 2024-12-18T01:09:55.9880702Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0 2024-12-18T01:09:55.9883003Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file 2024-12-18T01:09:55.9898967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0 2024-12-18T01:09:55.9901062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file 2024-12-18T01:09:55.9906677Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2024-12-18T01:09:55.9909235Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3466 <- wrt source file 2024-12-18T01:09:55.9915836Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2024-12-18T01:09:55.9918462Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3538 <- wrt source file 2024-12-18T01:09:55.9922304Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2024-12-18T01:09:55.9924765Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3615 <- wrt source file 2024-12-18T01:09:55.9929187Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2024-12-18T01:09:55.9931808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5178 <- wrt source file 2024-12-18T01:09:55.9939299Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2024-12-18T01:09:55.9941305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-12-18T01:09:55.9948381Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2024-12-18T01:09:55.9950421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-12-18T01:09:55.9953739Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-12-18T01:09:55.9955779Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-12-18T01:09:55.9962742Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-12-18T01:09:55.9964775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-12-18T01:09:55.9968446Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-12-18T01:09:55.9970491Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-12-18T01:09:56.0001967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-12-18T01:09:56.0004822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-12-18T01:09:56.0022153Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-12-18T01:09:56.0024552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-12-18T01:09:56.0026866Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-12-18T01:09:56.0028939Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-12-18T01:09:56.0030953Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-12-18T01:09:56.0032896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-12-18T01:09:56.0034865Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-12-18T01:09:56.0036996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-12-18T01:09:56.0054388Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-12-18T01:09:56.0056412Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-12-18T01:09:56.0058485Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-12-18T01:09:56.0060418Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-12-18T01:09:56.0062385Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-12-18T01:09:56.0064280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-12-18T01:09:56.0066456Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-12-18T01:09:56.0068435Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-12-18T01:09:56.0070372Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-12-18T01:09:56.0072287Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-12-18T01:09:56.0075441Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-12-18T01:09:56.0077615Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-12-18T01:09:56.0080152Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-12-18T01:09:56.0082154Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-12-18T01:09:56.0084348Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-12-18T01:09:56.0086291Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-12-18T01:09:56.0088523Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-12-18T01:09:56.0090536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-12-18T01:09:56.0092961Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-12-18T01:09:56.0095029Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-12-18T01:09:56.0096902Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-12-18T01:09:56.0098723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-12-18T01:09:56.0100743Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-12-18T01:09:56.0102858Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 103 <- wrt source file 2024-12-18T01:09:56.0105245Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-12-18T01:09:56.0107242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-12-18T01:09:56.0109579Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-12-18T01:09:56.0111762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-12-18T01:09:56.0114003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-12-18T01:09:56.0116037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-12-18T01:09:56.0118356Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-12-18T01:09:56.0120394Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-12-18T01:09:56.0123162Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-12-18T01:09:56.0125384Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-12-18T01:09:56.0129093Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-12-18T01:09:56.0131337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-12-18T01:09:56.0133817Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-12-18T01:09:56.0136180Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-12-18T01:09:56.0138492Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-12-18T01:09:56.0140790Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-12-18T01:09:56.0143195Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-12-18T01:09:56.0145450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-12-18T01:09:56.0147684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-12-18T01:09:56.0149910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-12-18T01:09:56.0152652Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-12-18T01:09:56.0154825Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-12-18T01:09:56.0157764Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-12-18T01:09:56.0159999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-12-18T01:09:56.0162502Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-12-18T01:09:56.0164735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-12-18T01:09:56.0167421Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-12-18T01:09:56.0169592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-12-18T01:09:56.0172735Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-12-18T01:09:56.0174918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-12-18T01:09:56.0177352Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-12-18T01:09:56.0179520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-12-18T01:09:56.0182398Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-12-18T01:09:56.0184744Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-12-18T01:09:56.0190481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-12-18T01:09:56.0192738Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-12-18T01:09:56.0195137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-12-18T01:09:56.0197701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-12-18T01:09:56.0200042Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-12-18T01:09:56.0202318Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-12-18T01:09:56.0204650Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-12-18T01:09:56.0206904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-12-18T01:09:56.0209347Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-12-18T01:09:56.0211628Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-12-18T01:09:56.0214213Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-12-18T01:09:56.0216610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-12-18T01:09:56.0219148Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-12-18T01:09:56.0221477Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-12-18T01:09:56.0223712Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-12-18T01:09:56.0225944Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-12-18T01:09:56.0228234Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-12-18T01:09:56.0230585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-12-18T01:09:56.0232928Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-12-18T01:09:56.0235178Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-12-18T01:09:56.0237641Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-12-18T01:09:56.0239865Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-12-18T01:09:56.0242136Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-12-18T01:09:56.0244814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-12-18T01:09:56.0247504Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-12-18T01:09:56.0250187Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-12-18T01:09:56.0252646Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-12-18T01:09:56.0255028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-12-18T01:09:56.0260287Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-12-18T01:09:56.0262938Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-12-18T01:09:56.0506608Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-12-18T01:09:56.0509328Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-12-18T01:09:56.3126433Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-12-18T01:09:56.3288736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-12-18T01:09:56.3310390Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-12-18T01:09:56.3312920Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-12-18T01:09:56.3315308Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-12-18T01:09:56.3317592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-12-18T01:09:56.3319809Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-12-18T01:09:56.3322222Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file 2024-12-18T01:09:56.3324625Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-12-18T01:09:56.3327344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file 2024-12-18T01:09:56.3330552Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-12-18T01:09:56.3333386Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file 2024-12-18T01:09:56.3335881Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-12-18T01:09:56.3338909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-12-18T01:09:56.3341600Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-12-18T01:09:56.3344758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-12-18T01:09:56.3347650Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-12-18T01:09:56.3350353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-12-18T01:09:56.3352924Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-12-18T01:09:56.3355749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-12-18T01:09:56.3358340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-12-18T01:09:56.3360720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-12-18T01:09:56.3381747Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-12-18T01:09:56.3383989Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-12-18T01:09:56.3459979Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-12-18T01:09:56.3462603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-12-18T01:09:56.3465176Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-12-18T01:09:56.3467544Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-12-18T01:09:56.3543565Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-12-18T01:09:56.3545955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-12-18T01:09:56.3552675Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-12-18T01:09:56.3554708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-12-18T01:09:56.3559516Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-12-18T01:09:56.3561674Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-12-18T01:09:56.3574119Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-12-18T01:09:56.3576408Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-12-18T01:09:56.3587442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-12-18T01:09:56.3590156Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-12-18T01:09:56.3779350Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-12-18T01:09:56.3782181Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-12-18T01:09:56.6364038Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-12-18T01:09:56.6524356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-12-18T01:09:56.6526720Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-12-18T01:09:56.6528939Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-12-18T01:09:56.6534401Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-12-18T01:09:56.6536830Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-12-18T01:09:56.6543392Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-12-18T01:09:56.6545546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-12-18T01:09:56.6565708Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-12-18T01:09:56.6567921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-12-18T01:09:56.6573591Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-12-18T01:09:56.6575686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-12-18T01:09:56.6601988Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-12-18T01:09:56.6604651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-12-18T01:09:56.6609548Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-12-18T01:09:56.6611813Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-12-18T01:09:56.6624882Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-12-18T01:09:56.6627099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 519 <- wrt source file 2024-12-18T01:09:56.6634288Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-12-18T01:09:56.6636569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 597 <- wrt source file 2024-12-18T01:09:56.6641593Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-12-18T01:09:56.6643699Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 679 <- wrt source file 2024-12-18T01:09:56.6648243Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-12-18T01:09:56.6650468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 750 <- wrt source file 2024-12-18T01:09:56.6660375Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-12-18T01:09:56.6663012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 943 <- wrt source file 2024-12-18T01:09:56.6668746Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-12-18T01:09:56.6671097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1263 <- wrt source file 2024-12-18T01:09:56.6677875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-12-18T01:09:56.6680396Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1403 <- wrt source file 2024-12-18T01:09:56.6688026Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-12-18T01:09:56.6690377Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1468 <- wrt source file 2024-12-18T01:09:56.6695630Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-12-18T01:09:56.6697931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1547 <- wrt source file 2024-12-18T01:09:56.6704356Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-12-18T01:09:56.6706659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1647 <- wrt source file 2024-12-18T01:09:56.6716745Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-12-18T01:09:56.6718961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1888 <- wrt source file 2024-12-18T01:09:56.6749274Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-12-18T01:09:56.6751569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 548 <- wrt source file 2024-12-18T01:09:56.6754052Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-12-18T01:09:56.6756384Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1008 <- wrt source file 2024-12-18T01:09:56.6764633Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-12-18T01:09:56.6766844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1262 <- wrt source file 2024-12-18T01:09:56.6771827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-12-18T01:09:56.6774095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2180 <- wrt source file 2024-12-18T01:09:56.6776489Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-12-18T01:09:56.6778845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2622 <- wrt source file 2024-12-18T01:09:56.6781225Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-12-18T01:09:56.6783854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2650 <- wrt source file 2024-12-18T01:09:56.6786348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-12-18T01:09:56.6788798Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2677 <- wrt source file 2024-12-18T01:09:56.6791124Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-12-18T01:09:56.6793572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2704 <- wrt source file 2024-12-18T01:09:56.6796016Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-12-18T01:09:56.6798424Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2735 <- wrt source file 2024-12-18T01:09:56.6800894Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-12-18T01:09:56.6803234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2759 <- wrt source file 2024-12-18T01:09:56.6805548Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-12-18T01:09:56.6807785Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2797 <- wrt source file 2024-12-18T01:09:56.6810062Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-12-18T01:09:56.6812513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-12-18T01:09:56.6825701Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-12-18T01:09:56.6828913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-12-18T01:09:56.6836424Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-12-18T01:09:56.6839372Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-12-18T01:09:56.6846965Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-12-18T01:09:56.6849659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-12-18T01:09:56.6854492Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-12-18T01:09:56.6856650Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-12-18T01:09:56.6862330Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-12-18T01:09:56.6864694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-12-18T01:09:56.6882552Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-12-18T01:09:56.6885116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-12-18T01:09:57.3481402Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-12-18T01:09:57.3778241Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-12-18T01:09:57.3789137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-12-18T01:09:57.3791722Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-12-18T01:09:57.3797292Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-12-18T01:09:57.3799608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-12-18T01:09:57.3823654Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-12-18T01:09:57.3825814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-12-18T01:09:57.3832123Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-12-18T01:09:57.3834512Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-12-18T01:09:57.3840229Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-12-18T01:09:57.3842509Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-12-18T01:09:57.3845715Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-12-18T01:09:57.3847967Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-12-18T01:09:57.3853828Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-12-18T01:09:57.3856243Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-12-18T01:09:57.3859916Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-12-18T01:09:57.3862300Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-12-18T01:09:57.9318744Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-12-18T01:09:57.9609697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-12-18T01:09:57.9619494Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-12-18T01:09:57.9621767Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-12-18T01:09:57.9625718Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-12-18T01:09:57.9628270Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-12-18T01:09:57.9651820Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-12-18T01:09:57.9654109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-12-18T01:09:57.9657928Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-12-18T01:09:57.9660626Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-12-18T01:09:57.9663587Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-12-18T01:09:57.9665930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-12-18T01:09:57.9670525Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-12-18T01:09:57.9672737Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-12-18T01:09:57.9726431Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-12-18T01:09:57.9728686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-12-18T01:09:58.2076607Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-12-18T01:09:58.2136887Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-12-18T01:09:58.2149948Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-12-18T01:09:58.2152205Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-12-18T01:09:58.3006422Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-12-18T01:09:58.3008571Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-12-18T01:09:58.3024370Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-12-18T01:09:58.3068043Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-12-18T01:09:58.3070441Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-12-18T01:09:58.3072627Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-12-18T01:09:58.4768634Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-12-18T01:09:58.4828535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-12-18T01:09:58.4880474Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-12-18T01:09:58.4883278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-12-18T01:09:58.5686280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-12-18T01:09:58.5688370Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-12-18T01:09:58.5697380Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-12-18T01:09:58.5752505Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-12-18T01:09:58.5755322Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-12-18T01:09:58.5757547Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-12-18T01:09:58.7984723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-12-18T01:09:58.8042814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-12-18T01:09:58.8049211Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-12-18T01:09:58.8051662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-12-18T01:09:58.8059328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-12-18T01:09:58.8061783Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-12-18T01:09:58.8091872Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-12-18T01:09:58.8094316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-12-18T01:09:58.8097143Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-12-18T01:09:58.8099664Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-12-18T01:09:58.8105766Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-12-18T01:09:58.8108361Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-12-18T01:09:58.8196600Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-12-18T01:09:58.8198796Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 591 <- wrt source file 2024-12-18T01:09:58.8209593Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-12-18T01:09:58.8211670Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 948 <- wrt source file 2024-12-18T01:09:58.8567002Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-12-18T01:09:58.8569735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file 2024-12-18T01:09:58.8584509Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-12-18T01:09:58.8587510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1536 <- wrt source file 2024-12-18T01:09:58.8598850Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-12-18T01:09:58.8601337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1658 <- wrt source file 2024-12-18T01:09:58.8611278Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-12-18T01:09:58.8613520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1772 <- wrt source file 2024-12-18T01:09:58.8625521Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-12-18T01:09:58.8627667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-12-18T01:09:58.8640814Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-12-18T01:09:58.8643095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2024-12-18T01:09:58.8647726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-12-18T01:09:58.8650234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2024-12-18T01:09:58.8656935Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-12-18T01:09:58.8659736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 86 <- wrt source file 2024-12-18T01:09:59.4665347Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-12-18T01:09:59.4787926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 254 <- wrt source file 2024-12-18T01:09:59.4790482Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-12-18T01:09:59.4792733Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 319 <- wrt source file 2024-12-18T01:09:59.5415062Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-12-18T01:09:59.5421499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 532 <- wrt source file 2024-12-18T01:09:59.6684051Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-12-18T01:09:59.6692103Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 653 <- wrt source file 2024-12-18T01:09:59.6910978Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-12-18T01:09:59.6931575Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 957 <- wrt source file 2024-12-18T01:09:59.7279157Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-12-18T01:09:59.7281629Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-12-18T01:09:59.7304413Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-12-18T01:09:59.7307143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-12-18T01:09:59.7316519Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-12-18T01:09:59.7319113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-12-18T01:09:59.7325276Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-12-18T01:09:59.7328297Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 126 <- wrt source file 2024-12-18T01:09:59.7330783Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-12-18T01:09:59.7333353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-12-18T01:09:59.7336234Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-12-18T01:09:59.7339004Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1418 <- wrt source file 2024-12-18T01:09:59.7341863Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-12-18T01:09:59.7344778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1981 <- wrt source file 2024-12-18T01:09:59.7347856Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-12-18T01:09:59.7350966Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1991 <- wrt source file 2024-12-18T01:09:59.7354046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-12-18T01:09:59.7357149Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2026 <- wrt source file 2024-12-18T01:09:59.7360357Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-12-18T01:09:59.7363182Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2024-12-18T01:09:59.7365789Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2024-12-18T01:09:59.7368375Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2024-12-18T01:09:59.7370507Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-12-18T01:09:59.7372769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-12-18T01:09:59.7375232Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-12-18T01:09:59.7377770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2024-12-18T01:09:59.7380246Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-12-18T01:09:59.7382693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2024-12-18T01:09:59.7385209Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-12-18T01:09:59.7387736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 506 <- wrt source file 2024-12-18T01:09:59.7390430Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-12-18T01:09:59.7392755Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file 2024-12-18T01:09:59.7394886Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-12-18T01:09:59.7397103Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 881 <- wrt source file 2024-12-18T01:09:59.7399477Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-12-18T01:09:59.7401745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 924 <- wrt source file 2024-12-18T01:09:59.7404003Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-12-18T01:09:59.7406148Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file 2024-12-18T01:09:59.7408267Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-12-18T01:09:59.7410360Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1219 <- wrt source file 2024-12-18T01:09:59.7412502Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-12-18T01:09:59.7414699Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 360 <- wrt source file 2024-12-18T01:09:59.7416954Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-12-18T01:09:59.7419142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 438 <- wrt source file 2024-12-18T01:09:59.7421309Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-12-18T01:09:59.7423445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 496 <- wrt source file 2024-12-18T01:09:59.7425787Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-12-18T01:09:59.7427918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 552 <- wrt source file 2024-12-18T01:09:59.7433701Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-12-18T01:09:59.7435889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 580 <- wrt source file 2024-12-18T01:09:59.7449774Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-12-18T01:09:59.7452107Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2024-12-18T01:09:59.7457137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-12-18T01:09:59.7459562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2024-12-18T01:09:59.7465798Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-12-18T01:09:59.7468711Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 214 <- wrt source file 2024-12-18T01:09:59.7471223Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-12-18T01:09:59.7473369Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2024-12-18T01:09:59.7479030Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-12-18T01:09:59.7481379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2024-12-18T01:09:59.7484271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-12-18T01:09:59.7486739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file 2024-12-18T01:09:59.7489354Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-12-18T01:09:59.7492199Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/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 2024-12-18T01:09:59.7584039Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2024-12-18T01:09:59.7586739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 309 <- wrt source file 2024-12-18T01:09:59.7589001Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-12-18T01:09:59.7591293Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 411 <- wrt source file 2024-12-18T01:09:59.7593670Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-12-18T01:09:59.7596204Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 511 <- wrt source file 2024-12-18T01:09:59.7598381Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-12-18T01:09:59.7600561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 571 <- wrt source file 2024-12-18T01:09:59.7602834Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-12-18T01:09:59.7605153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 636 <- wrt source file 2024-12-18T01:09:59.7607438Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-12-18T01:09:59.7609637Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2024-12-18T01:09:59.7728495Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-12-18T01:09:59.7731106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 249 <- wrt source file 2024-12-18T01:09:59.7733336Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2024-12-18T01:09:59.7735537Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 292 <- wrt source file 2024-12-18T01:09:59.7737913Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-12-18T01:09:59.7740150Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 334 <- wrt source file 2024-12-18T01:09:59.7742444Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-12-18T01:09:59.7744638Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 364 <- wrt source file 2024-12-18T01:09:59.7746822Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-12-18T01:09:59.7749051Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 399 <- wrt source file 2024-12-18T01:09:59.7751267Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-12-18T01:09:59.7753482Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 434 <- wrt source file 2024-12-18T01:09:59.7755760Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-12-18T01:09:59.7757925Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 471 <- wrt source file 2024-12-18T01:09:59.7760083Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-12-18T01:09:59.7762312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 847 <- wrt source file 2024-12-18T01:09:59.7764625Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-12-18T01:09:59.7766965Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 960 <- wrt source file 2024-12-18T01:09:59.7769052Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-12-18T01:09:59.7771444Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2024-12-18T01:09:59.7774196Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-12-18T01:09:59.7777104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file 2024-12-18T01:09:59.7780084Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-12-18T01:09:59.7782813Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file 2024-12-18T01:09:59.7785422Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-12-18T01:09:59.7787917Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 548 <- wrt source file 2024-12-18T01:09:59.7790456Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-12-18T01:09:59.7792916Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 750 <- wrt source file 2024-12-18T01:09:59.7795440Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-12-18T01:09:59.7797716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-12-18T01:09:59.7799857Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-12-18T01:09:59.7802278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 713 <- wrt source file 2024-12-18T01:09:59.8189791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2024-12-18T01:09:59.8192297Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-12-18T01:09:59.8194987Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-12-18T01:09:59.8197221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-12-18T01:09:59.8199247Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-12-18T01:09:59.8216405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 304 <- wrt source file 2024-12-18T01:09:59.8218952Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-12-18T01:09:59.8221310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2024-12-18T01:09:59.8223981Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-12-18T01:09:59.8226396Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2024-12-18T01:09:59.8228842Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-12-18T01:09:59.8231527Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2024-12-18T01:09:59.8234154Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-12-18T01:09:59.8236747Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 96 <- wrt source file 2024-12-18T01:09:59.8239277Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-12-18T01:09:59.8241899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 263 <- wrt source file 2024-12-18T01:09:59.8244608Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2024-12-18T01:09:59.8247247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 51 <- wrt source file 2024-12-18T01:09:59.8250313Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2024-12-18T01:09:59.8253304Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 197 <- wrt source file 2024-12-18T01:09:59.8268273Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2024-12-18T01:09:59.8271419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 87 <- wrt source file 2024-12-18T01:09:59.8274534Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-12-18T01:09:59.8277436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 48 <- wrt source file 2024-12-18T01:09:59.8280388Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-12-18T01:09:59.8283718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 98 <- wrt source file 2024-12-18T01:09:59.8286632Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-12-18T01:09:59.8289396Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 317 <- wrt source file 2024-12-18T01:09:59.8292241Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-12-18T01:09:59.8295305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 403 <- wrt source file 2024-12-18T01:09:59.8298464Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-12-18T01:09:59.8301472Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 613 <- wrt source file 2024-12-18T01:09:59.8304332Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-12-18T01:09:59.8307240Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 681 <- wrt source file 2024-12-18T01:09:59.8310220Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2024-12-18T01:09:59.8313108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2024-12-18T01:09:59.8316184Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2024-12-18T01:09:59.8319223Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 34 <- wrt source file 2024-12-18T01:09:59.8322293Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-12-18T01:09:59.8325181Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 62 <- wrt source file 2024-12-18T01:09:59.8328164Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-12-18T01:09:59.8330869Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 122 <- wrt source file 2024-12-18T01:09:59.8333604Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-12-18T01:09:59.8335273Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 189 <- wrt source file 2024-12-18T01:09:59.8337084Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-12-18T01:09:59.8338613Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 36 <- wrt source file 2024-12-18T01:09:59.8340442Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-12-18T01:09:59.8342408Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 24 <- wrt source file 2024-12-18T01:09:59.8344093Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-12-18T01:09:59.8345697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file 2024-12-18T01:09:59.8347432Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2024-12-18T01:09:59.8349971Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2024-12-18T01:09:59.8352546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2024-12-18T01:09:59.8355173Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 33 <- wrt source file 2024-12-18T01:09:59.8358188Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-12-18T01:09:59.8361028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 28 <- wrt source file 2024-12-18T01:09:59.8363683Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-12-18T01:09:59.8366269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 72 <- wrt source file 2024-12-18T01:09:59.8369119Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-12-18T01:09:59.8371908Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 28 <- wrt source file 2024-12-18T01:09:59.8374645Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-12-18T01:09:59.8377435Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file 2024-12-18T01:09:59.8380358Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-12-18T01:09:59.8383207Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 36 <- wrt source file 2024-12-18T01:09:59.8386022Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-12-18T01:09:59.8388758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2024-12-18T01:09:59.8391536Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-12-18T01:09:59.8394119Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2024-12-18T01:09:59.8396889Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-12-18T01:09:59.8399441Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2024-12-18T01:09:59.8401808Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-12-18T01:09:59.8404263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2024-12-18T01:09:59.8406776Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-12-18T01:09:59.8409449Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2024-12-18T01:09:59.8412191Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-12-18T01:09:59.8414855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2024-12-18T01:09:59.8417562Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-12-18T01:09:59.8420131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2024-12-18T01:09:59.8422895Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-12-18T01:09:59.8425446Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2024-12-18T01:09:59.8428127Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-12-18T01:09:59.8431221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2024-12-18T01:09:59.8433879Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-12-18T01:09:59.8436667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2024-12-18T01:09:59.8439540Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-12-18T01:09:59.8442102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2024-12-18T01:09:59.8444803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-12-18T01:09:59.8447448Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2024-12-18T01:09:59.8450120Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-12-18T01:09:59.8452545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2024-12-18T01:09:59.8455377Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-12-18T01:09:59.8457885Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2024-12-18T01:09:59.8460527Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-12-18T01:09:59.8463344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2024-12-18T01:09:59.8466081Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-12-18T01:09:59.8468853Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2024-12-18T01:09:59.8471469Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-12-18T01:09:59.8474003Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2024-12-18T01:09:59.8476563Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-12-18T01:09:59.8480423Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2024-12-18T01:09:59.8482798Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-12-18T01:09:59.8485027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2024-12-18T01:09:59.8487244Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-12-18T01:09:59.8488381Z ============ 2024-12-18T01:09:59.8488795Z Finished doctests 2024-12-18T01:09:59.8489148Z 338 / 705 passed 2024-12-18T01:09:59.8489524Z  2024-12-18T01:09:59.8489989Z === Found 105 parse-time warnings === 2024-12-18T01:09:59.8490616Z --- Parse Warning: 1 / 105 --- 2024-12-18T01:09:59.8492258Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py line=1496. 2024-12-18T01:09:59.8494108Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8494811Z 2024-12-18T01:09:59.8495166Z dim_order(ambiguity_check=False) -> tuple 2024-12-18T01:09:59.8495651Z 2024-12-18T01:09:59.8496174Z Returns the uniquely determined tuple of int describing the dim order or 2024-12-18T01:09:59.8496860Z physical layout of :attr:`self`. 2024-12-18T01:09:59.8497297Z 2024-12-18T01:09:59.8497734Z The dim order represents how dimensions are laid out in memory, 2024-12-18T01:09:59.8498449Z starting from the outermost to the innermost dimension. 2024-12-18T01:09:59.8499009Z 2024-12-18T01:09:59.8499458Z Note that the dim order may not always be uniquely determined. 2024-12-18T01:09:59.8500477Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2024-12-18T01:09:59.8501993Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2024-12-18T01:09:59.8503145Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2024-12-18T01:09:59.8504195Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2024-12-18T01:09:59.8505037Z Otherwise, it will raise TypeError. 2024-12-18T01:09:59.8505486Z 2024-12-18T01:09:59.8505776Z Args: 2024-12-18T01:09:59.8506410Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2024-12-18T01:09:59.8507177Z 2024-12-18T01:09:59.8507612Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2024-12-18T01:09:59.8508095Z (0, 1, 2, 3) 2024-12-18T01:09:59.8508632Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2024-12-18T01:09:59.8509161Z (0, 2, 1, 3) 2024-12-18T01:09:59.8509671Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2024-12-18T01:09:59.8510294Z (0, 2, 3, 1) 2024-12-18T01:09:59.8510683Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2024-12-18T01:09:59.8511149Z (0, 1, 2, 3) 2024-12-18T01:09:59.8511475Z >>> try: 2024-12-18T01:09:59.8511924Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2024-12-18T01:09:59.8512515Z ... except RuntimeError as e: 2024-12-18T01:09:59.8512970Z ... print(e) 2024-12-18T01:09:59.8513677Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2024-12-18T01:09:59.8514484Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2024-12-18T01:09:59.8515140Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2024-12-18T01:09:59.8515837Z ... ) # It can be mapped to contiguous format 2024-12-18T01:09:59.8516334Z (0, 1, 2, 3) 2024-12-18T01:09:59.8516677Z >>> try: 2024-12-18T01:09:59.8517206Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2024-12-18T01:09:59.8517826Z ... except TypeError as e: 2024-12-18T01:09:59.8518312Z ... print(e) 2024-12-18T01:09:59.8518956Z The ambiguity_check argument must be a bool or a list of memory formats. 2024-12-18T01:09:59.8519727Z .. warning:: 2024-12-18T01:09:59.8520270Z The dim_order tensor API is experimental and subject to change. 2024-12-18T01:09:59.8520959Z 2024-12-18T01:09:59.8521596Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8522399Z 2024-12-18T01:09:59.8522745Z warnings.warn(msg) 2024-12-18T01:09:59.8523130Z 2024-12-18T01:09:59.8523637Z --- Parse Warning: 2 / 105 --- 2024-12-18T01:09:59.8525392Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=431. 2024-12-18T01:09:59.8527560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8528656Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-12-18T01:09:59.8529378Z 2024-12-18T01:09:59.8529844Z This is helpful when you want to visualize data over some 2024-12-18T01:09:59.8530837Z range of inputs. See below for a plotting example. 2024-12-18T01:09:59.8531446Z 2024-12-18T01:09:59.8531856Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-12-18T01:09:59.8532581Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-12-18T01:09:59.8533423Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-12-18T01:09:59.8534224Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-12-18T01:09:59.8535088Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-12-18T01:09:59.8535650Z to the result shape. 2024-12-18T01:09:59.8536196Z 2024-12-18T01:09:59.8536532Z .. note:: 2024-12-18T01:09:59.8537041Z 0D inputs are treated equivalently to 1D inputs of a 2024-12-18T01:09:59.8537679Z single element. 2024-12-18T01:09:59.8538129Z 2024-12-18T01:09:59.8538461Z .. warning:: 2024-12-18T01:09:59.8539023Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-12-18T01:09:59.8539801Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-12-18T01:09:59.8540417Z 2024-12-18T01:09:59.8540846Z In the future `torch.meshgrid` will transition to 2024-12-18T01:09:59.8541586Z `indexing='xy'` as the default. 2024-12-18T01:09:59.8542015Z 2024-12-18T01:09:59.8542409Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-12-18T01:09:59.8543082Z this issue with the goal of migrating to NumPy's behavior. 2024-12-18T01:09:59.8543626Z 2024-12-18T01:09:59.8543962Z .. seealso:: 2024-12-18T01:09:59.8544364Z 2024-12-18T01:09:59.8544827Z :func:`torch.cartesian_prod` has the same effect but it 2024-12-18T01:09:59.8545536Z collects the data in a tensor of vectors. 2024-12-18T01:09:59.8546094Z 2024-12-18T01:09:59.8546422Z Args: 2024-12-18T01:09:59.8547103Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-12-18T01:09:59.8548051Z treated as tensors of size :math:`(1,)` automatically 2024-12-18T01:09:59.8548748Z 2024-12-18T01:09:59.8549246Z indexing: (str, optional): the indexing mode, either "xy" 2024-12-18T01:09:59.8550041Z or "ij", defaults to "ij". See warning for future changes. 2024-12-18T01:09:59.8550677Z 2024-12-18T01:09:59.8551119Z If "xy" is selected, the first dimension corresponds 2024-12-18T01:09:59.8551880Z to the cardinality of the second input and the second 2024-12-18T01:09:59.8552655Z dimension corresponds to the cardinality of the first 2024-12-18T01:09:59.8553311Z input. 2024-12-18T01:09:59.8553709Z 2024-12-18T01:09:59.8554066Z If "ij" is selected, the dimensions are in the same 2024-12-18T01:09:59.8554623Z order as the cardinality of the inputs. 2024-12-18T01:09:59.8555080Z 2024-12-18T01:09:59.8555351Z Returns: 2024-12-18T01:09:59.8555790Z seq (sequence of Tensors): If the input has :math:`N` 2024-12-18T01:09:59.8556454Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-12-18T01:09:59.8557238Z output will also have :math:`N` tensors, where each tensor 2024-12-18T01:09:59.8557960Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-12-18T01:09:59.8558509Z 2024-12-18T01:09:59.8558846Z Example:: 2024-12-18T01:09:59.8559225Z 2024-12-18T01:09:59.8559603Z >>> x = torch.tensor([1, 2, 3]) 2024-12-18T01:09:59.8560182Z >>> y = torch.tensor([4, 5, 6]) 2024-12-18T01:09:59.8560699Z 2024-12-18T01:09:59.8561202Z Observe the element-wise pairings across the grid, (1, 4), 2024-12-18T01:09:59.8561860Z (1, 5), ..., (3, 6). This is the same thing as the 2024-12-18T01:09:59.8562450Z cartesian product. 2024-12-18T01:09:59.8563067Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-12-18T01:09:59.8563708Z >>> grid_x 2024-12-18T01:09:59.8564147Z tensor([[1, 1, 1], 2024-12-18T01:09:59.8564628Z [2, 2, 2], 2024-12-18T01:09:59.8565082Z [3, 3, 3]]) 2024-12-18T01:09:59.8565557Z >>> grid_y 2024-12-18T01:09:59.8566157Z tensor([[4, 5, 6], 2024-12-18T01:09:59.8566584Z [4, 5, 6], 2024-12-18T01:09:59.8566940Z [4, 5, 6]]) 2024-12-18T01:09:59.8567312Z 2024-12-18T01:09:59.8567695Z This correspondence can be seen when these grids are 2024-12-18T01:09:59.8568229Z stacked properly. 2024-12-18T01:09:59.8568784Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-12-18T01:09:59.8569506Z ... torch.cartesian_prod(x, y)) 2024-12-18T01:09:59.8570049Z True 2024-12-18T01:09:59.8570428Z 2024-12-18T01:09:59.8570902Z `torch.meshgrid` is commonly used to produce a grid for 2024-12-18T01:09:59.8571567Z plotting. 2024-12-18T01:09:59.8572144Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-12-18T01:09:59.8572770Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-12-18T01:09:59.8573392Z >>> import matplotlib.pyplot as plt 2024-12-18T01:09:59.8574013Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-12-18T01:09:59.8574627Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-12-18T01:09:59.8575245Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-12-18T01:09:59.8575874Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-12-18T01:09:59.8576492Z >>> ax = plt.axes(projection='3d') 2024-12-18T01:09:59.8577158Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-12-18T01:09:59.8577776Z >>> plt.show() 2024-12-18T01:09:59.8578218Z 2024-12-18T01:09:59.8578593Z .. image:: ../_static/img/meshgrid.png 2024-12-18T01:09:59.8579090Z :width: 512 2024-12-18T01:09:59.8579440Z 2024-12-18T01:09:59.8579707Z 2024-12-18T01:09:59.8580234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8580857Z 2024-12-18T01:09:59.8581153Z warnings.warn(msg) 2024-12-18T01:09:59.8581538Z 2024-12-18T01:09:59.8582092Z --- Parse Warning: 3 / 105 --- 2024-12-18T01:09:59.8584020Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=820. 2024-12-18T01:09:59.8586186Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8587504Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-12-18T01:09:59.8588607Z 2024-12-18T01:09:59.8589046Z Returns the unique elements of the input tensor. 2024-12-18T01:09:59.8589636Z 2024-12-18T01:09:59.8590332Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-12-18T01:09:59.8591389Z this function also eliminates non-consecutive duplicate values. 2024-12-18T01:09:59.8591966Z 2024-12-18T01:09:59.8592434Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-12-18T01:09:59.8593319Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-12-18T01:09:59.8594408Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-12-18T01:09:59.8595417Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-12-18T01:09:59.8596048Z 2024-12-18T01:09:59.8596372Z Args: 2024-12-18T01:09:59.8596762Z input (Tensor): the input tensor 2024-12-18T01:09:59.8597457Z sorted (bool): Whether to sort the unique elements in ascending order 2024-12-18T01:09:59.8598230Z before returning as output. 2024-12-18T01:09:59.8598945Z return_inverse (bool): Whether to also return the indices for where 2024-12-18T01:09:59.8599983Z elements in the original input ended up in the returned unique list. 2024-12-18T01:09:59.8601032Z return_counts (bool): Whether to also return the counts for each unique 2024-12-18T01:09:59.8601784Z element. 2024-12-18T01:09:59.8602407Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-12-18T01:09:59.8603321Z unique of the flattened input is returned. Otherwise, each of the 2024-12-18T01:09:59.8604145Z tensors indexed by the given dimension is treated as one of the 2024-12-18T01:09:59.8604887Z elements to apply the unique operation upon. See examples for more 2024-12-18T01:09:59.8605507Z details. Default: ``None`` 2024-12-18T01:09:59.8605922Z 2024-12-18T01:09:59.8606276Z Returns: 2024-12-18T01:09:59.8606973Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-12-18T01:09:59.8607822Z 2024-12-18T01:09:59.8608340Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-12-18T01:09:59.8609118Z - **inverse_indices** (*Tensor*): (optional) if 2024-12-18T01:09:59.8609867Z :attr:`return_inverse` is True, there will be an additional 2024-12-18T01:09:59.8610736Z returned tensor (same shape as input) representing the indices 2024-12-18T01:09:59.8611670Z for where elements in the original input map to in the output; 2024-12-18T01:09:59.8612538Z otherwise, this function will only return a single tensor. 2024-12-18T01:09:59.8613271Z - **counts** (*Tensor*): (optional) if 2024-12-18T01:09:59.8613977Z :attr:`return_counts` is True, there will be an additional 2024-12-18T01:09:59.8614816Z returned tensor (same shape as output or output.size(dim), 2024-12-18T01:09:59.8615664Z if dim was specified) representing the number of occurrences 2024-12-18T01:09:59.8616390Z for each unique value or tensor. 2024-12-18T01:09:59.8616830Z 2024-12-18T01:09:59.8617113Z Example:: 2024-12-18T01:09:59.8617421Z 2024-12-18T01:09:59.8617857Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-12-18T01:09:59.8618451Z >>> output 2024-12-18T01:09:59.8618795Z tensor([1, 2, 3]) 2024-12-18T01:09:59.8619210Z 2024-12-18T01:09:59.8619610Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:09:59.8620408Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:09:59.8621181Z >>> output 2024-12-18T01:09:59.8621593Z tensor([1, 2, 3]) 2024-12-18T01:09:59.8622064Z >>> inverse_indices 2024-12-18T01:09:59.8622535Z tensor([0, 2, 1, 2]) 2024-12-18T01:09:59.8622973Z 2024-12-18T01:09:59.8623369Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:09:59.8624199Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:09:59.8624956Z >>> output 2024-12-18T01:09:59.8625359Z tensor([1, 2, 3]) 2024-12-18T01:09:59.8625793Z >>> inverse_indices 2024-12-18T01:09:59.8626253Z tensor([[0, 2], 2024-12-18T01:09:59.8626694Z [1, 2]]) 2024-12-18T01:09:59.8627111Z 2024-12-18T01:09:59.8627455Z >>> a = torch.tensor([ 2024-12-18T01:09:59.8627910Z ... [ 2024-12-18T01:09:59.8628390Z ... [1, 1, 0, 0], 2024-12-18T01:09:59.8628872Z ... [1, 1, 0, 0], 2024-12-18T01:09:59.8629260Z ... [0, 0, 1, 1], 2024-12-18T01:09:59.8629642Z ... ], 2024-12-18T01:09:59.8629951Z ... [ 2024-12-18T01:09:59.8630280Z ... [0, 0, 1, 1], 2024-12-18T01:09:59.8630959Z ... [0, 0, 1, 1], 2024-12-18T01:09:59.8631482Z ... [1, 1, 1, 1], 2024-12-18T01:09:59.8631944Z ... ], 2024-12-18T01:09:59.8632324Z ... [ 2024-12-18T01:09:59.8632718Z ... [1, 1, 0, 0], 2024-12-18T01:09:59.8633202Z ... [1, 1, 0, 0], 2024-12-18T01:09:59.8633676Z ... [0, 0, 1, 1], 2024-12-18T01:09:59.8634151Z ... ], 2024-12-18T01:09:59.8634528Z ... ]) 2024-12-18T01:09:59.8634882Z 2024-12-18T01:09:59.8635362Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-12-18T01:09:59.8636422Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-12-18T01:09:59.8637213Z >>> # each other, so one of them will be removed. 2024-12-18T01:09:59.8637944Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-12-18T01:09:59.8638472Z tensor(True) 2024-12-18T01:09:59.8638960Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-12-18T01:09:59.8639536Z >>> a_unique_dim0 2024-12-18T01:09:59.8639996Z tensor([[[0, 0, 1, 1], 2024-12-18T01:09:59.8640459Z [0, 0, 1, 1], 2024-12-18T01:09:59.8640922Z [1, 1, 1, 1]], 2024-12-18T01:09:59.8641398Z [[1, 1, 0, 0], 2024-12-18T01:09:59.8641860Z [1, 1, 0, 0], 2024-12-18T01:09:59.8642327Z [0, 0, 1, 1]]]) 2024-12-18T01:09:59.8642777Z 2024-12-18T01:09:59.8643251Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-12-18T01:09:59.8643843Z >>> # `a_unique_dim0`: 2024-12-18T01:09:59.8644273Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-12-18T01:09:59.8644723Z tensor(True) 2024-12-18T01:09:59.8645102Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-12-18T01:09:59.8645555Z tensor(True) 2024-12-18T01:09:59.8645947Z 2024-12-18T01:09:59.8646469Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-12-18T01:09:59.8647337Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-12-18T01:09:59.8648013Z >>> # them will be removed. 2024-12-18T01:09:59.8648530Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-12-18T01:09:59.8649039Z tensor(True) 2024-12-18T01:09:59.8649484Z >>> torch.unique(a, dim=1) 2024-12-18T01:09:59.8650006Z tensor([[[0, 0, 1, 1], 2024-12-18T01:09:59.8650463Z [1, 1, 0, 0]], 2024-12-18T01:09:59.8650932Z [[1, 1, 1, 1], 2024-12-18T01:09:59.8651397Z [0, 0, 1, 1]], 2024-12-18T01:09:59.8651863Z [[0, 0, 1, 1], 2024-12-18T01:09:59.8652325Z [1, 1, 0, 0]]]) 2024-12-18T01:09:59.8652768Z 2024-12-18T01:09:59.8653309Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-12-18T01:09:59.8654159Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-12-18T01:09:59.8654926Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-12-18T01:09:59.8655618Z >>> # sub-tensors will be removed. 2024-12-18T01:09:59.8656074Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-12-18T01:09:59.8656483Z tensor(True) 2024-12-18T01:09:59.8656848Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-12-18T01:09:59.8657275Z tensor(True) 2024-12-18T01:09:59.8657646Z >>> torch.unique(a, dim=2) 2024-12-18T01:09:59.8658070Z tensor([[[0, 1], 2024-12-18T01:09:59.8658508Z [0, 1], 2024-12-18T01:09:59.8658935Z [1, 0]], 2024-12-18T01:09:59.8659336Z [[1, 0], 2024-12-18T01:09:59.8659779Z [1, 0], 2024-12-18T01:09:59.8660212Z [1, 1]], 2024-12-18T01:09:59.8660662Z [[0, 1], 2024-12-18T01:09:59.8661287Z [0, 1], 2024-12-18T01:09:59.8661734Z [1, 0]]]) 2024-12-18T01:09:59.8662181Z 2024-12-18T01:09:59.8662823Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8663654Z 2024-12-18T01:09:59.8664014Z warnings.warn(msg) 2024-12-18T01:09:59.8664452Z 2024-12-18T01:09:59.8665025Z --- Parse Warning: 4 / 105 --- 2024-12-18T01:09:59.8666896Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-12-18T01:09:59.8668775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8669546Z 2024-12-18T01:09:59.8670004Z Load a model from a github repo or a local directory. 2024-12-18T01:09:59.8670633Z 2024-12-18T01:09:59.8671224Z Note: Loading a model is the typical use case, but this can also be used to 2024-12-18T01:09:59.8672212Z for loading other objects such as tokenizers, loss functions, etc. 2024-12-18T01:09:59.8672949Z 2024-12-18T01:09:59.8673437Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-12-18T01:09:59.8674237Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-12-18T01:09:59.8674916Z ref (a tag or a branch). 2024-12-18T01:09:59.8675360Z 2024-12-18T01:09:59.8675828Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-12-18T01:09:59.8676503Z path to a local directory. 2024-12-18T01:09:59.8676973Z 2024-12-18T01:09:59.8677306Z Args: 2024-12-18T01:09:59.8677680Z repo_or_dir (str): If ``source`` is 'github', 2024-12-18T01:09:59.8678638Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-12-18T01:09:59.8679740Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-12-18T01:09:59.8680739Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-12-18T01:09:59.8681653Z If ``source`` is 'local' then it should be a path to a local directory. 2024-12-18T01:09:59.8682572Z model (str): the name of a callable (entrypoint) defined in the 2024-12-18T01:09:59.8683298Z repo/dir's ``hubconf.py``. 2024-12-18T01:09:59.8684014Z *args (optional): the corresponding args for callable ``model``. 2024-12-18T01:09:59.8684896Z source (str, optional): 'github' or 'local'. Specifies how 2024-12-18T01:09:59.8685702Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-12-18T01:09:59.8686593Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-12-18T01:09:59.8687586Z This parameter was introduced in v1.12 and helps ensuring that users 2024-12-18T01:09:59.8688438Z only run code from repos that they trust. 2024-12-18T01:09:59.8689023Z 2024-12-18T01:09:59.8689551Z - If ``False``, a prompt will ask the user whether the repo should 2024-12-18T01:09:59.8690267Z be trusted. 2024-12-18T01:09:59.8690780Z - If ``True``, the repo will be added to the trusted list and loaded 2024-12-18T01:09:59.8691433Z without requiring explicit confirmation. 2024-12-18T01:09:59.8692042Z - If ``"check"``, the repo will be checked against the list of 2024-12-18T01:09:59.8692810Z trusted repos in the cache. If it is not present in that list, the 2024-12-18T01:09:59.8693753Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-12-18T01:09:59.8694647Z - If ``None``: this will raise a warning, inviting the user to set 2024-12-18T01:09:59.8695503Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-12-18T01:09:59.8696434Z is only present for backward compatibility and will be removed in 2024-12-18T01:09:59.8697285Z v2.0. 2024-12-18T01:09:59.8697684Z 2024-12-18T01:09:59.8698232Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-12-18T01:09:59.8699131Z force_reload (bool, optional): whether to force a fresh download of 2024-12-18T01:09:59.8699893Z the github repo unconditionally. Does not have any effect if 2024-12-18T01:09:59.8700515Z ``source = 'local'``. Default is ``False``. 2024-12-18T01:09:59.8701217Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-12-18T01:09:59.8702184Z local caches. Note that the message about first download cannot be 2024-12-18T01:09:59.8703095Z muted. Does not have any effect if ``source = 'local'``. 2024-12-18T01:09:59.8703837Z Default is ``True``. 2024-12-18T01:09:59.8704508Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-12-18T01:09:59.8705235Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-12-18T01:09:59.8705916Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-12-18T01:09:59.8706494Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-12-18T01:09:59.8707168Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-12-18T01:09:59.8707644Z 2024-12-18T01:09:59.8707854Z Returns: 2024-12-18T01:09:59.8708187Z The output of the ``model`` callable when called with the given 2024-12-18T01:09:59.8708698Z ``*args`` and ``**kwargs``. 2024-12-18T01:09:59.8708990Z 2024-12-18T01:09:59.8709184Z Example: 2024-12-18T01:09:59.8709463Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:09:59.8709817Z >>> # from a github repo 2024-12-18T01:09:59.8710117Z >>> repo = "pytorch/vision" 2024-12-18T01:09:59.8710428Z >>> model = torch.hub.load( 2024-12-18T01:09:59.8710816Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-12-18T01:09:59.8711216Z ... ) 2024-12-18T01:09:59.8711458Z >>> # from a local directory 2024-12-18T01:09:59.8711805Z >>> path = "/some/local/path/pytorch/vision" 2024-12-18T01:09:59.8712156Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.8712574Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-12-18T01:09:59.8713029Z 2024-12-18T01:09:59.8713403Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8713861Z 2024-12-18T01:09:59.8714078Z warnings.warn(msg) 2024-12-18T01:09:59.8714324Z 2024-12-18T01:09:59.8714746Z --- Parse Warning: 5 / 105 --- 2024-12-18T01:09:59.8715851Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687. 2024-12-18T01:09:59.8717070Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8717609Z Download object at the given URL to a local path. 2024-12-18T01:09:59.8717967Z 2024-12-18T01:09:59.8718164Z Args: 2024-12-18T01:09:59.8718436Z url (str): URL of the object to download 2024-12-18T01:09:59.8718921Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-12-18T01:09:59.8719618Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-12-18T01:09:59.8720177Z Default: None 2024-12-18T01:09:59.8720607Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-12-18T01:09:59.8721088Z Default: True 2024-12-18T01:09:59.8721364Z 2024-12-18T01:09:59.8721581Z Example: 2024-12-18T01:09:59.8722008Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:09:59.8722373Z >>> # xdoctest: +REQUIRES(POSIX) 2024-12-18T01:09:59.8722732Z >>> torch.hub.download_url_to_file( 2024-12-18T01:09:59.8723206Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-12-18T01:09:59.8723665Z ... "/tmp/temporary_file", 2024-12-18T01:09:59.8723981Z ... ) 2024-12-18T01:09:59.8724199Z 2024-12-18T01:09:59.8724403Z 2024-12-18T01:09:59.8724780Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8725292Z 2024-12-18T01:09:59.8725506Z warnings.warn(msg) 2024-12-18T01:09:59.8725754Z 2024-12-18T01:09:59.8726076Z --- Parse Warning: 6 / 105 --- 2024-12-18T01:09:59.8727235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812. 2024-12-18T01:09:59.8728489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8729032Z Loads the Torch serialized object at the given URL. 2024-12-18T01:09:59.8729388Z 2024-12-18T01:09:59.8729681Z If downloaded file is a zip file, it will be automatically 2024-12-18T01:09:59.8730081Z decompressed. 2024-12-18T01:09:59.8730331Z 2024-12-18T01:09:59.8730945Z If the object is already present in `model_dir`, it's deserialized and 2024-12-18T01:09:59.8731383Z returned. 2024-12-18T01:09:59.8731736Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-12-18T01:09:59.8732278Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-12-18T01:09:59.8732683Z 2024-12-18T01:09:59.8732890Z Args: 2024-12-18T01:09:59.8733157Z url (str): URL of the object to download 2024-12-18T01:09:59.8733592Z model_dir (str, optional): directory in which to save the object 2024-12-18T01:09:59.8734262Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-12-18T01:09:59.8734989Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-12-18T01:09:59.8735466Z Default: True 2024-12-18T01:09:59.8735975Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-12-18T01:09:59.8736866Z ``filename-.ext`` where ```` is the first eight or more 2024-12-18T01:09:59.8737432Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-12-18T01:09:59.8737988Z ensure unique names and to verify the contents of the file. 2024-12-18T01:09:59.8738401Z Default: False 2024-12-18T01:09:59.8738921Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-12-18T01:09:59.8739705Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-12-18T01:09:59.8740407Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-12-18T01:09:59.8740861Z 2024-12-18T01:09:59.8741070Z Example: 2024-12-18T01:09:59.8741357Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:09:59.8741763Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-12-18T01:09:59.8742256Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-12-18T01:09:59.8742672Z ... ) 2024-12-18T01:09:59.8742903Z 2024-12-18T01:09:59.8743107Z 2024-12-18T01:09:59.8743489Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8743947Z 2024-12-18T01:09:59.8744152Z warnings.warn(msg) 2024-12-18T01:09:59.8744550Z 2024-12-18T01:09:59.8744897Z --- Parse Warning: 7 / 105 --- 2024-12-18T01:09:59.8745989Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=360. 2024-12-18T01:09:59.8747199Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:59.8747802Z Registers the function implementation as the fallback for the given key. 2024-12-18T01:09:59.8748250Z 2024-12-18T01:09:59.8748682Z This function only works for a library with global namespace ("_"). 2024-12-18T01:09:59.8749107Z 2024-12-18T01:09:59.8749312Z Args: 2024-12-18T01:09:59.8749819Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-12-18T01:09:59.8750344Z to register a fallthrough. 2024-12-18T01:09:59.8750904Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-12-18T01:09:59.8751519Z the dispatch key that the library was created with. 2024-12-18T01:09:59.8752177Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-12-18T01:09:59.8752993Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-12-18T01:09:59.8753534Z 2024-12-18T01:09:59.8753746Z Example:: 2024-12-18T01:09:59.8754026Z >>> my_lib = Library("_", "IMPL") 2024-12-18T01:09:59.8754396Z >>> def fallback_kernel(op, *args, **kwargs): 2024-12-18T01:09:59.8754787Z >>> # Handle all autocast ops generically 2024-12-18T01:09:59.8755132Z >>> # ... 2024-12-18T01:09:59.8755449Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:09:59.8755805Z 2024-12-18T01:09:59.8756562Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-12-18T01:09:59.8757385Z 2024-12-18T01:09:59.8757639Z my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:09:59.8757963Z ^ 2024-12-18T01:09:59.8758187Z warnings.warn(msg) 2024-12-18T01:09:59.8758450Z 2024-12-18T01:09:59.8758773Z --- Parse Warning: 8 / 105 --- 2024-12-18T01:09:59.8759854Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=725. 2024-12-18T01:09:59.8761048Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:59.8761638Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-12-18T01:09:59.8762080Z 2024-12-18T01:09:59.8762386Z Also sometimes known as a "meta kernel", "abstract impl". 2024-12-18T01:09:59.8762774Z 2024-12-18T01:09:59.8763140Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-12-18T01:09:59.8763720Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-12-18T01:09:59.8764310Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-12-18T01:09:59.8764820Z what the properties of the output Tensors are. 2024-12-18T01:09:59.8765168Z 2024-12-18T01:09:59.8765524Z The FakeTensor implementation has the same signature as the operator. 2024-12-18T01:09:59.8766084Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-12-18T01:09:59.8766636Z implementation, assume that all Tensor inputs to the operator are 2024-12-18T01:09:59.8767182Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-12-18T01:09:59.8767789Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-12-18T01:09:59.8768354Z The FakeTensor implementation must consist of only PyTorch operations 2024-12-18T01:09:59.8768907Z (and may not directly access the storage or data of any input or 2024-12-18T01:09:59.8769311Z intermediate Tensors). 2024-12-18T01:09:59.8769592Z 2024-12-18T01:09:59.8769866Z This API may be used as a decorator (see examples). 2024-12-18T01:09:59.8770221Z 2024-12-18T01:09:59.8770479Z For a detailed guide on custom ops, please see 2024-12-18T01:09:59.8770961Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-12-18T01:09:59.8771396Z 2024-12-18T01:09:59.8771659Z Examples: 2024-12-18T01:09:59.8771905Z >>> import torch 2024-12-18T01:09:59.8772193Z >>> import numpy as np 2024-12-18T01:09:59.8772495Z >>> from torch import Tensor 2024-12-18T01:09:59.8772810Z >>> 2024-12-18T01:09:59.8773149Z >>> # Example 1: an operator without data-dependent output shape 2024-12-18T01:09:59.8773685Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-12-18T01:09:59.8774242Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-12-18T01:09:59.8774761Z >>> raise NotImplementedError("Implementation goes here") 2024-12-18T01:09:59.8775159Z >>> 2024-12-18T01:09:59.8775473Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-12-18T01:09:59.8775992Z >>> def _(x, weight, bias): 2024-12-18T01:09:59.8776314Z >>> assert x.dim() == 2 2024-12-18T01:09:59.8776635Z >>> assert weight.dim() == 2 2024-12-18T01:09:59.8776970Z >>> assert bias.dim() == 1 2024-12-18T01:09:59.8777318Z >>> assert x.shape[1] == weight.shape[1] 2024-12-18T01:09:59.8777700Z >>> assert weight.shape[0] == bias.shape[0] 2024-12-18T01:09:59.8778077Z >>> assert x.device == weight.device 2024-12-18T01:09:59.8778404Z >>> 2024-12-18T01:09:59.8778650Z >>> return (x @ weight.t()) + bias 2024-12-18T01:09:59.8778971Z >>> 2024-12-18T01:09:59.8779286Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-12-18T01:09:59.8779689Z >>> x = torch.randn(2, 3) 2024-12-18T01:09:59.8780012Z >>> w = torch.randn(3, 3) 2024-12-18T01:09:59.8780315Z >>> b = torch.randn(3) 2024-12-18T01:09:59.8780666Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-12-18T01:09:59.8781013Z >>> 2024-12-18T01:09:59.8781267Z >>> assert y.shape == (2, 3) 2024-12-18T01:09:59.8781570Z >>> 2024-12-18T01:09:59.8781872Z >>> # Example 2: an operator with data-dependent output shape 2024-12-18T01:09:59.8782396Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-12-18T01:09:59.8782873Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-12-18T01:09:59.8783235Z >>> x_np = x.numpy(force=True) 2024-12-18T01:09:59.8783597Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-12-18T01:09:59.8783974Z >>> return torch.tensor(res, device=x.device) 2024-12-18T01:09:59.8784321Z >>> 2024-12-18T01:09:59.8784637Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-12-18T01:09:59.8785023Z >>> def _(x): 2024-12-18T01:09:59.8785345Z >>> # Number of nonzero-elements is data-dependent. 2024-12-18T01:09:59.8785754Z >>> # Since we cannot peek at the data in an fake impl, 2024-12-18T01:09:59.8786196Z >>> # we use the ctx object to construct a new symint that 2024-12-18T01:09:59.8786714Z >>> # represents the data-dependent size. 2024-12-18T01:09:59.8787145Z >>> ctx = torch.library.get_ctx() 2024-12-18T01:09:59.8787528Z >>> nnz = ctx.new_dynamic_size() 2024-12-18T01:09:59.8787870Z >>> shape = [nnz, x.dim()] 2024-12-18T01:09:59.8788229Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-12-18T01:09:59.8788670Z >>> return result 2024-12-18T01:09:59.8788959Z >>> 2024-12-18T01:09:59.8789284Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:09:59.8789676Z >>> 2024-12-18T01:09:59.8789921Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-12-18T01:09:59.8790400Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-12-18T01:09:59.8790883Z >>> trace.print_readable() 2024-12-18T01:09:59.8791279Z >>> 2024-12-18T01:09:59.8791646Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-12-18T01:09:59.8792066Z 2024-12-18T01:09:59.8792278Z 2024-12-18T01:09:59.8792930Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-12-18T01:09:59.8793651Z 2024-12-18T01:09:59.8793857Z _._ = None 2024-12-18T01:09:59.8794067Z ^ 2024-12-18T01:09:59.8794286Z warnings.warn(msg) 2024-12-18T01:09:59.8794545Z 2024-12-18T01:09:59.8794905Z --- Parse Warning: 9 / 105 --- 2024-12-18T01:09:59.8796002Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=846. 2024-12-18T01:09:59.8797237Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8797760Z Register a backward formula for this custom op. 2024-12-18T01:09:59.8798109Z 2024-12-18T01:09:59.8798438Z In order for an operator to work with autograd, you need to register 2024-12-18T01:09:59.8798874Z a backward formula: 2024-12-18T01:09:59.8799272Z 1. You must tell us how to compute gradients during the backward pass 2024-12-18T01:09:59.8799714Z by providing us a "backward" function. 2024-12-18T01:09:59.8800174Z 2. If you need any values from the forward to compute gradients, you can 2024-12-18T01:09:59.8800662Z use `setup_context` to save values for backward. 2024-12-18T01:09:59.8801014Z 2024-12-18T01:09:59.8801364Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-12-18T01:09:59.8801900Z - ``grads`` is one or more gradients. The number of gradients matches 2024-12-18T01:09:59.8802353Z the number of outputs of the operator. 2024-12-18T01:09:59.8802820Z The ``ctx`` object is `the same ctx object `_ used by 2024-12-18T01:09:59.8803410Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-12-18T01:09:59.8803942Z same as :meth:`torch.autograd.Function.backward`. 2024-12-18T01:09:59.8804309Z 2024-12-18T01:09:59.8804635Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-12-18T01:09:59.8805206Z Please save quantities needed for backward onto the ``ctx`` object via 2024-12-18T01:09:59.8805794Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-12-18T01:09:59.8806349Z or assigning them as attributes of ``ctx``. If your custom op has 2024-12-18T01:09:59.8806882Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-12-18T01:09:59.8807398Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-12-18T01:09:59.8807796Z 2024-12-18T01:09:59.8808137Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-12-18T01:09:59.8808701Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-12-18T01:09:59.8809349Z not depend on or mutate global state. If you need a non-traceable backward, 2024-12-18T01:09:59.8809931Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-12-18T01:09:59.8810352Z 2024-12-18T01:09:59.8810693Z If you need different autograd behavior on different devices, then we 2024-12-18T01:09:59.8811267Z recommend creating two different custom operators, one for each device 2024-12-18T01:09:59.8811850Z that needs different behavior, and switching between them at runtime. 2024-12-18T01:09:59.8812284Z 2024-12-18T01:09:59.8812483Z Examples: 2024-12-18T01:09:59.8812731Z >>> import torch 2024-12-18T01:09:59.8813019Z >>> import numpy as np 2024-12-18T01:09:59.8813384Z >>> from torch import Tensor 2024-12-18T01:09:59.8813694Z >>> 2024-12-18T01:09:59.8814034Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-12-18T01:09:59.8814490Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-12-18T01:09:59.8814840Z >>> x_np = x.cpu().numpy() 2024-12-18T01:09:59.8815165Z >>> y_np = np.sin(x_np) 2024-12-18T01:09:59.8815537Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:09:59.8815894Z >>> 2024-12-18T01:09:59.8816194Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-12-18T01:09:59.8816563Z >>> x, = inputs 2024-12-18T01:09:59.8816862Z >>> ctx.save_for_backward(x) 2024-12-18T01:09:59.8817176Z >>> 2024-12-18T01:09:59.8817412Z >>> def backward(ctx, grad): 2024-12-18T01:09:59.8817736Z >>> x, = ctx.saved_tensors 2024-12-18T01:09:59.8818057Z >>> return grad * x.cos() 2024-12-18T01:09:59.8818361Z >>> 2024-12-18T01:09:59.8818624Z >>> torch.library.register_autograd( 2024-12-18T01:09:59.8819046Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-12-18T01:09:59.8819430Z ... ) 2024-12-18T01:09:59.8819664Z >>> 2024-12-18T01:09:59.8819932Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:09:59.8820275Z >>> y = numpy_sin(x) 2024-12-18T01:09:59.8820640Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:09:59.8821048Z >>> assert torch.allclose(grad_x, x.cos()) 2024-12-18T01:09:59.8821380Z >>> 2024-12-18T01:09:59.8821639Z >>> # Example with a keyword-only arg 2024-12-18T01:09:59.8822080Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:09:59.8822560Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-12-18T01:09:59.8822931Z >>> x_np = x.cpu().numpy() 2024-12-18T01:09:59.8823256Z >>> y_np = x_np * val 2024-12-18T01:09:59.8823620Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:09:59.8823986Z >>> 2024-12-18T01:09:59.8824348Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-12-18T01:09:59.8824813Z >>> ctx.val = keyword_only_inputs["val"] 2024-12-18T01:09:59.8825143Z >>> 2024-12-18T01:09:59.8825391Z >>> def backward(ctx, grad): 2024-12-18T01:09:59.8825714Z >>> return grad * ctx.val 2024-12-18T01:09:59.8826017Z >>> 2024-12-18T01:09:59.8826268Z >>> torch.library.register_autograd( 2024-12-18T01:09:59.8826687Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-12-18T01:09:59.8827077Z ... ) 2024-12-18T01:09:59.8827314Z >>> 2024-12-18T01:09:59.8827587Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:09:59.8827931Z >>> y = numpy_mul(x, val=3.14) 2024-12-18T01:09:59.8828412Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:09:59.8828972Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-12-18T01:09:59.8829360Z 2024-12-18T01:09:59.8829568Z 2024-12-18T01:09:59.8829935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8830620Z 2024-12-18T01:09:59.8830847Z warnings.warn(msg) 2024-12-18T01:09:59.8831107Z 2024-12-18T01:09:59.8831461Z --- Parse Warning: 10 / 105 --- 2024-12-18T01:09:59.8832517Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1258. 2024-12-18T01:09:59.8833714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8834397Z Given an operator and some sample arguments, tests if the operator is 2024-12-18T01:09:59.8859274Z registered correctly. 2024-12-18T01:09:59.8859616Z 2024-12-18T01:09:59.8859964Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-12-18T01:09:59.8860531Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-12-18T01:09:59.8861113Z and these APIs require that the functions you pass them satisfy certain 2024-12-18T01:09:59.8861677Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-12-18T01:09:59.8862163Z ``opcheck`` tests these metadata and properties. 2024-12-18T01:09:59.8862498Z 2024-12-18T01:09:59.8862725Z Concretely, we test the following: 2024-12-18T01:09:59.8863024Z 2024-12-18T01:09:59.8863336Z - test_schema: If the schema matches the implementation of 2024-12-18T01:09:59.8863869Z the operator. For example: if the schema specifies a Tensor is mutated, 2024-12-18T01:09:59.8864426Z then we check the implementation mutates the Tensor. If the schema 2024-12-18T01:09:59.8864952Z specifies that we return a new Tensor, then we check that the 2024-12-18T01:09:59.8865476Z implementation returns a new Tensor (instead of an existing one or 2024-12-18T01:09:59.8865935Z a view of an existing one). 2024-12-18T01:09:59.8866357Z - test_autograd_registration: If the operator supports training 2024-12-18T01:09:59.8866880Z (autograd): we check that its autograd formula is registered via 2024-12-18T01:09:59.8867416Z torch.library.register_autograd or a manual registration to one 2024-12-18T01:09:59.8867960Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2024-12-18T01:09:59.8868530Z registrations may lead to undefined behavior. 2024-12-18T01:09:59.8868978Z - test_faketensor: If the operator has a FakeTensor kernel 2024-12-18T01:09:59.8869462Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-12-18T01:09:59.8869975Z but not sufficient) for the operator to work with PyTorch compilation 2024-12-18T01:09:59.8870536Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2024-12-18T01:09:59.8871065Z (also sometimes known as a meta kernel) was registered for the 2024-12-18T01:09:59.8871557Z operator and that it is correct. This test takes the result of 2024-12-18T01:09:59.8872060Z running the operator on real tensors and the result of running 2024-12-18T01:09:59.8872571Z the operator on FakeTensors and checks that they have the same 2024-12-18T01:09:59.8873044Z Tensor metadata (sizes/strides/dtype/device/etc). 2024-12-18T01:09:59.8873516Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-12-18T01:09:59.8874003Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-12-18T01:09:59.8874522Z This checks that the outputs (and gradients, if applicable) are the 2024-12-18T01:09:59.8875009Z same under eager-mode PyTorch and torch.compile. 2024-12-18T01:09:59.8875666Z This test is a superset of ``test_faketensor`` and is an e2e test; 2024-12-18T01:09:59.8876154Z other things it tests are that the operator supports 2024-12-18T01:09:59.8876660Z functionalization and that the backward pass (if it exists) also 2024-12-18T01:09:59.8877130Z supports FakeTensor and functionalization. 2024-12-18T01:09:59.8877475Z 2024-12-18T01:09:59.8877800Z For best results, please call ``opcheck`` multiple times with a 2024-12-18T01:09:59.8878306Z representative set of inputs. If your operator supports 2024-12-18T01:09:59.8878847Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-12-18T01:09:59.8879417Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-12-18T01:09:59.8880003Z use ``opcheck`` with inputs on all supported devices. 2024-12-18T01:09:59.8880361Z 2024-12-18T01:09:59.8880547Z Args: 2024-12-18T01:09:59.8880868Z op: The operator. Must either be a function decorated with 2024-12-18T01:09:59.8881364Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-12-18T01:09:59.8881918Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-12-18T01:09:59.8882375Z args: The args to the operator 2024-12-18T01:09:59.8882701Z kwargs: The kwargs to the operator 2024-12-18T01:09:59.8883107Z test_utils: Tests that we should run. Default: all of them. 2024-12-18T01:09:59.8883558Z Example: ("test_schema", "test_faketensor") 2024-12-18T01:09:59.8883987Z raise_exception: If we should raise an exception on the first 2024-12-18T01:09:59.8884450Z error. If False, we will return a dict with information 2024-12-18T01:09:59.8884858Z on if each test passed or not. 2024-12-18T01:09:59.8885162Z 2024-12-18T01:09:59.8885380Z .. warning:: 2024-12-18T01:09:59.8885595Z 2024-12-18T01:09:59.8885939Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-12-18T01:09:59.8886473Z opcheck tests if your usage of torch.library APIs is correct while 2024-12-18T01:09:59.8887006Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-12-18T01:09:59.8887552Z mathematically correct. Use both to test custom ops that support 2024-12-18T01:09:59.8887982Z gradient computation. 2024-12-18T01:09:59.8888259Z 2024-12-18T01:09:59.8888455Z Example: 2024-12-18T01:09:59.8888676Z 2024-12-18T01:09:59.8888918Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:59.8889358Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:09:59.8889826Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2024-12-18T01:09:59.8890201Z >>> x_np = x.numpy(force=True) 2024-12-18T01:09:59.8890518Z >>> z_np = x_np * y 2024-12-18T01:09:59.8890853Z >>> return torch.from_numpy(z_np).to(x.device) 2024-12-18T01:09:59.8891179Z >>> 2024-12-18T01:09:59.8891410Z >>> @numpy_mul.register_fake 2024-12-18T01:09:59.8891719Z >>> def _(x, y): 2024-12-18T01:09:59.8891998Z >>> return torch.empty_like(x) 2024-12-18T01:09:59.8892298Z >>> 2024-12-18T01:09:59.8892560Z >>> def setup_context(ctx, inputs, output): 2024-12-18T01:09:59.8892879Z >>> y, = inputs 2024-12-18T01:09:59.8893144Z >>> ctx.y = y 2024-12-18T01:09:59.8893404Z >>> 2024-12-18T01:09:59.8893631Z >>> def backward(ctx, grad): 2024-12-18T01:09:59.8893948Z >>> return grad * ctx.y, None 2024-12-18T01:09:59.8894232Z >>> 2024-12-18T01:09:59.8894587Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2024-12-18T01:09:59.8895004Z >>> 2024-12-18T01:09:59.8895281Z >>> sample_inputs = [ 2024-12-18T01:09:59.8895603Z >>> (torch.randn(3), 3.14), 2024-12-18T01:09:59.8895935Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-12-18T01:09:59.8896311Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-12-18T01:09:59.8896759Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-12-18T01:09:59.8897155Z >>> ] 2024-12-18T01:09:59.8897375Z >>> 2024-12-18T01:09:59.8897609Z >>> for args in sample_inputs: 2024-12-18T01:09:59.8897959Z >>> torch.library.opcheck(numpy_mul, args) 2024-12-18T01:09:59.8898282Z 2024-12-18T01:09:59.8898465Z 2024-12-18T01:09:59.8898902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8899333Z 2024-12-18T01:09:59.8899537Z warnings.warn(msg) 2024-12-18T01:09:59.8899787Z 2024-12-18T01:09:59.8900156Z --- Parse Warning: 11 / 105 --- 2024-12-18T01:09:59.8901229Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1226. 2024-12-18T01:09:59.8902402Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8903107Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2024-12-18T01:09:59.8903621Z 2024-12-18T01:09:59.8903898Z Loads an object saved with :func:`torch.save` from a file. 2024-12-18T01:09:59.8904275Z 2024-12-18T01:09:59.8904607Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-12-18T01:09:59.8905187Z which underlie tensors, specially. They are first deserialized on the 2024-12-18T01:09:59.8905734Z CPU and are then moved to the device they were saved from. If this fails 2024-12-18T01:09:59.8906289Z (e.g. because the run time system doesn't have certain devices), an exception 2024-12-18T01:09:59.8906865Z is raised. However, storages can be dynamically remapped to an alternative 2024-12-18T01:09:59.8907394Z set of devices using the :attr:`map_location` argument. 2024-12-18T01:09:59.8907481Z 2024-12-18T01:09:59.8907734Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-12-18T01:09:59.8907950Z storage with two arguments: storage and location. The storage argument 2024-12-18T01:09:59.8908186Z will be the initial deserialization of the storage, residing on the CPU. 2024-12-18T01:09:59.8908486Z Each serialized storage has a location tag associated with it which 2024-12-18T01:09:59.8908703Z identifies the device it was saved from, and this tag is the second 2024-12-18T01:09:59.8908947Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-12-18T01:09:59.8909169Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-12-18T01:09:59.8909374Z :attr:`map_location` should return either ``None`` or a storage. If 2024-12-18T01:09:59.8909622Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-12-18T01:09:59.8909873Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-12-18T01:09:59.8910110Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-12-18T01:09:59.8910199Z 2024-12-18T01:09:59.8910432Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-12-18T01:09:59.8910657Z a device tag, it indicates the location where all tensors should be loaded. 2024-12-18T01:09:59.8910755Z 2024-12-18T01:09:59.8911014Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-12-18T01:09:59.8911227Z appearing in the file (keys), to ones that specify where to put the 2024-12-18T01:09:59.8911389Z storages (values). 2024-12-18T01:09:59.8911473Z 2024-12-18T01:09:59.8911687Z User extensions can register their own location tags and tagging and 2024-12-18T01:09:59.8911942Z deserialization methods using :func:`torch.serialization.register_package`. 2024-12-18T01:09:59.8912032Z 2024-12-18T01:09:59.8912114Z Args: 2024-12-18T01:09:59.8912436Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-12-18T01:09:59.8912605Z or a string or os.PathLike object containing a file name 2024-12-18T01:09:59.8912926Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-12-18T01:09:59.8913074Z locations 2024-12-18T01:09:59.8913305Z pickle_module: module used for unpickling metadata and objects (has to 2024-12-18T01:09:59.8913489Z match the :attr:`pickle_module` used to serialize file) 2024-12-18T01:09:59.8913698Z weights_only: Indicates whether unpickler should be restricted to 2024-12-18T01:09:59.8913859Z loading only tensors, primitive types, dictionaries 2024-12-18T01:09:59.8914073Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-12-18T01:09:59.8914210Z See :ref:`weights-only` for more details. 2024-12-18T01:09:59.8914534Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-12-18T01:09:59.8914865Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-12-18T01:09:59.8915220Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-12-18T01:09:59.8915543Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-12-18T01:09:59.8915788Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-12-18T01:09:59.8916024Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-12-18T01:09:59.8916245Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-12-18T01:09:59.8916350Z :attr:`errors=...`. 2024-12-18T01:09:59.8916447Z 2024-12-18T01:09:59.8916542Z .. warning:: 2024-12-18T01:09:59.8916760Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-12-18T01:09:59.8916958Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-12-18T01:09:59.8917231Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-12-18T01:09:59.8917484Z during unpickling. Never load data that could have come from an untrusted 2024-12-18T01:09:59.8917778Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-12-18T01:09:59.8917873Z 2024-12-18T01:09:59.8917963Z .. note:: 2024-12-18T01:09:59.8918221Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-12-18T01:09:59.8918488Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-12-18T01:09:59.8918756Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-12-18T01:09:59.8918846Z 2024-12-18T01:09:59.8918935Z .. note:: 2024-12-18T01:09:59.8919191Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-12-18T01:09:59.8919410Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-12-18T01:09:59.8919617Z when loading files saved by Python 2 in Python 3. If this default 2024-12-18T01:09:59.8919909Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-12-18T01:09:59.8920171Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-12-18T01:09:59.8920414Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-12-18T01:09:59.8920643Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-12-18T01:09:59.8920725Z 2024-12-18T01:09:59.8920807Z Example: 2024-12-18T01:09:59.8920936Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-12-18T01:09:59.8921083Z >>> torch.load("tensors.pt", weights_only=True) 2024-12-18T01:09:59.8921190Z # Load all tensors onto the CPU 2024-12-18T01:09:59.8921499Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-12-18T01:09:59.8921648Z # Load all tensors onto the CPU, using a function 2024-12-18T01:09:59.8921756Z >>> torch.load( 2024-12-18T01:09:59.8922000Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-12-18T01:09:59.8922089Z ... ) 2024-12-18T01:09:59.8922215Z # Load all tensors onto GPU 1 2024-12-18T01:09:59.8922307Z >>> torch.load( 2024-12-18T01:09:59.8922417Z ... "tensors.pt", 2024-12-18T01:09:59.8922582Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-12-18T01:09:59.8922685Z ... weights_only=True, 2024-12-18T01:09:59.8922815Z ... ) # type: ignore[attr-defined] 2024-12-18T01:09:59.8922932Z # Map tensors from GPU 1 to GPU 0 2024-12-18T01:09:59.8923187Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-12-18T01:09:59.8923306Z # Load tensor from io.BytesIO object 2024-12-18T01:09:59.8923568Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-12-18T01:09:59.8923695Z >>> with open("tensor.pt", "rb") as f: 2024-12-18T01:09:59.8923812Z ... buffer = io.BytesIO(f.read()) 2024-12-18T01:09:59.8923953Z >>> torch.load(buffer, weights_only=False) 2024-12-18T01:09:59.8924108Z # Load a module with 'ascii' encoding for unpickling 2024-12-18T01:09:59.8924365Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-12-18T01:09:59.8924560Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-12-18T01:09:59.8924660Z 2024-12-18T01:09:59.8924914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8924997Z 2024-12-18T01:09:59.8925109Z warnings.warn(msg) 2024-12-18T01:09:59.8925194Z 2024-12-18T01:09:59.8925437Z --- Parse Warning: 12 / 105 --- 2024-12-18T01:09:59.8926324Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=21. 2024-12-18T01:09:59.8926583Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:59.8926768Z Check if there is an available :ref:`accelerator`. 2024-12-18T01:09:59.8926847Z 2024-12-18T01:09:59.8926937Z Returns: 2024-12-18T01:09:59.8927208Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2024-12-18T01:09:59.8927292Z 2024-12-18T01:09:59.8927387Z Example:: 2024-12-18T01:09:59.8927471Z 2024-12-18T01:09:59.8927753Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:59.8927843Z 2024-12-18T01:09:59.8928411Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2024-12-18T01:09:59.8928556Z 2024-12-18T01:09:59.8928828Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:59.8928931Z ^ 2024-12-18T01:09:59.8929030Z warnings.warn(msg) 2024-12-18T01:09:59.8929126Z 2024-12-18T01:09:59.8929317Z --- Parse Warning: 13 / 105 --- 2024-12-18T01:09:59.8930218Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=110. 2024-12-18T01:09:59.8930748Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:59.8931040Z Wait for all kernels in all streams on the given device to complete. 2024-12-18T01:09:59.8931126Z 2024-12-18T01:09:59.8931215Z Args: 2024-12-18T01:09:59.8931549Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2024-12-18T01:09:59.8931784Z the current :ref:`accelerator` device type. If not given, 2024-12-18T01:09:59.8931985Z use :func:`torch.accelerator.current_device_idx` by default. 2024-12-18T01:09:59.8932069Z 2024-12-18T01:09:59.8932396Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2024-12-18T01:09:59.8932481Z 2024-12-18T01:09:59.8932572Z Example:: 2024-12-18T01:09:59.8932673Z 2024-12-18T01:09:59.8932816Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:59.8933095Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:59.8933248Z >>> start_event = torch.Event(enable_timing=True) 2024-12-18T01:09:59.8933400Z >>> end_event = torch.Event(enable_timing=True) 2024-12-18T01:09:59.8933516Z >>> start_event.record() 2024-12-18T01:09:59.8933761Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2024-12-18T01:09:59.8933880Z >>> sum = torch.sum(tensor) 2024-12-18T01:09:59.8933981Z >>> end_event.record() 2024-12-18T01:09:59.8934110Z >>> torch.accelerator.synchronize() 2024-12-18T01:09:59.8934296Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2024-12-18T01:09:59.8934387Z 2024-12-18T01:09:59.8934953Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2024-12-18T01:09:59.8935040Z 2024-12-18T01:09:59.8935298Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:09:59.8935418Z ^ 2024-12-18T01:09:59.8935518Z warnings.warn(msg) 2024-12-18T01:09:59.8935618Z 2024-12-18T01:09:59.8935816Z --- Parse Warning: 14 / 105 --- 2024-12-18T01:09:59.8936912Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=343. 2024-12-18T01:09:59.8937167Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:59.8937289Z Retrieves the CUDA runtime API module. 2024-12-18T01:09:59.8937389Z 2024-12-18T01:09:59.8937472Z 2024-12-18T01:09:59.8937737Z This function initializes the CUDA runtime environment if it is not already 2024-12-18T01:09:59.8937967Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-12-18T01:09:59.8938211Z runtime API module provides access to various CUDA runtime functions. 2024-12-18T01:09:59.8938299Z 2024-12-18T01:09:59.8938387Z Args: 2024-12-18T01:09:59.8938492Z ``None`` 2024-12-18T01:09:59.8938577Z 2024-12-18T01:09:59.8938810Z Returns: 2024-12-18T01:09:59.8938957Z module: The CUDA runtime API module (_cudart). 2024-12-18T01:09:59.8939042Z 2024-12-18T01:09:59.8939146Z Raises: 2024-12-18T01:09:59.8939378Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-12-18T01:09:59.8939759Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-12-18T01:09:59.8939842Z 2024-12-18T01:09:59.8939993Z Example of CUDA operations with profiling: 2024-12-18T01:09:59.8940095Z >>> import torch 2024-12-18T01:09:59.8940235Z >>> from torch.cuda import cudart, check_error 2024-12-18T01:09:59.8940342Z >>> import os 2024-12-18T01:09:59.8940429Z >>> 2024-12-18T01:09:59.8940625Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-12-18T01:09:59.8940712Z >>> 2024-12-18T01:09:59.8940852Z >>> def perform_cuda_operations_with_streams(): 2024-12-18T01:09:59.8940983Z >>> stream = torch.cuda.Stream() 2024-12-18T01:09:59.8941103Z >>> with torch.cuda.stream(stream): 2024-12-18T01:09:59.8941244Z >>> x = torch.randn(100, 100, device='cuda') 2024-12-18T01:09:59.8941371Z >>> y = torch.randn(100, 100, device='cuda') 2024-12-18T01:09:59.8941490Z >>> z = torch.mul(x, y) 2024-12-18T01:09:59.8941586Z >>> return z 2024-12-18T01:09:59.8941671Z >>> 2024-12-18T01:09:59.8941796Z >>> torch.cuda.synchronize() 2024-12-18T01:09:59.8941930Z >>> print("====== Start nsys profiling ======") 2024-12-18T01:09:59.8942079Z >>> check_error(cudart().cudaProfilerStart()) 2024-12-18T01:09:59.8942223Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-12-18T01:09:59.8942377Z >>> result = perform_cuda_operations_with_streams() 2024-12-18T01:09:59.8942516Z >>> print("CUDA operations completed.") 2024-12-18T01:09:59.8942686Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-12-18T01:09:59.8942823Z >>> print("====== End nsys profiling ======") 2024-12-18T01:09:59.8942911Z 2024-12-18T01:09:59.8943120Z To run this example and save the profiling information, execute: 2024-12-18T01:09:59.8943482Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:09:59.8943568Z 2024-12-18T01:09:59.8943823Z This command profiles the CUDA operations in the provided script and saves 2024-12-18T01:09:59.8944021Z the profiling information to a file named `trace_name.prof`. 2024-12-18T01:09:59.8944272Z The `--profile-from-start off` option ensures that profiling starts only 2024-12-18T01:09:59.8944424Z after the `cudaProfilerStart` call in the script. 2024-12-18T01:09:59.8944658Z The `--csv` and `--print-summary` options format the profiling output as a 2024-12-18T01:09:59.8944798Z CSV file and print a summary, respectively. 2024-12-18T01:09:59.8945042Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-12-18T01:09:59.8945211Z overwrite of the output file if it already exists. 2024-12-18T01:09:59.8945298Z 2024-12-18T01:09:59.8945956Z 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)) 2024-12-18T01:09:59.8946042Z 2024-12-18T01:09:59.8946401Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:09:59.8946483Z ^ 2024-12-18T01:09:59.8946583Z warnings.warn(msg) 2024-12-18T01:09:59.8946681Z 2024-12-18T01:09:59.8946892Z --- Parse Warning: 15 / 105 --- 2024-12-18T01:09:59.8947767Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-12-18T01:09:59.8948085Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8948182Z 2024-12-18T01:09:59.8948487Z Append the given callback function to this ``Future``, which will be run 2024-12-18T01:09:59.8948696Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-12-18T01:09:59.8948917Z the same ``Future``, but the order in which they will be executed cannot 2024-12-18T01:09:59.8949106Z be guaranteed (to enforce a certain order consider chaining: 2024-12-18T01:09:59.8949321Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-12-18T01:09:59.8949585Z is the reference to this ``Future``. The callback function can use the 2024-12-18T01:09:59.8949807Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-12-18T01:09:59.8950039Z already completed, the given callback will be run immediately inline. 2024-12-18T01:09:59.8950123Z 2024-12-18T01:09:59.8950329Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-12-18T01:09:59.8950549Z callback might be invoked while the async kernels that are populating 2024-12-18T01:09:59.8950788Z those tensors haven't yet finished executing on the device. However, the 2024-12-18T01:09:59.8950996Z callback will be invoked with some dedicated streams set as current 2024-12-18T01:09:59.8951213Z (fetched from a global pool) which will be synchronized with those 2024-12-18T01:09:59.8951440Z kernels. Hence any operation performed by the callback on these tensors 2024-12-18T01:09:59.8951650Z will be scheduled on the device after the kernels complete. In other 2024-12-18T01:09:59.8951859Z words, as long as the callback doesn't switch streams, it can safely 2024-12-18T01:09:59.8952088Z manipulate the result without any additional synchronization. This is 2024-12-18T01:09:59.8952263Z similar to the non-blocking behavior of :meth:`wait`. 2024-12-18T01:09:59.8952346Z 2024-12-18T01:09:59.8952571Z Similarly, if the callback returns a value that contains tensors that 2024-12-18T01:09:59.8952764Z reside on a GPU, it can do so even if the kernels that are producing 2024-12-18T01:09:59.8952983Z these tensors are still running on the device, as long as the callback 2024-12-18T01:09:59.8953196Z didn't change streams during its execution. If one wants to change 2024-12-18T01:09:59.8953407Z streams, one must be careful to re-synchronize them with the original 2024-12-18T01:09:59.8953637Z streams, that is, those that were current when the callback was invoked. 2024-12-18T01:09:59.8953724Z 2024-12-18T01:09:59.8953829Z Args: 2024-12-18T01:09:59.8954033Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-12-18T01:09:59.8954146Z the only argument. 2024-12-18T01:09:59.8954245Z 2024-12-18T01:09:59.8954334Z Returns: 2024-12-18T01:09:59.8954512Z A new ``Future`` object that holds the return value of the 2024-12-18T01:09:59.8954691Z ``callback`` and will be marked as completed when the given 2024-12-18T01:09:59.8954790Z ``callback`` finishes. 2024-12-18T01:09:59.8954883Z 2024-12-18T01:09:59.8955063Z .. note:: Note that if the callback function throws, either 2024-12-18T01:09:59.8955287Z through the original future being completed with an exception and 2024-12-18T01:09:59.8955479Z calling ``fut.wait()``, or through other code in the callback, the 2024-12-18T01:09:59.8955695Z future returned by ``then`` will be marked appropriately with the 2024-12-18T01:09:59.8955897Z encountered error. However, if this callback later completes 2024-12-18T01:09:59.8956113Z additional futures, those futures are not marked as completed with 2024-12-18T01:09:59.8956357Z an error and the user is responsible for handling completion/waiting 2024-12-18T01:09:59.8956499Z on those futures independently. 2024-12-18T01:09:59.8956594Z 2024-12-18T01:09:59.8956685Z Example:: 2024-12-18T01:09:59.8956845Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:09:59.8956946Z >>> def callback(fut): 2024-12-18T01:09:59.8957090Z ... print(f"RPC return value is {fut.wait()}.") 2024-12-18T01:09:59.8957216Z >>> fut = torch.futures.Future() 2024-12-18T01:09:59.8957392Z >>> # The inserted callback will print the return value when 2024-12-18T01:09:59.8957529Z >>> # receiving the response from "worker1" 2024-12-18T01:09:59.8957635Z >>> cb_fut = fut.then(callback) 2024-12-18T01:09:59.8957803Z >>> chain_cb_fut = cb_fut.then( 2024-12-18T01:09:59.8957955Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-12-18T01:09:59.8958040Z ... ) 2024-12-18T01:09:59.8958146Z >>> fut.set_result(5) 2024-12-18T01:09:59.8958252Z RPC return value is 5. 2024-12-18T01:09:59.8958361Z Chained cb done. None 2024-12-18T01:09:59.8958444Z 2024-12-18T01:09:59.8958697Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8958791Z 2024-12-18T01:09:59.8958888Z warnings.warn(msg) 2024-12-18T01:09:59.8958981Z 2024-12-18T01:09:59.8959185Z --- Parse Warning: 16 / 105 --- 2024-12-18T01:09:59.8960069Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-12-18T01:09:59.8960346Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8960430Z 2024-12-18T01:09:59.8960648Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-12-18T01:09:59.8960864Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-12-18T01:09:59.8960992Z cannot be marked completed twice. 2024-12-18T01:09:59.8961079Z 2024-12-18T01:09:59.8961297Z If the result contains tensors that reside on GPUs, this method can be 2024-12-18T01:09:59.8961511Z called even if the asynchronous kernels that are populating those 2024-12-18T01:09:59.8961732Z tensors haven't yet completed running on the device, provided that the 2024-12-18T01:09:59.8961967Z streams on which those kernels were enqueued are set as the current ones 2024-12-18T01:09:59.8962178Z when this method is called. Put simply, it's safe to call this method 2024-12-18T01:09:59.8962407Z immediately after launching those kernels, without any additional 2024-12-18T01:09:59.8962641Z synchronization, as long as one doesn't change streams in between. This 2024-12-18T01:09:59.8962873Z method will record events on all the relevant current streams and will 2024-12-18T01:09:59.8963076Z use them to ensure proper scheduling for all the consumers of this 2024-12-18T01:09:59.8963167Z ``Future``. 2024-12-18T01:09:59.8963263Z 2024-12-18T01:09:59.8963350Z Args: 2024-12-18T01:09:59.8963526Z result (object): the result object of this ``Future``. 2024-12-18T01:09:59.8963608Z 2024-12-18T01:09:59.8963701Z Example:: 2024-12-18T01:09:59.8963862Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:09:59.8963961Z >>> import threading 2024-12-18T01:09:59.8964063Z >>> import time 2024-12-18T01:09:59.8964179Z >>> def slow_set_future(fut, value): 2024-12-18T01:09:59.8964278Z ... time.sleep(0.5) 2024-12-18T01:09:59.8964394Z ... fut.set_result(value) 2024-12-18T01:09:59.8964505Z >>> fut = torch.futures.Future() 2024-12-18T01:09:59.8964625Z >>> t = threading.Thread( 2024-12-18T01:09:59.8964729Z ... target=slow_set_future, 2024-12-18T01:09:59.8964852Z ... args=(fut, torch.ones(2) * 3) 2024-12-18T01:09:59.8965011Z ... ) 2024-12-18T01:09:59.8965102Z >>> t.start() 2024-12-18T01:09:59.8965212Z >>> print(fut.wait()) 2024-12-18T01:09:59.8965306Z tensor([3., 3.]) 2024-12-18T01:09:59.8965408Z >>> t.join() 2024-12-18T01:09:59.8965491Z 2024-12-18T01:09:59.8965749Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8965849Z 2024-12-18T01:09:59.8965946Z warnings.warn(msg) 2024-12-18T01:09:59.8966045Z 2024-12-18T01:09:59.8966237Z --- Parse Warning: 17 / 105 --- 2024-12-18T01:09:59.8967159Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/mps/__init__.py line=144. 2024-12-18T01:09:59.8967437Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8967659Z Compiles compute shader from source and allows one to invoke kernels 2024-12-18T01:09:59.8967829Z defined there from the comfort of Python runtime 2024-12-18T01:09:59.8967924Z Example:: 2024-12-18T01:09:59.8968021Z 2024-12-18T01:09:59.8968161Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2024-12-18T01:09:59.8968283Z >>> lib = torch.mps._compile_shader( 2024-12-18T01:09:59.8968681Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2024-12-18T01:09:59.8968769Z ... ) 2024-12-18T01:09:59.8968904Z >>> x = torch.zeros(16, device="mps") 2024-12-18T01:09:59.8969004Z >>> lib.full(x, 3.14) 2024-12-18T01:09:59.8969104Z 2024-12-18T01:09:59.8969361Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8969445Z 2024-12-18T01:09:59.8969559Z warnings.warn(msg) 2024-12-18T01:09:59.8969645Z 2024-12-18T01:09:59.8969851Z --- Parse Warning: 18 / 105 --- 2024-12-18T01:09:59.8970670Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202. 2024-12-18T01:09:59.8970947Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8971114Z Return the sum of each row of the given sparse tensor. 2024-12-18T01:09:59.8971201Z 2024-12-18T01:09:59.8971442Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-12-18T01:09:59.8971643Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-12-18T01:09:59.8971872Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-12-18T01:09:59.8972027Z returns a dense tensor instead of a sparse tensor. 2024-12-18T01:09:59.8972129Z 2024-12-18T01:09:59.8972390Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-12-18T01:09:59.8972585Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-12-18T01:09:59.8972678Z 2024-12-18T01:09:59.8972901Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-12-18T01:09:59.8973150Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-12-18T01:09:59.8973236Z 2024-12-18T01:09:59.8973320Z Args: 2024-12-18T01:09:59.8973458Z input (Tensor): the input sparse tensor 2024-12-18T01:09:59.8973732Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-12-18T01:09:59.8973841Z over all dims. 2024-12-18T01:09:59.8974104Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-12-18T01:09:59.8974232Z Default: dtype of :attr:`input`. 2024-12-18T01:09:59.8974365Z 2024-12-18T01:09:59.8974484Z Example:: 2024-12-18T01:09:59.8974578Z 2024-12-18T01:09:59.8974667Z >>> nnz = 3 2024-12-18T01:09:59.8974775Z >>> dims = [5, 5, 2, 3] 2024-12-18T01:09:59.8974935Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-12-18T01:09:59.8975139Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-12-18T01:09:59.8975265Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-12-18T01:09:59.8975371Z >>> size = torch.Size(dims) 2024-12-18T01:09:59.8975521Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:59.8975649Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-12-18T01:09:59.8975752Z >>> S 2024-12-18T01:09:59.8975918Z tensor(indices=tensor([[2, 0, 3], 2024-12-18T01:09:59.8976022Z [2, 4, 1]]), 2024-12-18T01:09:59.8976165Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-12-18T01:09:59.8976283Z [ 0.3411, 0.0918, -0.2312]], 2024-12-18T01:09:59.8976379Z 2024-12-18T01:09:59.8976490Z [[ 0.5348, 0.0634, -2.0494], 2024-12-18T01:09:59.8976614Z [-0.7125, -1.0646, 2.1844]], 2024-12-18T01:09:59.8976695Z 2024-12-18T01:09:59.8976808Z [[ 0.1276, 0.1874, -0.6334], 2024-12-18T01:09:59.8976934Z [-1.9682, -0.5340, 0.7483]]]), 2024-12-18T01:09:59.8977079Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:09:59.8977175Z 2024-12-18T01:09:59.8977371Z # when sum over only part of sparse_dims, return a sparse tensor 2024-12-18T01:09:59.8977501Z >>> torch.sparse.sum(S, [1, 3]) 2024-12-18T01:09:59.8977616Z tensor(indices=tensor([[0, 2, 3]]), 2024-12-18T01:09:59.8977734Z values=tensor([[-1.4512, 0.4073], 2024-12-18T01:09:59.8977857Z [-0.8901, 0.2017], 2024-12-18T01:09:59.8977965Z [-0.3183, -1.7539]]), 2024-12-18T01:09:59.8978112Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:09:59.8978196Z 2024-12-18T01:09:59.8978351Z # when sum over all sparse dim, return a dense tensor 2024-12-18T01:09:59.8978468Z # with summed dims squeezed 2024-12-18T01:09:59.8978584Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-12-18T01:09:59.8978696Z tensor([-2.6596, -1.1450]) 2024-12-18T01:09:59.8978780Z 2024-12-18T01:09:59.8979049Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.8979131Z 2024-12-18T01:09:59.8979233Z warnings.warn(msg) 2024-12-18T01:09:59.8979325Z 2024-12-18T01:09:59.8979517Z --- Parse Warning: 19 / 105 --- 2024-12-18T01:09:59.8980350Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-12-18T01:09:59.8980615Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.8980712Z 2024-12-18T01:09:59.8980928Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-12-18T01:09:59.8981129Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-12-18T01:09:59.8981359Z pushes the map into PyTorch operations called by ``func``, effectively 2024-12-18T01:09:59.8981468Z vectorizing those operations. 2024-12-18T01:09:59.8981568Z 2024-12-18T01:09:59.8981779Z vmap is useful for handling batch dimensions: one can write a function 2024-12-18T01:09:59.8981986Z ``func`` that runs on examples and then lift it to a function that can 2024-12-18T01:09:59.8982216Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-12-18T01:09:59.8982445Z compute batched gradients when composed with autograd. 2024-12-18T01:09:59.8982542Z 2024-12-18T01:09:59.8982629Z .. note:: 2024-12-18T01:09:59.8982826Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-12-18T01:09:59.8982959Z convenience. Use whichever one you'd like. 2024-12-18T01:09:59.8983040Z 2024-12-18T01:09:59.8983139Z Args: 2024-12-18T01:09:59.8983347Z func (function): A Python function that takes one or more arguments. 2024-12-18T01:09:59.8983475Z Must return one or more Tensors. 2024-12-18T01:09:59.8983676Z in_dims (int or nested structure): Specifies which dimension of the 2024-12-18T01:09:59.8983851Z inputs should be mapped over. ``in_dims`` should have a 2024-12-18T01:09:59.8984115Z structure like the inputs. If the ``in_dim`` for a particular 2024-12-18T01:09:59.8984300Z input is None, then that indicates there is no map dimension. 2024-12-18T01:09:59.8984406Z Default: 0. 2024-12-18T01:09:59.8984600Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-12-18T01:09:59.8984803Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-12-18T01:09:59.8984959Z it should have one element per output. Default: 0. 2024-12-18T01:09:59.8985147Z randomness (str): Specifies whether the randomness in this 2024-12-18T01:09:59.8985367Z vmap should be the same or different across batches. If 'different', 2024-12-18T01:09:59.8985570Z the randomness for each batch will be different. If 'same', the 2024-12-18T01:09:59.8985793Z randomness will be the same across batches. If 'error', any calls to 2024-12-18T01:09:59.8986008Z random functions will error. Default: 'error'. WARNING: this flag 2024-12-18T01:09:59.8986222Z only applies to random PyTorch operations and does not apply to 2024-12-18T01:09:59.8986363Z Python's random module or numpy randomness. 2024-12-18T01:09:59.8986605Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-12-18T01:09:59.8986823Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-12-18T01:09:59.8987085Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-12-18T01:09:59.8987366Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-12-18T01:09:59.8987451Z 2024-12-18T01:09:59.8987551Z Returns: 2024-12-18T01:09:59.8987741Z Returns a new "batched" function. It takes the same inputs as 2024-12-18T01:09:59.8987939Z ``func``, except each input has an extra dimension at the index 2024-12-18T01:09:59.8988134Z specified by ``in_dims``. It takes returns the same outputs as 2024-12-18T01:09:59.8988403Z ``func``, except each output has an extra dimension at the index 2024-12-18T01:09:59.8988531Z specified by ``out_dims``. 2024-12-18T01:09:59.8988616Z 2024-12-18T01:09:59.8988716Z .. warning: 2024-12-18T01:09:59.8988915Z :func:`vmap` works best with functional-style code. Please do not 2024-12-18T01:09:59.8989103Z perform any side-effects in ``func``, with the exception of 2024-12-18T01:09:59.8989351Z in-place PyTorch operations. Examples of side-effects include mutating 2024-12-18T01:09:59.8989576Z Python data structures and assigning values to variables not captured 2024-12-18T01:09:59.8989679Z in ``func``. 2024-12-18T01:09:59.8989763Z 2024-12-18T01:09:59.8990012Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-12-18T01:09:59.8990235Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-12-18T01:09:59.8990458Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-12-18T01:09:59.8990551Z 2024-12-18T01:09:59.8990700Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:09:59.8990975Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-12-18T01:09:59.8991106Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:09:59.8991217Z >>> batched_dot(x, y) 2024-12-18T01:09:59.8991300Z 2024-12-18T01:09:59.8991528Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-12-18T01:09:59.8991644Z model authoring experience. 2024-12-18T01:09:59.8991727Z 2024-12-18T01:09:59.8991855Z >>> batch_size, feature_size = 3, 5 2024-12-18T01:09:59.8992029Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-12-18T01:09:59.8992119Z >>> 2024-12-18T01:09:59.8992238Z >>> def model(feature_vec): 2024-12-18T01:09:59.8992429Z >>> # Very simple linear model with activation 2024-12-18T01:09:59.8992576Z >>> return feature_vec.dot(weights).relu() 2024-12-18T01:09:59.8992660Z >>> 2024-12-18T01:09:59.8992831Z >>> examples = torch.randn(batch_size, feature_size) 2024-12-18T01:09:59.8992953Z >>> result = torch.vmap(model)(examples) 2024-12-18T01:09:59.8993036Z 2024-12-18T01:09:59.8993299Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-12-18T01:09:59.8993531Z or impossible to batch. One example is higher-order gradient computation. 2024-12-18T01:09:59.8993772Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-12-18T01:09:59.8994000Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-12-18T01:09:59.8994250Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-12-18T01:09:59.8994503Z we can vectorize the whole computation, computing the Jacobian in a single 2024-12-18T01:09:59.8994608Z call to ``autograd.grad``. 2024-12-18T01:09:59.8994703Z 2024-12-18T01:09:59.8994789Z >>> # Setup 2024-12-18T01:09:59.8994890Z >>> N = 5 2024-12-18T01:09:59.8994990Z >>> f = lambda x: x ** 2 2024-12-18T01:09:59.8995113Z >>> x = torch.randn(N, requires_grad=True) 2024-12-18T01:09:59.8995212Z >>> y = f(x) 2024-12-18T01:09:59.8995309Z >>> I_N = torch.eye(N) 2024-12-18T01:09:59.8995406Z >>> 2024-12-18T01:09:59.8995516Z >>> # Sequential approach 2024-12-18T01:09:59.8995734Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-12-18T01:09:59.8995860Z >>> for v in I_N.unbind()] 2024-12-18T01:09:59.8995986Z >>> jacobian = torch.stack(jacobian_rows) 2024-12-18T01:09:59.8996089Z >>> 2024-12-18T01:09:59.8996209Z >>> # vectorized gradient computation 2024-12-18T01:09:59.8996320Z >>> def get_vjp(v): 2024-12-18T01:09:59.8996449Z >>> return torch.autograd.grad(y, x, v) 2024-12-18T01:09:59.8996574Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-12-18T01:09:59.8996675Z 2024-12-18T01:09:59.8996943Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-12-18T01:09:59.8997045Z 2024-12-18T01:09:59.8997192Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:09:59.8997465Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-12-18T01:09:59.8997623Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-12-18T01:09:59.8997757Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-12-18T01:09:59.8997856Z 2024-12-18T01:09:59.8998098Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-12-18T01:09:59.8998269Z the dimension that each inputs are batched along as 2024-12-18T01:09:59.8998356Z 2024-12-18T01:09:59.8998504Z >>> torch.dot # [N], [N] -> [] 2024-12-18T01:09:59.8998742Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-12-18T01:09:59.8998928Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:09:59.8999184Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-12-18T01:09:59.8999270Z 2024-12-18T01:09:59.8999544Z If there are multiple inputs each of which is batched along different dimensions, 2024-12-18T01:09:59.8999747Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-12-18T01:09:59.8999832Z 2024-12-18T01:09:59.8999992Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:09:59.9000223Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-12-18T01:09:59.9000363Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:09:59.9000675Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-12-18T01:09:59.9000778Z 2024-12-18T01:09:59.9001016Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-12-18T01:09:59.9001130Z matching the shape of the input: 2024-12-18T01:09:59.9001227Z 2024-12-18T01:09:59.9001369Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-12-18T01:09:59.9001505Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:09:59.9001604Z >>> input = {'x': x, 'y': y} 2024-12-18T01:09:59.9001784Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-12-18T01:09:59.9001898Z >>> batched_dot(input) 2024-12-18T01:09:59.9001980Z 2024-12-18T01:09:59.9002266Z By default, the output is batched along the first dimension. However, it can be batched 2024-12-18T01:09:59.9002390Z along any dimension by using ``out_dims`` 2024-12-18T01:09:59.9002487Z 2024-12-18T01:09:59.9002587Z >>> f = lambda x: x ** 2 2024-12-18T01:09:59.9002688Z >>> x = torch.randn(2, 5) 2024-12-18T01:09:59.9002825Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-12-18T01:09:59.9002931Z >>> batched_pow(x) # [5, 2] 2024-12-18T01:09:59.9003024Z 2024-12-18T01:09:59.9003314Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-12-18T01:09:59.9003406Z accept kwargs 2024-12-18T01:09:59.9003500Z 2024-12-18T01:09:59.9003599Z >>> x = torch.randn([2, 5]) 2024-12-18T01:09:59.9003707Z >>> def fn(x, scale=4.): 2024-12-18T01:09:59.9003803Z >>> return x * scale 2024-12-18T01:09:59.9003891Z >>> 2024-12-18T01:09:59.9004015Z >>> batched_pow = torch.vmap(fn) 2024-12-18T01:09:59.9004158Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-12-18T01:09:59.9004393Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-12-18T01:09:59.9004477Z 2024-12-18T01:09:59.9004583Z .. note:: 2024-12-18T01:09:59.9004803Z vmap does not provide general autobatching or handle variable-length 2024-12-18T01:09:59.9004907Z sequences out of the box. 2024-12-18T01:09:59.9005008Z 2024-12-18T01:09:59.9005261Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9005356Z 2024-12-18T01:09:59.9005455Z warnings.warn(msg) 2024-12-18T01:09:59.9005538Z 2024-12-18T01:09:59.9005772Z --- Parse Warning: 20 / 105 --- 2024-12-18T01:09:59.9006616Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=20. 2024-12-18T01:09:59.9006895Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9007147Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-12-18T01:09:59.9007249Z 2024-12-18T01:09:59.9007460Z This is a more structured way of using triton kernels with PyTorch. 2024-12-18T01:09:59.9007727Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2024-12-18T01:09:59.9008034Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2024-12-18T01:09:59.9008130Z that is simpler; 2024-12-18T01:09:59.9008393Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2024-12-18T01:09:59.9008622Z want to create an operator that behaves like PyTorch built-in operators. 2024-12-18T01:09:59.9008846Z For example, you may use a ``torch.library`` wrapper API to define the 2024-12-18T01:09:59.9009062Z behavior of the triton kernel when passed a tensor subclass or under 2024-12-18T01:09:59.9009179Z a TorchDispatchMode. 2024-12-18T01:09:59.9009263Z 2024-12-18T01:09:59.9009568Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2024-12-18T01:09:59.9009685Z when the implementation 2024-12-18T01:09:59.9009903Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-12-18T01:09:59.9010088Z custom operators as opaque (:func:`torch.compile` and 2024-12-18T01:09:59.9010322Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-12-18T01:09:59.9010543Z makes the implementation visible to these subsystems, allowing them 2024-12-18T01:09:59.9010672Z to optimize the triton kernel(s). 2024-12-18T01:09:59.9010757Z 2024-12-18T01:09:59.9010959Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-12-18T01:09:59.9011186Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-12-18T01:09:59.9011397Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2024-12-18T01:09:59.9011482Z 2024-12-18T01:09:59.9011568Z Args: 2024-12-18T01:09:59.9011809Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-12-18T01:09:59.9012025Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-12-18T01:09:59.9012209Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-12-18T01:09:59.9012448Z To avoid name collisions, please use your project name as the namespace; 2024-12-18T01:09:59.9012667Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-12-18T01:09:59.9012947Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-12-18T01:09:59.9013196Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-12-18T01:09:59.9013460Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-12-18T01:09:59.9013648Z schema (None | str): A schema string for the operator. If None 2024-12-18T01:09:59.9013872Z (recommended) we'll infer a schema for the operator from its type 2024-12-18T01:09:59.9014078Z annotations. We recommend letting us infer a schema unless you 2024-12-18T01:09:59.9014211Z have a specific reason not to. 2024-12-18T01:09:59.9014363Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-12-18T01:09:59.9014459Z 2024-12-18T01:09:59.9014551Z Example:: 2024-12-18T01:09:59.9014635Z 2024-12-18T01:09:59.9014787Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:59.9014883Z >>> import torch 2024-12-18T01:09:59.9015055Z >>> from torch._library import triton_op, wrap_triton 2024-12-18T01:09:59.9015142Z >>> 2024-12-18T01:09:59.9015238Z >>> import triton 2024-12-18T01:09:59.9015371Z >>> from triton import language as tl 2024-12-18T01:09:59.9015457Z >>> 2024-12-18T01:09:59.9015565Z >>> @triton.jit 2024-12-18T01:09:59.9015664Z >>> def add_kernel( 2024-12-18T01:09:59.9015757Z >>> in_ptr0, 2024-12-18T01:09:59.9015861Z >>> in_ptr1, 2024-12-18T01:09:59.9016010Z >>> out_ptr, 2024-12-18T01:09:59.9016118Z >>> n_elements, 2024-12-18T01:09:59.9016237Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:09:59.9016337Z >>> ): 2024-12-18T01:09:59.9016455Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:09:59.9016573Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:09:59.9016736Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:09:59.9016848Z >>> mask = offsets < n_elements 2024-12-18T01:09:59.9016989Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:09:59.9017119Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:09:59.9017217Z >>> output = x + y 2024-12-18T01:09:59.9017432Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:09:59.9017519Z >>> 2024-12-18T01:09:59.9017661Z >>> @triton_op("mylib::add", mutates_args={}) 2024-12-18T01:09:59.9017846Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-12-18T01:09:59.9017975Z >>> output = torch.empty_like(x) 2024-12-18T01:09:59.9018089Z >>> n_elements = output.numel() 2024-12-18T01:09:59.9018176Z >>> 2024-12-18T01:09:59.9018284Z >>> def grid(meta): 2024-12-18T01:09:59.9018457Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:09:59.9018556Z >>> 2024-12-18T01:09:59.9018741Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2024-12-18T01:09:59.9018940Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-12-18T01:09:59.9019037Z >>> return output 2024-12-18T01:09:59.9019127Z >>> 2024-12-18T01:09:59.9019237Z >>> @torch.compile 2024-12-18T01:09:59.9019335Z >>> def f(x, y): 2024-12-18T01:09:59.9019449Z >>> return add(x, y) 2024-12-18T01:09:59.9019537Z >>> 2024-12-18T01:09:59.9019657Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:09:59.9019787Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:09:59.9019875Z >>> 2024-12-18T01:09:59.9019983Z >>> z = f(x, y) 2024-12-18T01:09:59.9020103Z >>> assert torch.allclose(z, x + y) 2024-12-18T01:09:59.9020199Z 2024-12-18T01:09:59.9020286Z 2024-12-18T01:09:59.9020541Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9020641Z 2024-12-18T01:09:59.9020738Z warnings.warn(msg) 2024-12-18T01:09:59.9020836Z 2024-12-18T01:09:59.9021047Z --- Parse Warning: 21 / 105 --- 2024-12-18T01:09:59.9021907Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=183. 2024-12-18T01:09:59.9022186Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9022378Z Allows capture of a triton kernel into a graph via make_fx or 2024-12-18T01:09:59.9022500Z non-strict ``torch.export``. 2024-12-18T01:09:59.9022584Z 2024-12-18T01:09:59.9022783Z These technologies perform Dispatcher-based tracing (via 2024-12-18T01:09:59.9022985Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-12-18T01:09:59.9023185Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2024-12-18T01:09:59.9023315Z can actually be traced into a graph. 2024-12-18T01:09:59.9023398Z 2024-12-18T01:09:59.9023623Z Please use this API together with :func:`torch.library.triton_op`. 2024-12-18T01:09:59.9023708Z 2024-12-18T01:09:59.9023798Z Examples: 2024-12-18T01:09:59.9023891Z 2024-12-18T01:09:59.9023994Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9024164Z >>> import torch 2024-12-18T01:09:59.9024261Z >>> import triton 2024-12-18T01:09:59.9024393Z >>> from triton import language as tl 2024-12-18T01:09:59.9024576Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:09:59.9024707Z >>> from torch.library import wrap_triton 2024-12-18T01:09:59.9024808Z >>> 2024-12-18T01:09:59.9024903Z >>> @triton.jit 2024-12-18T01:09:59.9025014Z >>> def add_kernel( 2024-12-18T01:09:59.9025109Z >>> in_ptr0, 2024-12-18T01:09:59.9025199Z >>> in_ptr1, 2024-12-18T01:09:59.9025305Z >>> out_ptr, 2024-12-18T01:09:59.9025400Z >>> n_elements, 2024-12-18T01:09:59.9025590Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:09:59.9025680Z >>> ): 2024-12-18T01:09:59.9025811Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:09:59.9025931Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:09:59.9026089Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:09:59.9026217Z >>> mask = offsets < n_elements 2024-12-18T01:09:59.9026350Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:09:59.9026494Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:09:59.9026594Z >>> output = x + y 2024-12-18T01:09:59.9026743Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:09:59.9026845Z >>> 2024-12-18T01:09:59.9026943Z >>> def add(x, y): 2024-12-18T01:09:59.9027076Z >>> output = torch.empty_like(x) 2024-12-18T01:09:59.9027195Z >>> n_elements = output.numel() 2024-12-18T01:09:59.9027302Z >>> 2024-12-18T01:09:59.9027408Z >>> def grid_fn(meta): 2024-12-18T01:09:59.9027580Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:09:59.9027686Z >>> 2024-12-18T01:09:59.9027882Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-12-18T01:09:59.9027996Z >>> return output 2024-12-18T01:09:59.9028084Z >>> 2024-12-18T01:09:59.9028206Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:09:59.9028418Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:09:59.9028527Z >>> gm = make_fx(add)(x, y) 2024-12-18T01:09:59.9028641Z >>> print(gm.code) 2024-12-18T01:09:59.9028755Z >>> # def forward(self, x_1, y_1): 2024-12-18T01:09:59.9029006Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-12-18T01:09:59.9029254Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-12-18T01:09:59.9029386Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-12-18T01:09:59.9029515Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-12-18T01:09:59.9029675Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-12-18T01:09:59.9029815Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-12-18T01:09:59.9029905Z >>> # }) 2024-12-18T01:09:59.9030021Z >>> # return empty_like 2024-12-18T01:09:59.9030103Z 2024-12-18T01:09:59.9030186Z 2024-12-18T01:09:59.9030681Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9030765Z 2024-12-18T01:09:59.9030876Z warnings.warn(msg) 2024-12-18T01:09:59.9030958Z 2024-12-18T01:09:59.9031178Z --- Parse Warning: 22 / 105 --- 2024-12-18T01:09:59.9032111Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330. 2024-12-18T01:09:59.9032452Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9032547Z 2024-12-18T01:09:59.9032754Z Raises an AssertionError if two items are not equal up to desired 2024-12-18T01:09:59.9032862Z precision. 2024-12-18T01:09:59.9032943Z 2024-12-18T01:09:59.9033125Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:09:59.9033318Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:09:59.9033507Z instead of this function for more consistent floating point 2024-12-18T01:09:59.9033616Z comparisons. 2024-12-18T01:09:59.9033699Z 2024-12-18T01:09:59.9033930Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-12-18T01:09:59.9034093Z 2024-12-18T01:09:59.9034256Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-12-18T01:09:59.9034355Z 2024-12-18T01:09:59.9034582Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:09:59.9034831Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-12-18T01:09:59.9035066Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-12-18T01:09:59.9035187Z delegates to assert_array_almost_equal 2024-12-18T01:09:59.9035285Z 2024-12-18T01:09:59.9035376Z Parameters 2024-12-18T01:09:59.9035481Z ---------- 2024-12-18T01:09:59.9035575Z actual : array_like 2024-12-18T01:09:59.9035687Z The object to check. 2024-12-18T01:09:59.9035783Z desired : array_like 2024-12-18T01:09:59.9035884Z The expected object. 2024-12-18T01:09:59.9035994Z decimal : int, optional 2024-12-18T01:09:59.9036337Z Desired precision, default is 7. 2024-12-18T01:09:59.9036491Z err_msg : str, optional 2024-12-18T01:09:59.9036649Z The error message to be printed in case of failure. 2024-12-18T01:09:59.9036750Z verbose : bool, optional 2024-12-18T01:09:59.9036975Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:59.9037060Z 2024-12-18T01:09:59.9037161Z Raises 2024-12-18T01:09:59.9037251Z ------ 2024-12-18T01:09:59.9037346Z AssertionError 2024-12-18T01:09:59.9037557Z If actual and desired are not equal up to specified precision. 2024-12-18T01:09:59.9037640Z 2024-12-18T01:09:59.9037740Z See Also 2024-12-18T01:09:59.9037829Z -------- 2024-12-18T01:09:59.9038078Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:59.9038209Z relative and/or absolute precision. 2024-12-18T01:09:59.9038419Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:59.9038515Z 2024-12-18T01:09:59.9038609Z Examples 2024-12-18T01:09:59.9038711Z -------- 2024-12-18T01:09:59.9038877Z >>> from torch._numpy.testing import assert_almost_equal 2024-12-18T01:09:59.9039010Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-12-18T01:09:59.9039199Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-12-18T01:09:59.9039313Z Traceback (most recent call last): 2024-12-18T01:09:59.9039410Z ... 2024-12-18T01:09:59.9039506Z AssertionError: 2024-12-18T01:09:59.9039635Z Arrays are not almost equal to 10 decimals 2024-12-18T01:09:59.9039743Z ACTUAL: 2.3333333333333 2024-12-18T01:09:59.9039836Z DESIRED: 2.33333334 2024-12-18T01:09:59.9039930Z 2024-12-18T01:09:59.9040078Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-12-18T01:09:59.9040217Z ... np.array([1.0,2.33333334]), decimal=9) 2024-12-18T01:09:59.9040328Z Traceback (most recent call last): 2024-12-18T01:09:59.9040413Z ... 2024-12-18T01:09:59.9040522Z AssertionError: 2024-12-18T01:09:59.9040644Z Arrays are not almost equal to 9 decimals 2024-12-18T01:09:59.9040746Z 2024-12-18T01:09:59.9040931Z Mismatched elements: 1 / 2 (50%) 2024-12-18T01:09:59.9041103Z Max absolute difference: 6.666699636781459e-09 2024-12-18T01:09:59.9041249Z Max relative difference: 2.8571569790287484e-09 2024-12-18T01:09:59.9041390Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:09:59.9041540Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:09:59.9041625Z 2024-12-18T01:09:59.9041722Z 2024-12-18T01:09:59.9041974Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9042057Z 2024-12-18T01:09:59.9042168Z warnings.warn(msg) 2024-12-18T01:09:59.9042254Z 2024-12-18T01:09:59.9042483Z --- Parse Warning: 23 / 105 --- 2024-12-18T01:09:59.9043478Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455. 2024-12-18T01:09:59.9043759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9043843Z 2024-12-18T01:09:59.9044064Z Raises an AssertionError if two items are not equal up to significant 2024-12-18T01:09:59.9044169Z digits. 2024-12-18T01:09:59.9044253Z 2024-12-18T01:09:59.9044447Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:09:59.9044628Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:09:59.9044813Z instead of this function for more consistent floating point 2024-12-18T01:09:59.9044921Z comparisons. 2024-12-18T01:09:59.9045003Z 2024-12-18T01:09:59.9045203Z Given two numbers, check that they are approximately equal. 2024-12-18T01:09:59.9045423Z Approximately equal is defined as the number of significant digits 2024-12-18T01:09:59.9045524Z that agree. 2024-12-18T01:09:59.9045607Z 2024-12-18T01:09:59.9045698Z Parameters 2024-12-18T01:09:59.9045802Z ---------- 2024-12-18T01:09:59.9045894Z actual : scalar 2024-12-18T01:09:59.9046003Z The object to check. 2024-12-18T01:09:59.9046097Z desired : scalar 2024-12-18T01:09:59.9046197Z The expected object. 2024-12-18T01:09:59.9046315Z significant : int, optional 2024-12-18T01:09:59.9046430Z Desired precision, default is 7. 2024-12-18T01:09:59.9046542Z err_msg : str, optional 2024-12-18T01:09:59.9046699Z The error message to be printed in case of failure. 2024-12-18T01:09:59.9046798Z verbose : bool, optional 2024-12-18T01:09:59.9047013Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:59.9047097Z 2024-12-18T01:09:59.9047196Z Raises 2024-12-18T01:09:59.9047284Z ------ 2024-12-18T01:09:59.9047387Z AssertionError 2024-12-18T01:09:59.9047598Z If actual and desired are not equal up to specified precision. 2024-12-18T01:09:59.9047682Z 2024-12-18T01:09:59.9047783Z See Also 2024-12-18T01:09:59.9047875Z -------- 2024-12-18T01:09:59.9048122Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:59.9048255Z relative and/or absolute precision. 2024-12-18T01:09:59.9048464Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:59.9048561Z 2024-12-18T01:09:59.9048648Z Examples 2024-12-18T01:09:59.9048749Z -------- 2024-12-18T01:09:59.9049022Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-12-18T01:09:59.9049272Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-12-18T01:09:59.9049404Z ... significant=8) 2024-12-18T01:09:59.9049657Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-12-18T01:09:59.9049786Z ... significant=8) 2024-12-18T01:09:59.9049899Z Traceback (most recent call last): 2024-12-18T01:09:59.9050057Z ... 2024-12-18T01:09:59.9050155Z AssertionError: 2024-12-18T01:09:59.9050288Z Items are not equal to 8 significant digits: 2024-12-18T01:09:59.9050397Z ACTUAL: 1.234567e-21 2024-12-18T01:09:59.9050492Z DESIRED: 1.2345672e-21 2024-12-18T01:09:59.9050601Z 2024-12-18T01:09:59.9050765Z the evaluated condition that raises the exception is 2024-12-18T01:09:59.9050849Z 2024-12-18T01:09:59.9051038Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-12-18T01:09:59.9051126Z True 2024-12-18T01:09:59.9051225Z 2024-12-18T01:09:59.9051311Z 2024-12-18T01:09:59.9051577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9051660Z 2024-12-18T01:09:59.9051816Z warnings.warn(msg) 2024-12-18T01:09:59.9051914Z 2024-12-18T01:09:59.9052114Z --- Parse Warning: 24 / 105 --- 2024-12-18T01:09:59.9053036Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734. 2024-12-18T01:09:59.9053305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9053392Z 2024-12-18T01:09:59.9053617Z Raises an AssertionError if two array_like objects are not equal. 2024-12-18T01:09:59.9053702Z 2024-12-18T01:09:59.9053925Z Given two array_like objects, check that the shape is equal and all 2024-12-18T01:09:59.9054146Z elements of these objects are equal (but see the Notes for the special 2024-12-18T01:09:59.9054364Z handling of a scalar). An exception is raised at shape mismatch or 2024-12-18T01:09:59.9054593Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-12-18T01:09:59.9054815Z are compared like numbers, no assertion is raised if both objects have 2024-12-18T01:09:59.9054941Z NaNs in the same positions. 2024-12-18T01:09:59.9055030Z 2024-12-18T01:09:59.9055275Z The usual caution for verifying equality with floating point numbers is 2024-12-18T01:09:59.9055370Z advised. 2024-12-18T01:09:59.9055471Z 2024-12-18T01:09:59.9055565Z Parameters 2024-12-18T01:09:59.9055657Z ---------- 2024-12-18T01:09:59.9055761Z x : array_like 2024-12-18T01:09:59.9055869Z The actual object to check. 2024-12-18T01:09:59.9055974Z y : array_like 2024-12-18T01:09:59.9056082Z The desired, expected object. 2024-12-18T01:09:59.9056185Z err_msg : str, optional 2024-12-18T01:09:59.9056354Z The error message to be printed in case of failure. 2024-12-18T01:09:59.9056457Z verbose : bool, optional 2024-12-18T01:09:59.9056681Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:59.9056783Z strict : bool, optional 2024-12-18T01:09:59.9056983Z If True, raise an AssertionError when either the shape or the data 2024-12-18T01:09:59.9057174Z type of the array_like objects does not match. The special 2024-12-18T01:09:59.9057379Z handling for scalars mentioned in the Notes section is disabled. 2024-12-18T01:09:59.9057472Z 2024-12-18T01:09:59.9057562Z Raises 2024-12-18T01:09:59.9057668Z ------ 2024-12-18T01:09:59.9057764Z AssertionError 2024-12-18T01:09:59.9057901Z If actual and desired objects are not equal. 2024-12-18T01:09:59.9057994Z 2024-12-18T01:09:59.9058079Z See Also 2024-12-18T01:09:59.9058177Z -------- 2024-12-18T01:09:59.9058412Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:59.9058540Z relative and/or absolute precision. 2024-12-18T01:09:59.9058764Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:59.9058846Z 2024-12-18T01:09:59.9058943Z Notes 2024-12-18T01:09:59.9059030Z ----- 2024-12-18T01:09:59.9059215Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-12-18T01:09:59.9059509Z function checks that each element of the array_like object is equal to 2024-12-18T01:09:59.9059734Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-12-18T01:09:59.9059829Z 2024-12-18T01:09:59.9059918Z Examples 2024-12-18T01:09:59.9060015Z -------- 2024-12-18T01:09:59.9060153Z The first assert does not raise an exception: 2024-12-18T01:09:59.9060236Z 2024-12-18T01:09:59.9060407Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-12-18T01:09:59.9060535Z ... [np.exp(0),2.33333, np.nan]) 2024-12-18T01:09:59.9060633Z 2024-12-18T01:09:59.9060859Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-12-18T01:09:59.9061028Z functions for these cases instead: 2024-12-18T01:09:59.9061125Z 2024-12-18T01:09:59.9061271Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-12-18T01:09:59.9061419Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-12-18T01:09:59.9061539Z ... rtol=1e-10, atol=0) 2024-12-18T01:09:59.9061635Z 2024-12-18T01:09:59.9061841Z As mentioned in the Notes section, `assert_array_equal` has special 2024-12-18T01:09:59.9062068Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-12-18T01:09:59.9062163Z 2024-12-18T01:09:59.9062274Z >>> x = np.full((2, 5), fill_value=3) 2024-12-18T01:09:59.9062407Z >>> np.testing.assert_array_equal(x, 3) 2024-12-18T01:09:59.9062490Z 2024-12-18T01:09:59.9062704Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-12-18T01:09:59.9062805Z array: 2024-12-18T01:09:59.9062889Z 2024-12-18T01:09:59.9063055Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-12-18T01:09:59.9063169Z Traceback (most recent call last): 2024-12-18T01:09:59.9063267Z ... 2024-12-18T01:09:59.9063364Z AssertionError: 2024-12-18T01:09:59.9063463Z Arrays are not equal 2024-12-18T01:09:59.9063567Z 2024-12-18T01:09:59.9063666Z (shapes (2, 5), () mismatch) 2024-12-18T01:09:59.9063785Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-12-18T01:09:59.9063876Z [3, 3, 3, 3, 3]]) 2024-12-18T01:09:59.9063974Z y: torch.ndarray(3) 2024-12-18T01:09:59.9064070Z 2024-12-18T01:09:59.9064285Z The `strict` parameter also ensures that the array data types match: 2024-12-18T01:09:59.9064380Z 2024-12-18T01:09:59.9064478Z >>> x = np.array([2, 2, 2]) 2024-12-18T01:09:59.9064609Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-12-18T01:09:59.9064770Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-12-18T01:09:59.9064884Z Traceback (most recent call last): 2024-12-18T01:09:59.9064986Z ... 2024-12-18T01:09:59.9065083Z AssertionError: 2024-12-18T01:09:59.9065193Z Arrays are not equal 2024-12-18T01:09:59.9065282Z 2024-12-18T01:09:59.9065433Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-12-18T01:09:59.9065548Z x: torch.ndarray([2, 2, 2]) 2024-12-18T01:09:59.9065656Z y: torch.ndarray([2., 2., 2.]) 2024-12-18T01:09:59.9065753Z 2024-12-18T01:09:59.9066006Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9066089Z 2024-12-18T01:09:59.9066200Z warnings.warn(msg) 2024-12-18T01:09:59.9066287Z 2024-12-18T01:09:59.9066504Z --- Parse Warning: 25 / 105 --- 2024-12-18T01:09:59.9067437Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840. 2024-12-18T01:09:59.9067718Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9067802Z 2024-12-18T01:09:59.9068017Z Raises an AssertionError if two objects are not equal up to desired 2024-12-18T01:09:59.9068205Z precision. 2024-12-18T01:09:59.9068288Z 2024-12-18T01:09:59.9068592Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:09:59.9068778Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:09:59.9068984Z instead of this function for more consistent floating point 2024-12-18T01:09:59.9069082Z comparisons. 2024-12-18T01:09:59.9069167Z 2024-12-18T01:09:59.9069422Z The test verifies identical shapes and that the elements of ``actual`` and 2024-12-18T01:09:59.9069519Z ``desired`` satisfy. 2024-12-18T01:09:59.9069618Z 2024-12-18T01:09:59.9069754Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-12-18T01:09:59.9069839Z 2024-12-18T01:09:59.9070144Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:09:59.9070389Z actual implementation did up to rounding vagaries. An exception is raised 2024-12-18T01:09:59.9070637Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-12-18T01:09:59.9070856Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-12-18T01:09:59.9070992Z objects have NaNs in the same positions. 2024-12-18T01:09:59.9071076Z 2024-12-18T01:09:59.9071167Z Parameters 2024-12-18T01:09:59.9071269Z ---------- 2024-12-18T01:09:59.9071360Z x : array_like 2024-12-18T01:09:59.9071480Z The actual object to check. 2024-12-18T01:09:59.9071570Z y : array_like 2024-12-18T01:09:59.9071680Z The desired, expected object. 2024-12-18T01:09:59.9071793Z decimal : int, optional 2024-12-18T01:09:59.9071909Z Desired precision, default is 6. 2024-12-18T01:09:59.9072019Z err_msg : str, optional 2024-12-18T01:09:59.9072177Z The error message to be printed in case of failure. 2024-12-18T01:09:59.9072278Z verbose : bool, optional 2024-12-18T01:09:59.9072494Z If True, the conflicting values are appended to the error message. 2024-12-18T01:09:59.9072580Z 2024-12-18T01:09:59.9072679Z Raises 2024-12-18T01:09:59.9072767Z ------ 2024-12-18T01:09:59.9072874Z AssertionError 2024-12-18T01:09:59.9073073Z If actual and desired are not equal up to specified precision. 2024-12-18T01:09:59.9073156Z 2024-12-18T01:09:59.9073255Z See Also 2024-12-18T01:09:59.9073343Z -------- 2024-12-18T01:09:59.9073594Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:09:59.9073724Z relative and/or absolute precision. 2024-12-18T01:09:59.9073934Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:09:59.9074034Z 2024-12-18T01:09:59.9074122Z Examples 2024-12-18T01:09:59.9074226Z -------- 2024-12-18T01:09:59.9074363Z the first assert does not raise an exception 2024-12-18T01:09:59.9074446Z 2024-12-18T01:09:59.9074632Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-12-18T01:09:59.9074756Z ... [1.0,2.333,np.nan]) 2024-12-18T01:09:59.9074851Z 2024-12-18T01:09:59.9075030Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:09:59.9075177Z ... [1.0,2.33339,np.nan], decimal=5) 2024-12-18T01:09:59.9075290Z Traceback (most recent call last): 2024-12-18T01:09:59.9075374Z ... 2024-12-18T01:09:59.9075483Z AssertionError: 2024-12-18T01:09:59.9075608Z Arrays are not almost equal to 5 decimals 2024-12-18T01:09:59.9075709Z 2024-12-18T01:09:59.9075819Z Mismatched elements: 1 / 3 (33.3%) 2024-12-18T01:09:59.9075950Z Max absolute difference: 5.999999999994898e-05 2024-12-18T01:09:59.9076099Z Max relative difference: 2.5713661239633743e-05 2024-12-18T01:09:59.9076264Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:09:59.9076437Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-12-18T01:09:59.9076581Z 2024-12-18T01:09:59.9076771Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:09:59.9076896Z ... [1.0,2.33333, 5], decimal=5) 2024-12-18T01:09:59.9077009Z Traceback (most recent call last): 2024-12-18T01:09:59.9077107Z ... 2024-12-18T01:09:59.9077205Z AssertionError: 2024-12-18T01:09:59.9077348Z Arrays are not almost equal to 5 decimals 2024-12-18T01:09:59.9077443Z 2024-12-18T01:09:59.9077554Z x and y nan location mismatch: 2024-12-18T01:09:59.9077730Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:09:59.9077889Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-12-18T01:09:59.9077990Z 2024-12-18T01:09:59.9078130Z 2024-12-18T01:09:59.9078399Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9078484Z 2024-12-18T01:09:59.9078588Z warnings.warn(msg) 2024-12-18T01:09:59.9078685Z 2024-12-18T01:09:59.9078903Z --- Parse Warning: 26 / 105 --- 2024-12-18T01:09:59.9079864Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-12-18T01:09:59.9080134Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9080365Z Context manager that resets warning registry for catching warnings 2024-12-18T01:09:59.9080451Z 2024-12-18T01:09:59.9080698Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-12-18T01:09:59.9080945Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-12-18T01:09:59.9081186Z it impossible to retrigger the warning in this module, whatever you put in 2024-12-18T01:09:59.9081444Z the warnings filters. This context manager accepts a sequence of `modules` 2024-12-18T01:09:59.9081589Z as a keyword argument to its constructor and: 2024-12-18T01:09:59.9081692Z 2024-12-18T01:09:59.9081923Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-12-18T01:09:59.9082015Z on entry; 2024-12-18T01:09:59.9082220Z * resets ``__warningregistry__`` to its previous state on exit. 2024-12-18T01:09:59.9082307Z 2024-12-18T01:09:59.9082551Z This makes it possible to trigger any warning afresh inside the context 2024-12-18T01:09:59.9082736Z manager without disturbing the state of warnings outside. 2024-12-18T01:09:59.9082822Z 2024-12-18T01:09:59.9083078Z For compatibility with Python 3.0, please consider all arguments to be 2024-12-18T01:09:59.9083175Z keyword-only. 2024-12-18T01:09:59.9083276Z 2024-12-18T01:09:59.9083368Z Parameters 2024-12-18T01:09:59.9083461Z ---------- 2024-12-18T01:09:59.9083578Z record : bool, optional 2024-12-18T01:09:59.9083768Z Specifies whether warnings should be captured by a custom 2024-12-18T01:09:59.9084016Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-12-18T01:09:59.9084225Z returned by the context manager. Otherwise None is returned by the 2024-12-18T01:09:59.9084465Z context manager. The objects appended to the list are arguments whose 2024-12-18T01:09:59.9084640Z attributes mirror the arguments to ``showwarning()``. 2024-12-18T01:09:59.9084747Z modules : sequence, optional 2024-12-18T01:09:59.9084978Z Sequence of modules for which to reset warnings registry on entry and 2024-12-18T01:09:59.9085174Z restore on exit. To work correctly, all 'ignore' filters should 2024-12-18T01:09:59.9085303Z filter by one of these modules. 2024-12-18T01:09:59.9085387Z 2024-12-18T01:09:59.9085490Z Examples 2024-12-18T01:09:59.9085640Z -------- 2024-12-18T01:09:59.9085737Z >>> import warnings 2024-12-18T01:09:59.9085933Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-12-18T01:09:59.9086060Z ... modules=[np.core.fromnumeric]): 2024-12-18T01:09:59.9086196Z ... warnings.simplefilter('always') 2024-12-18T01:09:59.9086421Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-12-18T01:09:59.9086606Z ... # do something that raises a warning but ignore those in 2024-12-18T01:09:59.9086713Z ... # np.core.fromnumeric 2024-12-18T01:09:59.9086800Z 2024-12-18T01:09:59.9087064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9087149Z 2024-12-18T01:09:59.9087314Z warnings.warn(msg) 2024-12-18T01:09:59.9087398Z 2024-12-18T01:09:59.9087594Z --- Parse Warning: 27 / 105 --- 2024-12-18T01:09:59.9088516Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2024-12-18T01:09:59.9088778Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9089005Z Applies a 1D convolution over a quantized input signal composed of 2024-12-18T01:09:59.9089119Z several quantized input planes. 2024-12-18T01:09:59.9089217Z 2024-12-18T01:09:59.9089430Z For details on input arguments, parameters, and implementation see 2024-12-18T01:09:59.9089544Z :class:`~torch.nn.Conv1d`. 2024-12-18T01:09:59.9089644Z 2024-12-18T01:09:59.9089742Z .. note:: 2024-12-18T01:09:59.9089958Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-12-18T01:09:59.9090045Z 2024-12-18T01:09:59.9090150Z .. note:: 2024-12-18T01:09:59.9090328Z Only `torch.quint8` is supported for the input data type. 2024-12-18T01:09:59.9090420Z 2024-12-18T01:09:59.9090512Z 2024-12-18T01:09:59.9090605Z Attributes: 2024-12-18T01:09:59.9090826Z weight (Tensor): packed tensor derived from the learnable weight 2024-12-18T01:09:59.9090931Z parameter. 2024-12-18T01:09:59.9091078Z scale (Tensor): scalar for the output scale 2024-12-18T01:09:59.9091253Z zero_point (Tensor): scalar for the output zero point 2024-12-18T01:09:59.9091337Z 2024-12-18T01:09:59.9091502Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-12-18T01:09:59.9091584Z 2024-12-18T01:09:59.9091676Z Examples:: 2024-12-18T01:09:59.9091773Z 2024-12-18T01:09:59.9091927Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-12-18T01:09:59.9092075Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-12-18T01:09:59.9092192Z >>> input = torch.randn(20, 16, 100) 2024-12-18T01:09:59.9092321Z >>> # quantize input to quint8 2024-12-18T01:09:59.9092422Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9092638Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-12-18T01:09:59.9092782Z ... dtype=torch.quint8) 2024-12-18T01:09:59.9092885Z >>> output = m(q_input) 2024-12-18T01:09:59.9092982Z 2024-12-18T01:09:59.9093069Z 2024-12-18T01:09:59.9093321Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9093417Z 2024-12-18T01:09:59.9093516Z warnings.warn(msg) 2024-12-18T01:09:59.9093612Z 2024-12-18T01:09:59.9093800Z --- Parse Warning: 28 / 105 --- 2024-12-18T01:09:59.9094696Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-12-18T01:09:59.9095020Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9095150Z A quantized long short-term memory (LSTM). 2024-12-18T01:09:59.9095245Z 2024-12-18T01:09:59.9095524Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-12-18T01:09:59.9095622Z 2024-12-18T01:09:59.9095715Z Attributes: 2024-12-18T01:09:59.9095855Z layers : instances of the `_LSTMLayer` 2024-12-18T01:09:59.9095940Z 2024-12-18T01:09:59.9096031Z .. note:: 2024-12-18T01:09:59.9096257Z To access the weights and biases, you need to access them per layer. 2024-12-18T01:09:59.9096434Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-12-18T01:09:59.9096589Z 2024-12-18T01:09:59.9096684Z Examples:: 2024-12-18T01:09:59.9096785Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9096907Z >>> custom_module_config = { 2024-12-18T01:09:59.9097051Z ... 'float_to_observed_custom_module_class': { 2024-12-18T01:09:59.9097194Z ... nn.LSTM: nn.quantizable.LSTM, 2024-12-18T01:09:59.9097284Z ... }, 2024-12-18T01:09:59.9097449Z ... 'observed_to_quantized_custom_module_class': { 2024-12-18T01:09:59.9097594Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-12-18T01:09:59.9097684Z ... } 2024-12-18T01:09:59.9097786Z ... } 2024-12-18T01:09:59.9098006Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-12-18T01:09:59.9098234Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-12-18T01:09:59.9098321Z 2024-12-18T01:09:59.9098577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9098676Z 2024-12-18T01:09:59.9098776Z warnings.warn(msg) 2024-12-18T01:09:59.9098875Z 2024-12-18T01:09:59.9099067Z --- Parse Warning: 29 / 105 --- 2024-12-18T01:09:59.9100128Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-12-18T01:09:59.9100389Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9100563Z Squashes the sparse masks into the appropriate tensors. 2024-12-18T01:09:59.9100658Z 2024-12-18T01:09:59.9100865Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-12-18T01:09:59.9101064Z the module will have a `sparse_params` dict attached to it. 2024-12-18T01:09:59.9101152Z 2024-12-18T01:09:59.9101251Z Args: 2024-12-18T01:09:59.9101439Z params_to_keep: List of keys to save in the module or a dict 2024-12-18T01:09:59.9101604Z representing the modules and keys that will have 2024-12-18T01:09:59.9101741Z sparsity parameters saved 2024-12-18T01:09:59.9101957Z params_to_keep_per_layer: Dict to specify the params that should be 2024-12-18T01:09:59.9102128Z saved for specific layers. The keys in the dict 2024-12-18T01:09:59.9102286Z should be the module fqn, while the values should 2024-12-18T01:09:59.9102463Z be a list of strings with the names of the variables 2024-12-18T01:09:59.9102593Z to save in the `sparse_params` 2024-12-18T01:09:59.9102677Z 2024-12-18T01:09:59.9102782Z Examples: 2024-12-18T01:09:59.9102921Z >>> # xdoctest: +SKIP("locals are undefined") 2024-12-18T01:09:59.9103054Z >>> # Don't save any sparse params 2024-12-18T01:09:59.9103172Z >>> sparsifier.squash_mask() 2024-12-18T01:09:59.9103419Z >>> hasattr(model.submodule1, 'sparse_params') 2024-12-18T01:09:59.9103511Z False 2024-12-18T01:09:59.9103595Z 2024-12-18T01:09:59.9103731Z >>> # Keep sparse params per layer 2024-12-18T01:09:59.9103846Z >>> sparsifier.squash_mask( 2024-12-18T01:09:59.9103976Z ... params_to_keep_per_layer={ 2024-12-18T01:09:59.9104111Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-12-18T01:09:59.9104240Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:09:59.9104344Z ... }) 2024-12-18T01:09:59.9104502Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:09:59.9104671Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:59.9104833Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:09:59.9104938Z {'baz': 0.1} 2024-12-18T01:09:59.9105026Z 2024-12-18T01:09:59.9105153Z >>> # Keep sparse params for all layers 2024-12-18T01:09:59.9105340Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-12-18T01:09:59.9105496Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:09:59.9105608Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:59.9105767Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:09:59.9105866Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:59.9105962Z 2024-12-18T01:09:59.9106160Z >>> # Keep some sparse params for all layers, and specific ones for 2024-12-18T01:09:59.9106276Z >>> # some other layers 2024-12-18T01:09:59.9106390Z >>> sparsifier.squash_mask( 2024-12-18T01:09:59.9106534Z ... params_to_keep=('foo', 'bar'), 2024-12-18T01:09:59.9106651Z ... params_to_keep_per_layer={ 2024-12-18T01:09:59.9106780Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:09:59.9106888Z ... }) 2024-12-18T01:09:59.9107045Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:09:59.9107159Z {'foo': 42, 'bar': 24} 2024-12-18T01:09:59.9107322Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:09:59.9107448Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-12-18T01:09:59.9107537Z 2024-12-18T01:09:59.9107795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9107893Z 2024-12-18T01:09:59.9107993Z warnings.warn(msg) 2024-12-18T01:09:59.9108091Z 2024-12-18T01:09:59.9108284Z --- Parse Warning: 30 / 105 --- 2024-12-18T01:09:59.9109404Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-12-18T01:09:59.9109689Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9109776Z 2024-12-18T01:09:59.9110046Z Config object that specifies the supported data types passed as arguments to 2024-12-18T01:09:59.9110284Z quantize ops in the reference model spec, for input and output activations, 2024-12-18T01:09:59.9110399Z weights, and biases. 2024-12-18T01:09:59.9110485Z 2024-12-18T01:09:59.9110648Z For example, consider the following reference model: 2024-12-18T01:09:59.9110747Z 2024-12-18T01:09:59.9110907Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-12-18T01:09:59.9111008Z 2024-12-18T01:09:59.9111228Z The pattern in the square brackets refers to the reference pattern of 2024-12-18T01:09:59.9111480Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-12-18T01:09:59.9111711Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-12-18T01:09:59.9111995Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-12-18T01:09:59.9112231Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-12-18T01:09:59.9112340Z the second quantize op (quant2). 2024-12-18T01:09:59.9112442Z 2024-12-18T01:09:59.9112662Z Note that the dtype here does not refer to the interface dtypes of the 2024-12-18T01:09:59.9112888Z op. For example, the "input dtype" here is not the dtype of the input 2024-12-18T01:09:59.9113106Z tensor passed to the quantized linear op. Though it can still be the 2024-12-18T01:09:59.9113306Z same as the interface dtype, this is not always the case, e.g. the 2024-12-18T01:09:59.9113594Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-12-18T01:09:59.9113811Z specified in the DTypeConfig would still be quint8. The semantics of 2024-12-18T01:09:59.9114034Z dtypes here are the same as the semantics of the dtypes specified in 2024-12-18T01:09:59.9114131Z the observers. 2024-12-18T01:09:59.9114216Z 2024-12-18T01:09:59.9114435Z These dtypes are matched against the ones specified in the user's 2024-12-18T01:09:59.9114655Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-12-18T01:09:59.9114886Z specified in the DTypeConfig (if any), then we will quantize the given 2024-12-18T01:09:59.9115111Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-12-18T01:09:59.9115235Z the pattern will not be quantized. 2024-12-18T01:09:59.9115319Z 2024-12-18T01:09:59.9115417Z Example usage:: 2024-12-18T01:09:59.9115512Z 2024-12-18T01:09:59.9115619Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:09:59.9115746Z >>> dtype_config1 = DTypeConfig( 2024-12-18T01:09:59.9115857Z ... input_dtype=torch.quint8, 2024-12-18T01:09:59.9115979Z ... output_dtype=torch.quint8, 2024-12-18T01:09:59.9116092Z ... weight_dtype=torch.qint8, 2024-12-18T01:09:59.9116199Z ... bias_dtype=torch.float) 2024-12-18T01:09:59.9116294Z 2024-12-18T01:09:59.9116402Z >>> dtype_config2 = DTypeConfig( 2024-12-18T01:09:59.9116545Z ... input_dtype=DTypeWithConstraints( 2024-12-18T01:09:59.9116647Z ... dtype=torch.quint8, 2024-12-18T01:09:59.9116754Z ... quant_min_lower_bound=0, 2024-12-18T01:09:59.9116883Z ... quant_max_upper_bound=255, 2024-12-18T01:09:59.9116970Z ... ), 2024-12-18T01:09:59.9117114Z ... output_dtype=DTypeWithConstraints( 2024-12-18T01:09:59.9117217Z ... dtype=torch.quint8, 2024-12-18T01:09:59.9117324Z ... quant_min_lower_bound=0, 2024-12-18T01:09:59.9117454Z ... quant_max_upper_bound=255, 2024-12-18T01:09:59.9117542Z ... ), 2024-12-18T01:09:59.9117687Z ... weight_dtype=DTypeWithConstraints( 2024-12-18T01:09:59.9117791Z ... dtype=torch.qint8, 2024-12-18T01:09:59.9117925Z ... quant_min_lower_bound=-128, 2024-12-18T01:09:59.9118039Z ... quant_max_upper_bound=127, 2024-12-18T01:09:59.9118126Z ... ), 2024-12-18T01:09:59.9118245Z ... bias_dtype=torch.float) 2024-12-18T01:09:59.9118332Z 2024-12-18T01:09:59.9118454Z >>> dtype_config1.input_dtype 2024-12-18T01:09:59.9118546Z torch.quint8 2024-12-18T01:09:59.9118630Z 2024-12-18T01:09:59.9118750Z >>> dtype_config2.input_dtype 2024-12-18T01:09:59.9118841Z torch.quint8 2024-12-18T01:09:59.9118936Z 2024-12-18T01:09:59.9119074Z >>> dtype_config2.input_dtype_with_constraints 2024-12-18T01:09:59.9119622Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-12-18T01:09:59.9119706Z 2024-12-18T01:09:59.9119960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9120109Z 2024-12-18T01:09:59.9120209Z warnings.warn(msg) 2024-12-18T01:09:59.9120303Z 2024-12-18T01:09:59.9120506Z --- Parse Warning: 31 / 105 --- 2024-12-18T01:09:59.9121738Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-12-18T01:09:59.9122018Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9122104Z 2024-12-18T01:09:59.9122395Z Takes in optional filter values and generates two tables with desired information. 2024-12-18T01:09:59.9122535Z 2024-12-18T01:09:59.9122760Z The generated tables are presented in both a list-of-lists format 2024-12-18T01:09:59.9122843Z 2024-12-18T01:09:59.9123051Z The reason for the two tables are that they handle different things: 2024-12-18T01:09:59.9123229Z 1.) the first table handles all tensor level information 2024-12-18T01:09:59.9123445Z 2.) the second table handles and displays all channel based information 2024-12-18T01:09:59.9123543Z 2024-12-18T01:09:59.9123866Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:09:59.9124206Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:09:59.9124557Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:09:59.9124641Z 2024-12-18T01:09:59.9124751Z Tensor table columns: 2024-12-18T01:09:59.9124947Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:59.9125117Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:09:59.9125203Z 2024-12-18T01:09:59.9125324Z Per-Channel table columns: 2024-12-18T01:09:59.9125543Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:59.9125713Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:09:59.9125811Z 2024-12-18T01:09:59.9125898Z Args: 2024-12-18T01:09:59.9126175Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:09:59.9126290Z contain this filter substring 2024-12-18T01:09:59.9126467Z Default = "", results in all the features being printed 2024-12-18T01:09:59.9126728Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:59.9126978Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:59.9127075Z 2024-12-18T01:09:59.9127189Z Returns a dictionary with two keys: 2024-12-18T01:09:59.9127381Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-12-18T01:09:59.9127509Z "tensor_level_info", "channel_level_info" 2024-12-18T01:09:59.9127623Z Each key maps to a tuple with: 2024-12-18T01:09:59.9127757Z A list of the headers of each table 2024-12-18T01:09:59.9127942Z A list of lists containing the table information row by row 2024-12-18T01:09:59.9128129Z The 0th index row will contain the headers of the columns 2024-12-18T01:09:59.9128255Z The rest of the rows will contain data 2024-12-18T01:09:59.9128353Z 2024-12-18T01:09:59.9128447Z Example Use: 2024-12-18T01:09:59.9128577Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9128746Z >>> mod_report_visualizer.generate_filtered_tables( 2024-12-18T01:09:59.9128869Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:59.9128992Z ... module_fqn_filter = "block1" 2024-12-18T01:09:59.9129324Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-12-18T01:09:59.9129422Z 2024-12-18T01:09:59.9129676Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9129760Z 2024-12-18T01:09:59.9129870Z warnings.warn(msg) 2024-12-18T01:09:59.9129953Z 2024-12-18T01:09:59.9130160Z --- Parse Warning: 32 / 105 --- 2024-12-18T01:09:59.9131665Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-12-18T01:09:59.9132027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9132115Z 2024-12-18T01:09:59.9132385Z Takes in optional filter values and prints out formatted tables of the information. 2024-12-18T01:09:59.9132485Z 2024-12-18T01:09:59.9132827Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-12-18T01:09:59.9133006Z 1.) the first table handles all tensor level information 2024-12-18T01:09:59.9133223Z 2.) the second table handles and displays all channel based information 2024-12-18T01:09:59.9133321Z 2024-12-18T01:09:59.9133638Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:09:59.9133966Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:09:59.9134332Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:09:59.9134416Z 2024-12-18T01:09:59.9134536Z Tensor table columns: 2024-12-18T01:09:59.9134729Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:59.9134898Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:09:59.9134985Z 2024-12-18T01:09:59.9135097Z Per-Channel table columns: 2024-12-18T01:09:59.9135196Z 2024-12-18T01:09:59.9135414Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:09:59.9135592Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:09:59.9135677Z 2024-12-18T01:09:59.9135766Z Args: 2024-12-18T01:09:59.9136227Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:09:59.9136361Z contain this filter substring 2024-12-18T01:09:59.9136539Z Default = "", results in all the features being printed 2024-12-18T01:09:59.9136802Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:59.9137063Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:59.9137150Z 2024-12-18T01:09:59.9137243Z Example Use: 2024-12-18T01:09:59.9163050Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9163368Z >>> mod_report_visualizer.generate_table_visualization( 2024-12-18T01:09:59.9163517Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:59.9163630Z ... module_fqn_filter = "block1" 2024-12-18T01:09:59.9163721Z ... ) 2024-12-18T01:09:59.9163932Z >>> # prints out neatly formatted table with per_channel_min info 2024-12-18T01:09:59.9164060Z >>> # for all modules in block 1 of the model 2024-12-18T01:09:59.9164158Z 2024-12-18T01:09:59.9164417Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9164523Z 2024-12-18T01:09:59.9164637Z warnings.warn(msg) 2024-12-18T01:09:59.9164722Z 2024-12-18T01:09:59.9164988Z --- Parse Warning: 33 / 105 --- 2024-12-18T01:09:59.9166439Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566. 2024-12-18T01:09:59.9166716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9166802Z 2024-12-18T01:09:59.9167040Z Takes in a feature and optional module_filter and plots of the desired data. 2024-12-18T01:09:59.9167141Z 2024-12-18T01:09:59.9167413Z For per channel features, it averages the value across the channels and plots a point 2024-12-18T01:09:59.9167770Z per module. The reason for this is that for models with hundreds of channels, it can 2024-12-18T01:09:59.9168046Z be hard to differentiate one channel line from another, and so the point of generating 2024-12-18T01:09:59.9168325Z a single average point per module is to give a sense of general trends that encourage 2024-12-18T01:09:59.9168431Z further deep dives. 2024-12-18T01:09:59.9168514Z 2024-12-18T01:09:59.9168617Z Note: 2024-12-18T01:09:59.9168883Z Only features in the report that have tensor value data are plottable by this class 2024-12-18T01:09:59.9169062Z When the tensor information is plotted, it will plot: 2024-12-18T01:09:59.9169201Z idx as the x val, feature value as the y_val 2024-12-18T01:09:59.9169385Z When the channel information is plotted, it will plot: 2024-12-18T01:09:59.9169648Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-12-18T01:09:59.9169879Z The reason for this is that we want to be able to compare values across the 2024-12-18T01:09:59.9170130Z channels for same layer, and it will be hard if values are staggered by idx 2024-12-18T01:09:59.9170300Z This means each module is represented by only 1 x value 2024-12-18T01:09:59.9170407Z Args: 2024-12-18T01:09:59.9170633Z feature_filter (str): Filters the features presented to only those that 2024-12-18T01:09:59.9170760Z contain this filter substring 2024-12-18T01:09:59.9171022Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:59.9171271Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:59.9171369Z 2024-12-18T01:09:59.9171465Z Example Use: 2024-12-18T01:09:59.9171609Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9171769Z >>> mod_report_visualizer.generate_plot_visualization( 2024-12-18T01:09:59.9171907Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:59.9172024Z ... module_fqn_filter = "block1" 2024-12-18T01:09:59.9172110Z ... ) 2024-12-18T01:09:59.9172308Z >>> # outputs line plot of per_channel_min information for all 2024-12-18T01:09:59.9172496Z >>> # modules in block1 of model each channel gets it's own line, 2024-12-18T01:09:59.9172681Z >>> # and it's plotted across the in-order modules on the x-axis 2024-12-18T01:09:59.9172763Z 2024-12-18T01:09:59.9173016Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9173111Z 2024-12-18T01:09:59.9173209Z warnings.warn(msg) 2024-12-18T01:09:59.9173303Z 2024-12-18T01:09:59.9173503Z --- Parse Warning: 34 / 105 --- 2024-12-18T01:09:59.9174786Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646. 2024-12-18T01:09:59.9175048Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9175219Z 2024-12-18T01:09:59.9175497Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-12-18T01:09:59.9175580Z 2024-12-18T01:09:59.9175680Z Note: 2024-12-18T01:09:59.9175944Z Only features in the report that have tensor value data can be viewed as a histogram 2024-12-18T01:09:59.9176216Z If you want to plot a histogram from all the channel values of a specific feature for 2024-12-18T01:09:59.9176463Z a specific model, make sure to specify both the model and the feature properly 2024-12-18T01:09:59.9176707Z in the filters and you should be able to see a distribution of the channel data 2024-12-18T01:09:59.9176803Z 2024-12-18T01:09:59.9176890Z Args: 2024-12-18T01:09:59.9177253Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:09:59.9177369Z contain this filter substring 2024-12-18T01:09:59.9177546Z Default = "", results in all the features being printed 2024-12-18T01:09:59.9177812Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:09:59.9178057Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:09:59.9178296Z num_bins (int, optional): The number of bins to create the histogram with 2024-12-18T01:09:59.9178483Z Default = 10, the values will be split into 10 equal sized bins 2024-12-18T01:09:59.9178579Z 2024-12-18T01:09:59.9178673Z Example Use: 2024-12-18T01:09:59.9178786Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9179086Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-12-18T01:09:59.9179219Z ... feature_filter = "per_channel_min", 2024-12-18T01:09:59.9179344Z ... module_fqn_filter = "block1" 2024-12-18T01:09:59.9179431Z ... ) 2024-12-18T01:09:59.9179721Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-12-18T01:09:59.9179983Z information is gathered across all channels for all modules in block 1 for the 2024-12-18T01:09:59.9180214Z per_channel_min and is displayed in a histogram of equally sized bins 2024-12-18T01:09:59.9180298Z 2024-12-18T01:09:59.9180551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9180648Z 2024-12-18T01:09:59.9180747Z warnings.warn(msg) 2024-12-18T01:09:59.9180842Z 2024-12-18T01:09:59.9181033Z --- Parse Warning: 35 / 105 --- 2024-12-18T01:09:59.9181975Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=660. 2024-12-18T01:09:59.9182234Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:09:59.9182319Z 2024-12-18T01:09:59.9182598Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-12-18T01:09:59.9182863Z The submesh created consists of the dimensions and the communicators indicated by 2024-12-18T01:09:59.9182969Z ``mesh_dim_names`` 2024-12-18T01:09:59.9183053Z 2024-12-18T01:09:59.9183138Z Args: 2024-12-18T01:09:59.9183388Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-12-18T01:09:59.9183576Z mesh dimension of the DeviceMesh to create the submesh for. 2024-12-18T01:09:59.9183677Z Returns: 2024-12-18T01:09:59.9183785Z A :class:`DeviceMesh` object 2024-12-18T01:09:59.9183882Z 2024-12-18T01:09:59.9184166Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-12-18T01:09:59.9184266Z In the first example: 2024-12-18T01:09:59.9184532Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-12-18T01:09:59.9184836Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-12-18T01:09:59.9185072Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-12-18T01:09:59.9185293Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-12-18T01:09:59.9185523Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-12-18T01:09:59.9185741Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-12-18T01:09:59.9185823Z 2024-12-18T01:09:59.9185936Z In the second example: 2024-12-18T01:09:59.9186257Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-12-18T01:09:59.9186533Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-12-18T01:09:59.9186794Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-12-18T01:09:59.9187071Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-12-18T01:09:59.9187155Z 2024-12-18T01:09:59.9187259Z Example:: 2024-12-18T01:09:59.9187383Z >>> # xdoctest: +SKIP("no rank") 2024-12-18T01:09:59.9187556Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-12-18T01:09:59.9187654Z >>> 2024-12-18T01:09:59.9187857Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-12-18T01:09:59.9188016Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-12-18T01:09:59.9188274Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:09:59.9188467Z >>> tp_mesh = mesh_2d["tp"] 2024-12-18T01:09:59.9188585Z >>> dp_mesh = mesh_2d["dp"] 2024-12-18T01:09:59.9188671Z >>> 2024-12-18T01:09:59.9188795Z >>> # Initialize a 3D mesh. 2024-12-18T01:09:59.9189072Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-12-18T01:09:59.9189389Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-12-18T01:09:59.9189507Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-12-18T01:09:59.9189622Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-12-18T01:09:59.9189725Z 2024-12-18T01:09:59.9190394Z 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)) 2024-12-18T01:09:59.9190495Z 2024-12-18T01:09:59.9190741Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:09:59.9190871Z ^ 2024-12-18T01:09:59.9190975Z warnings.warn(msg) 2024-12-18T01:09:59.9191057Z 2024-12-18T01:09:59.9191272Z --- Parse Warning: 36 / 105 --- 2024-12-18T01:09:59.9192213Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3063. 2024-12-18T01:09:59.9192487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9192570Z 2024-12-18T01:09:59.9192807Z Gathers picklable objects from the whole group in a single process. 2024-12-18T01:09:59.9192889Z 2024-12-18T01:09:59.9193123Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-12-18T01:09:59.9193289Z object must be picklable in order to be gathered. 2024-12-18T01:09:59.9193371Z 2024-12-18T01:09:59.9193470Z Args: 2024-12-18T01:09:59.9193604Z obj (Any): Input object. Must be picklable. 2024-12-18T01:09:59.9193881Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-12-18T01:09:59.9194082Z should be correctly sized as the size of the group for this 2024-12-18T01:09:59.9194299Z collective and will contain the output. Must be ``None`` on non-dst 2024-12-18T01:09:59.9194415Z ranks. (default is ``None``) 2024-12-18T01:09:59.9194734Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2024-12-18T01:09:59.9197132Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2024-12-18T01:09:59.9197401Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:09:59.9197638Z the default process group will be used. Default is ``None``. 2024-12-18T01:09:59.9197999Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2024-12-18T01:09:59.9198090Z 2024-12-18T01:09:59.9198192Z Returns: 2024-12-18T01:09:59.9198376Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-12-18T01:09:59.9198485Z output of the collective. 2024-12-18T01:09:59.9198582Z 2024-12-18T01:09:59.9198802Z .. note:: Note that this API differs slightly from the gather collective 2024-12-18T01:09:59.9199035Z since it does not provide an async_op handle and thus will be a blocking 2024-12-18T01:09:59.9199123Z call. 2024-12-18T01:09:59.9199219Z 2024-12-18T01:09:59.9199488Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-12-18T01:09:59.9199699Z of objects must be moved to the GPU device before communication takes 2024-12-18T01:09:59.9199860Z place. In this case, the device used is given by 2024-12-18T01:09:59.9200080Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-12-18T01:09:59.9200300Z ensure that this is set so that each rank has an individual GPU, via 2024-12-18T01:09:59.9200413Z ``torch.cuda.set_device()``. 2024-12-18T01:09:59.9200495Z 2024-12-18T01:09:59.9200604Z .. warning:: 2024-12-18T01:09:59.9200803Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-12-18T01:09:59.9201033Z known to be insecure. It is possible to construct malicious pickle data 2024-12-18T01:09:59.9201249Z which will execute arbitrary code during unpickling. Only call this 2024-12-18T01:09:59.9201373Z function with data you trust. 2024-12-18T01:09:59.9201457Z 2024-12-18T01:09:59.9201549Z .. warning:: 2024-12-18T01:09:59.9201775Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-12-18T01:09:59.9202005Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-12-18T01:09:59.9202189Z pickled. Please consider using :func:`gather` instead. 2024-12-18T01:09:59.9202273Z 2024-12-18T01:09:59.9202370Z Example:: 2024-12-18T01:09:59.9202523Z >>> # xdoctest: +SKIP("need process group init") 2024-12-18T01:09:59.9202705Z >>> # Note: Process group initialization omitted on each rank. 2024-12-18T01:09:59.9202839Z >>> import torch.distributed as dist 2024-12-18T01:09:59.9202948Z >>> # Assumes world_size of 3. 2024-12-18T01:09:59.9203140Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-12-18T01:09:59.9203267Z >>> output = [None for _ in gather_objects] 2024-12-18T01:09:59.9203367Z >>> dist.gather_object( 2024-12-18T01:09:59.9203505Z ... gather_objects[dist.get_rank()], 2024-12-18T01:09:59.9203639Z ... output if dist.get_rank() == 0 else None, 2024-12-18T01:09:59.9203740Z ... dst=0 2024-12-18T01:09:59.9203831Z ... ) 2024-12-18T01:09:59.9203919Z >>> # On rank 0 2024-12-18T01:09:59.9204023Z >>> output 2024-12-18T01:09:59.9204117Z ['foo', 12, {1: 2}] 2024-12-18T01:09:59.9204273Z 2024-12-18T01:09:59.9204530Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9204626Z 2024-12-18T01:09:59.9204723Z warnings.warn(msg) 2024-12-18T01:09:59.9204817Z 2024-12-18T01:09:59.9205044Z --- Parse Warning: 37 / 105 --- 2024-12-18T01:09:59.9205895Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-12-18T01:09:59.9206232Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9206316Z 2024-12-18T01:09:59.9206448Z Module ``torch.distributed.launch``. 2024-12-18T01:09:59.9206532Z 2024-12-18T01:09:59.9206818Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-12-18T01:09:59.9206972Z training processes on each of the training nodes. 2024-12-18T01:09:59.9207057Z 2024-12-18T01:09:59.9207164Z .. warning:: 2024-12-18T01:09:59.9207249Z 2024-12-18T01:09:59.9207514Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-12-18T01:09:59.9207598Z 2024-12-18T01:09:59.9207850Z The utility can be used for single-node distributed training, in which one or 2024-12-18T01:09:59.9208084Z more processes per node will be spawned. The utility can be used for either 2024-12-18T01:09:59.9208301Z CPU training or GPU training. If the utility is used for GPU training, 2024-12-18T01:09:59.9208561Z each distributed process will be operating on a single GPU. This can achieve 2024-12-18T01:09:59.9208793Z well-improved single-node training performance. It can also be used in 2024-12-18T01:09:59.9209075Z multi-node distributed training, by spawning up multiple processes on each node 2024-12-18T01:09:59.9209304Z for well-improved multi-node distributed training performance as well. 2024-12-18T01:09:59.9209551Z This will especially be beneficial for systems with multiple Infiniband 2024-12-18T01:09:59.9209805Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-12-18T01:09:59.9209924Z aggregated communication bandwidth. 2024-12-18T01:09:59.9210019Z 2024-12-18T01:09:59.9210249Z In both cases of single-node distributed training or multi-node distributed 2024-12-18T01:09:59.9210496Z training, this utility will launch the given number of processes per node 2024-12-18T01:09:59.9210722Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-12-18T01:09:59.9210958Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-12-18T01:09:59.9211161Z and each process will be operating on a single GPU from *GPU 0 to 2024-12-18T01:09:59.9211269Z GPU (nproc_per_node - 1)*. 2024-12-18T01:09:59.9211365Z 2024-12-18T01:09:59.9211467Z **How to use this module:** 2024-12-18T01:09:59.9211568Z 2024-12-18T01:09:59.9211720Z 1. Single-Node multi-process distributed training 2024-12-18T01:09:59.9211802Z 2024-12-18T01:09:59.9211907Z :: 2024-12-18T01:09:59.9211989Z 2024-12-18T01:09:59.9212235Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:09:59.9212426Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-12-18T01:09:59.9212564Z arguments of your training script) 2024-12-18T01:09:59.9212645Z 2024-12-18T01:09:59.9212857Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-12-18T01:09:59.9212953Z 2024-12-18T01:09:59.9213034Z 2024-12-18T01:09:59.9213194Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-12-18T01:09:59.9213275Z 2024-12-18T01:09:59.9213361Z :: 2024-12-18T01:09:59.9213454Z 2024-12-18T01:09:59.9213689Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:09:59.9213933Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-12-18T01:09:59.9214144Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:09:59.9214311Z and all other arguments of your training script) 2024-12-18T01:09:59.9214393Z 2024-12-18T01:09:59.9214481Z Node 2: 2024-12-18T01:09:59.9214576Z 2024-12-18T01:09:59.9214662Z :: 2024-12-18T01:09:59.9214759Z 2024-12-18T01:09:59.9214993Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:09:59.9215199Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-12-18T01:09:59.9215421Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:09:59.9215600Z and all other arguments of your training script) 2024-12-18T01:09:59.9215697Z 2024-12-18T01:09:59.9215862Z 3. To look up what optional arguments this module offers: 2024-12-18T01:09:59.9215961Z 2024-12-18T01:09:59.9216045Z :: 2024-12-18T01:09:59.9216129Z 2024-12-18T01:09:59.9216281Z python -m torch.distributed.launch --help 2024-12-18T01:09:59.9216364Z 2024-12-18T01:09:59.9216460Z 2024-12-18T01:09:59.9216562Z **Important Notices:** 2024-12-18T01:09:59.9216644Z 2024-12-18T01:09:59.9216847Z 1. This utility and multi-process distributed (single-node or 2024-12-18T01:09:59.9217094Z multi-node) GPU training currently only achieves the best performance using 2024-12-18T01:09:59.9217357Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-12-18T01:09:59.9217459Z use for GPU training. 2024-12-18T01:09:59.9217542Z 2024-12-18T01:09:59.9217769Z 2. In your training program, you must parse the command-line argument: 2024-12-18T01:09:59.9218002Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-12-18T01:09:59.9218242Z If your training program uses GPUs, you should ensure that your code only 2024-12-18T01:09:59.9218436Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-12-18T01:09:59.9218529Z 2024-12-18T01:09:59.9218638Z Parsing the local_rank argument 2024-12-18T01:09:59.9218719Z 2024-12-18T01:09:59.9218812Z :: 2024-12-18T01:09:59.9218892Z 2024-12-18T01:09:59.9219002Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9219096Z >>> import argparse 2024-12-18T01:09:59.9219225Z >>> parser = argparse.ArgumentParser() 2024-12-18T01:09:59.9219430Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-12-18T01:09:59.9219544Z >>> args = parser.parse_args() 2024-12-18T01:09:59.9219639Z 2024-12-18T01:09:59.9219769Z Set your device to local rank using either 2024-12-18T01:09:59.9219869Z 2024-12-18T01:09:59.9219954Z :: 2024-12-18T01:09:59.9220038Z 2024-12-18T01:09:59.9220250Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-12-18T01:09:59.9220337Z 2024-12-18T01:09:59.9220433Z or 2024-12-18T01:09:59.9220516Z 2024-12-18T01:09:59.9220600Z :: 2024-12-18T01:09:59.9220694Z 2024-12-18T01:09:59.9220830Z >>> with torch.cuda.device(args.local_rank): 2024-12-18T01:09:59.9220945Z >>> # your code to run 2024-12-18T01:09:59.9221036Z >>> ... 2024-12-18T01:09:59.9221118Z 2024-12-18T01:09:59.9221232Z .. versionchanged:: 2.0.0 2024-12-18T01:09:59.9221315Z 2024-12-18T01:09:59.9221572Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-12-18T01:09:59.9221812Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-12-18T01:09:59.9221957Z previously used underscored ``--local_rank``. 2024-12-18T01:09:59.9222052Z 2024-12-18T01:09:59.9222291Z For backward compatibility, it may be necessary for users to handle both 2024-12-18T01:09:59.9222569Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-12-18T01:09:59.9222849Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-12-18T01:09:59.9223113Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-12-18T01:09:59.9223342Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-12-18T01:09:59.9223494Z including ``"--local-rank"`` should be sufficient. 2024-12-18T01:09:59.9223591Z 2024-12-18T01:09:59.9223827Z 3. In your training program, you are supposed to call the following function 2024-12-18T01:09:59.9224114Z at the beginning to start the distributed backend. It is strongly recommended 2024-12-18T01:09:59.9224340Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-12-18T01:09:59.9224573Z but ``env://`` is the one that is officially supported by this module. 2024-12-18T01:09:59.9224656Z 2024-12-18T01:09:59.9224742Z :: 2024-12-18T01:09:59.9224840Z 2024-12-18T01:09:59.9225045Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-12-18T01:09:59.9225194Z >>> init_method='env://') 2024-12-18T01:09:59.9225276Z 2024-12-18T01:09:59.9225525Z 4. In your training program, you can either use regular distributed functions 2024-12-18T01:09:59.9225761Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-12-18T01:09:59.9225971Z training program uses GPUs for training and you would like to use 2024-12-18T01:09:59.9226175Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-12-18T01:09:59.9226284Z here is how to configure it. 2024-12-18T01:09:59.9226378Z 2024-12-18T01:09:59.9226462Z :: 2024-12-18T01:09:59.9226543Z 2024-12-18T01:09:59.9226747Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-12-18T01:09:59.9226890Z >>> device_ids=[args.local_rank], 2024-12-18T01:09:59.9227046Z >>> output_device=args.local_rank) 2024-12-18T01:09:59.9227129Z 2024-12-18T01:09:59.9227384Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-12-18T01:09:59.9227616Z that your code will be operating on. This is generally the local rank of the 2024-12-18T01:09:59.9227855Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-12-18T01:09:59.9228086Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-12-18T01:09:59.9228172Z utility 2024-12-18T01:09:59.9228269Z 2024-12-18T01:09:59.9228604Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-12-18T01:09:59.9228837Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-12-18T01:09:59.9229057Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-12-18T01:09:59.9229257Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-12-18T01:09:59.9229456Z will not pass ``--local-rank`` when you specify this flag. 2024-12-18T01:09:59.9229540Z 2024-12-18T01:09:59.9229634Z .. warning:: 2024-12-18T01:09:59.9229729Z 2024-12-18T01:09:59.9229934Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-12-18T01:09:59.9230136Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-12-18T01:09:59.9230255Z write to a networked filesystem. See 2024-12-18T01:09:59.9230726Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-12-18T01:09:59.9230907Z how things can go wrong if you don't do this correctly. 2024-12-18T01:09:59.9230990Z 2024-12-18T01:09:59.9231092Z 2024-12-18T01:09:59.9231172Z 2024-12-18T01:09:59.9231270Z 2024-12-18T01:09:59.9231520Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9231690Z 2024-12-18T01:09:59.9231803Z warnings.warn(msg) 2024-12-18T01:09:59.9231889Z 2024-12-18T01:09:59.9232127Z --- Parse Warning: 38 / 105 --- 2024-12-18T01:09:59.9233158Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-12-18T01:09:59.9233435Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9233518Z 2024-12-18T01:09:59.9233793Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-12-18T01:09:59.9233989Z Needs to be called on all ranks in an SPMD fashion. 2024-12-18T01:09:59.9234075Z 2024-12-18T01:09:59.9234176Z Args: 2024-12-18T01:09:59.9234447Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-12-18T01:09:59.9234627Z of shards that represent the local shards on this rank. 2024-12-18T01:09:59.9234868Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-12-18T01:09:59.9234988Z shape of the overall sharded tensor. 2024-12-18T01:09:59.9235087Z 2024-12-18T01:09:59.9235177Z Keyword args: 2024-12-18T01:09:59.9235451Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:09:59.9235580Z the default process group will be used. 2024-12-18T01:09:59.9235758Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:09:59.9235981Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:09:59.9236368Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:09:59.9236486Z Default: ``False``. 2024-12-18T01:09:59.9236571Z 2024-12-18T01:09:59.9236662Z Returns: 2024-12-18T01:09:59.9236831Z A :class:`ShardedTensor` object handle on this rank 2024-12-18T01:09:59.9236912Z 2024-12-18T01:09:59.9237009Z 2024-12-18T01:09:59.9237098Z Examples: 2024-12-18T01:09:59.9237365Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-12-18T01:09:59.9237546Z each shard have a (5, 5) local tensor, we can do it like below: 2024-12-18T01:09:59.9237630Z 2024-12-18T01:09:59.9237733Z on rank 0: 2024-12-18T01:09:59.9237852Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:09:59.9237988Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:09:59.9238093Z >>> shard_offsets=[0, 0], 2024-12-18T01:09:59.9238195Z >>> shard_lengths=[5, 5], 2024-12-18T01:09:59.9238316Z >>> placement="rank:0/cuda:0" 2024-12-18T01:09:59.9238402Z >>> ) 2024-12-18T01:09:59.9238609Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:09:59.9238807Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:09:59.9238900Z 2024-12-18T01:09:59.9238984Z on rank 1: 2024-12-18T01:09:59.9239103Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:09:59.9239237Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:09:59.9239343Z >>> shard_offsets=[5, 0], 2024-12-18T01:09:59.9239458Z >>> shard_lengths=[5, 5], 2024-12-18T01:09:59.9239563Z >>> placement="rank:1/cuda:1" 2024-12-18T01:09:59.9239650Z >>> ) 2024-12-18T01:09:59.9239856Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:09:59.9240048Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:09:59.9240142Z 2024-12-18T01:09:59.9240393Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9240485Z 2024-12-18T01:09:59.9240580Z warnings.warn(msg) 2024-12-18T01:09:59.9240780Z 2024-12-18T01:09:59.9241005Z --- Parse Warning: 39 / 105 --- 2024-12-18T01:09:59.9242077Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-12-18T01:09:59.9242350Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9242432Z 2024-12-18T01:09:59.9242746Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-12-18T01:09:59.9242863Z size and sharding spec on each rank. 2024-12-18T01:09:59.9242949Z 2024-12-18T01:09:59.9243048Z Args: 2024-12-18T01:09:59.9243323Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-12-18T01:09:59.9243596Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-12-18T01:09:59.9243769Z The specification describing how to shard the Tensor. 2024-12-18T01:09:59.9243938Z global_size (Sequence[int]): Size of the sharded tensor. 2024-12-18T01:09:59.9244202Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-12-18T01:09:59.9244296Z Default: None 2024-12-18T01:09:59.9244482Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:09:59.9244691Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:09:59.9244903Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:09:59.9245001Z Default: ``False``. 2024-12-18T01:09:59.9245084Z 2024-12-18T01:09:59.9245185Z Returns: 2024-12-18T01:09:59.9245430Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-12-18T01:09:59.9245562Z tensor stored in the current rank. 2024-12-18T01:09:59.9245648Z 2024-12-18T01:09:59.9245753Z Examples: 2024-12-18T01:09:59.9245851Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9245988Z >>> # All tensors below are of torch.int64 type. 2024-12-18T01:09:59.9246119Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:09:59.9246294Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:09:59.9246506Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-12-18T01:09:59.9246598Z >>> local_tensor 2024-12-18T01:09:59.9246698Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-12-18T01:09:59.9246811Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-12-18T01:09:59.9246907Z >>> sharding_dim = 0 2024-12-18T01:09:59.9247043Z >>> sharding_spec = ChunkShardingSpec( 2024-12-18T01:09:59.9247143Z dim=sharding_dim, 2024-12-18T01:09:59.9247250Z placements=[ 2024-12-18T01:09:59.9247347Z "rank:0/cuda:0", 2024-12-18T01:09:59.9247442Z "rank:1/cuda:1", 2024-12-18T01:09:59.9247540Z ], 2024-12-18T01:09:59.9247625Z ) 2024-12-18T01:09:59.9247894Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-12-18T01:09:59.9247979Z >>> st 2024-12-18T01:09:59.9248072Z ShardedTensor( 2024-12-18T01:09:59.9248192Z ShardedTensorMetadata( 2024-12-18T01:09:59.9248288Z shards_metadata=[ 2024-12-18T01:09:59.9248566Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-12-18T01:09:59.9248828Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-12-18T01:09:59.9248928Z ], 2024-12-18T01:09:59.9249036Z size=torch.Size([2, 4]) 2024-12-18T01:09:59.9249122Z ) 2024-12-18T01:09:59.9249232Z >>> st.local_tensor() 2024-12-18T01:09:59.9249329Z tensor([1, 2, 3, 4]) # Rank 0 2024-12-18T01:09:59.9249508Z tensor([3, 4, 5, 6]) # Rank 1 2024-12-18T01:09:59.9249593Z 2024-12-18T01:09:59.9249858Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-12-18T01:09:59.9250110Z rank validations, and we only validate the local shard on the current rank. 2024-12-18T01:09:59.9250322Z We fully rely on the user to ensure local tensor is sharded based on the 2024-12-18T01:09:59.9250431Z sharding spec. 2024-12-18T01:09:59.9250514Z 2024-12-18T01:09:59.9250779Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9250894Z 2024-12-18T01:09:59.9250995Z warnings.warn(msg) 2024-12-18T01:09:59.9251088Z 2024-12-18T01:09:59.9251283Z --- Parse Warning: 40 / 105 --- 2024-12-18T01:09:59.9252354Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-12-18T01:09:59.9252621Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9252715Z 2024-12-18T01:09:59.9252967Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-12-18T01:09:59.9253062Z single local shard. 2024-12-18T01:09:59.9253157Z 2024-12-18T01:09:59.9253378Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-12-18T01:09:59.9253630Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-12-18T01:09:59.9253739Z we swap local shards directly. 2024-12-18T01:09:59.9254010Z For more generic cases, we merge different shards across different ranks and split 2024-12-18T01:09:59.9254261Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-12-18T01:09:59.9254344Z 2024-12-18T01:09:59.9254442Z Args: 2024-12-18T01:09:59.9254730Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-12-18T01:09:59.9254905Z specification describing how the tensor is sharded. 2024-12-18T01:09:59.9254988Z 2024-12-18T01:09:59.9255072Z Returns: 2024-12-18T01:09:59.9255287Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-12-18T01:09:59.9255370Z 2024-12-18T01:09:59.9255470Z Examples: 2024-12-18T01:09:59.9255569Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9255690Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:09:59.9255881Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:09:59.9256003Z >>> tensor = torch.stack([tensor, tensor]) 2024-12-18T01:09:59.9256102Z >>> tensor 2024-12-18T01:09:59.9256226Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-12-18T01:09:59.9256357Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-12-18T01:09:59.9256478Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-12-18T01:09:59.9256601Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-12-18T01:09:59.9256711Z >>> sharding_dim = 0 2024-12-18T01:09:59.9256823Z >>> spec = ChunkShardingSpec( 2024-12-18T01:09:59.9256932Z dim=sharding_dim, 2024-12-18T01:09:59.9257028Z placements=[ 2024-12-18T01:09:59.9257135Z "rank:0/cuda:0", 2024-12-18T01:09:59.9257230Z "rank:1/cuda:1", 2024-12-18T01:09:59.9257322Z "rank:2/cuda:2", 2024-12-18T01:09:59.9257427Z "rank:3/cuda:3", 2024-12-18T01:09:59.9257511Z ], 2024-12-18T01:09:59.9257605Z ) 2024-12-18T01:09:59.9257710Z >>> current_offsets = [0] * 2 2024-12-18T01:09:59.9257819Z >>> current_offsets[0] = rank * 2 2024-12-18T01:09:59.9257943Z >>> shard_metadata = ShardMetadata( 2024-12-18T01:09:59.9258095Z shard_offsets=copy.deepcopy(current_offsets), 2024-12-18T01:09:59.9258271Z shard_sizes=tensor.size(), 2024-12-18T01:09:59.9258395Z placement=spec.placements[rank], 2024-12-18T01:09:59.9258478Z ) 2024-12-18T01:09:59.9258587Z >>> local_shards = [ 2024-12-18T01:09:59.9258676Z Shard( 2024-12-18T01:09:59.9258784Z tensor=tensor, 2024-12-18T01:09:59.9258894Z metadata=shard_metadata, 2024-12-18T01:09:59.9258990Z ) 2024-12-18T01:09:59.9259080Z ] 2024-12-18T01:09:59.9259335Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-12-18T01:09:59.9259447Z >>> sharding_dim = 1 2024-12-18T01:09:59.9259575Z >>> resharding_spec = ChunkShardingSpec( 2024-12-18T01:09:59.9259713Z dim=sharding_dim, 2024-12-18T01:09:59.9259811Z placements=[ 2024-12-18T01:09:59.9259904Z "rank:0/cuda:0", 2024-12-18T01:09:59.9260014Z "rank:1/cuda:1", 2024-12-18T01:09:59.9260109Z "rank:2/cuda:2", 2024-12-18T01:09:59.9260217Z "rank:3/cuda:3", 2024-12-18T01:09:59.9260304Z ], 2024-12-18T01:09:59.9260387Z ) 2024-12-18T01:09:59.9260512Z >>> st.reshard(resharding_spec) 2024-12-18T01:09:59.9260626Z >>> tensor = st.local_shards()[0].tensor 2024-12-18T01:09:59.9260726Z >>> tensor 2024-12-18T01:09:59.9260871Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-12-18T01:09:59.9261026Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-12-18T01:09:59.9261168Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-12-18T01:09:59.9261309Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-12-18T01:09:59.9261407Z 2024-12-18T01:09:59.9261655Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9261755Z 2024-12-18T01:09:59.9261857Z warnings.warn(msg) 2024-12-18T01:09:59.9261941Z 2024-12-18T01:09:59.9262152Z --- Parse Warning: 41 / 105 --- 2024-12-18T01:09:59.9263119Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-12-18T01:09:59.9263400Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9263484Z 2024-12-18T01:09:59.9263721Z Representation of a sharding plan, describes how to shard a module 2024-12-18T01:09:59.9263993Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-12-18T01:09:59.9264289Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-12-18T01:09:59.9264535Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-12-18T01:09:59.9264619Z 2024-12-18T01:09:59.9264722Z Args: 2024-12-18T01:09:59.9264990Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-12-18T01:09:59.9265178Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-12-18T01:09:59.9265448Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-12-18T01:09:59.9265717Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-12-18T01:09:59.9265840Z a parameter to a `ShardingSpec`. 2024-12-18T01:09:59.9266094Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-12-18T01:09:59.9266212Z to a `Sharder` object. 2024-12-18T01:09:59.9266541Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-12-18T01:09:59.9266807Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-12-18T01:09:59.9267101Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-12-18T01:09:59.9267207Z Default: `None` 2024-12-18T01:09:59.9267459Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-12-18T01:09:59.9267697Z a module's sharded output to be returned as a Tensor from its local shards to 2024-12-18T01:09:59.9267956Z ensure further processing in a data parallel fashion. ("" in list means the 2024-12-18T01:09:59.9268050Z root module). 2024-12-18T01:09:59.9268182Z Default: None 2024-12-18T01:09:59.9268271Z Example: 2024-12-18T01:09:59.9268688Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-12-18T01:09:59.9268973Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-12-18T01:09:59.9269059Z 2024-12-18T01:09:59.9269244Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-12-18T01:09:59.9269353Z >>> class MyModule(nn.Module): 2024-12-18T01:09:59.9269471Z >>> def __init__(self) -> None: 2024-12-18T01:09:59.9269574Z >>> super().__init__() 2024-12-18T01:09:59.9269690Z >>> self.fc1 = nn.Linear() 2024-12-18T01:09:59.9269793Z >>> self.gelu = nn.GELU() 2024-12-18T01:09:59.9269898Z >>> self.fc2 = nn.Linear() 2024-12-18T01:09:59.9270017Z >>> self.relu = nn.Linear() 2024-12-18T01:09:59.9270103Z >>> 2024-12-18T01:09:59.9270222Z >>> def forward(self, input): 2024-12-18T01:09:59.9270399Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-12-18T01:09:59.9270484Z 2024-12-18T01:09:59.9270578Z 2024-12-18T01:09:59.9270715Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-12-18T01:09:59.9270837Z >>> sharding_plan = ShardingPlan( 2024-12-18T01:09:59.9270930Z >>> plan={ 2024-12-18T01:09:59.9271027Z >>> "fc1.weight": spec1, 2024-12-18T01:09:59.9271141Z >>> "fc2.weight": spec2 2024-12-18T01:09:59.9271226Z >>> }, 2024-12-18T01:09:59.9271335Z >>> output_plan={ 2024-12-18T01:09:59.9271435Z >>> "fc2": output_spec 2024-12-18T01:09:59.9271529Z >>> }, 2024-12-18T01:09:59.9271637Z >>> return_local_tensor=["fc2"] 2024-12-18T01:09:59.9271722Z >>> ) 2024-12-18T01:09:59.9271815Z 2024-12-18T01:09:59.9272070Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9272163Z 2024-12-18T01:09:59.9272260Z warnings.warn(msg) 2024-12-18T01:09:59.9272342Z 2024-12-18T01:09:59.9272558Z --- Parse Warning: 42 / 105 --- 2024-12-18T01:09:59.9273650Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-12-18T01:09:59.9273925Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9274009Z 2024-12-18T01:09:59.9274136Z Run post-localSGD algorithm. 2024-12-18T01:09:59.9274220Z 2024-12-18T01:09:59.9274457Z This DDP communication hook is used for running post-localSGD algorithm, 2024-12-18T01:09:59.9274630Z by combining with a model averaging component (e.g., 2024-12-18T01:09:59.9274958Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-12-18T01:09:59.9275086Z that runs after the optimizer step. 2024-12-18T01:09:59.9275173Z 2024-12-18T01:09:59.9275262Z Args: 2024-12-18T01:09:59.9275498Z state (PostLocalSGDState): State information to run post-localSGD. 2024-12-18T01:09:59.9275774Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-12-18T01:09:59.9276283Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:09:59.9276530Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:09:59.9276697Z only exactly one tensor is stored in this bucket. 2024-12-18T01:09:59.9276781Z 2024-12-18T01:09:59.9276882Z Returns: 2024-12-18T01:09:59.9277201Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:09:59.9277381Z 2024-12-18T01:09:59.9277499Z Example:: 2024-12-18T01:09:59.9277598Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9277892Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-12-18T01:09:59.9278015Z start_localSGD_iter=10) 2024-12-18T01:09:59.9278189Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:59.9278541Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-12-18T01:09:59.9278886Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-12-18T01:09:59.9278981Z 2024-12-18T01:09:59.9279233Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9279324Z 2024-12-18T01:09:59.9279422Z warnings.warn(msg) 2024-12-18T01:09:59.9279503Z 2024-12-18T01:09:59.9279718Z --- Parse Warning: 43 / 105 --- 2024-12-18T01:09:59.9280767Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=343. 2024-12-18T01:09:59.9281039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9281128Z 2024-12-18T01:09:59.9281248Z Implement PowerSGD algorithm. 2024-12-18T01:09:59.9281330Z 2024-12-18T01:09:59.9281556Z This DDP communication hook implements PowerSGD gradient compression 2024-12-18T01:09:59.9281808Z algorithm described in the `paper `_. 2024-12-18T01:09:59.9282049Z Once gradient tensors are aggregated across all workers, this hook applies 2024-12-18T01:09:59.9282163Z compression as follows: 2024-12-18T01:09:59.9282246Z 2024-12-18T01:09:59.9282709Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-12-18T01:09:59.9282795Z 2024-12-18T01:09:59.9283214Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-12-18T01:09:59.9283307Z 2024-12-18T01:09:59.9283710Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-12-18T01:09:59.9283808Z 2024-12-18T01:09:59.9283918Z 2. Handles uncompressed tensors: 2024-12-18T01:09:59.9283998Z 2024-12-18T01:09:59.9284515Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-12-18T01:09:59.9284597Z 2024-12-18T01:09:59.9284942Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-12-18T01:09:59.9285024Z 2024-12-18T01:09:59.9285268Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-12-18T01:09:59.9285349Z 2024-12-18T01:09:59.9285588Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-12-18T01:09:59.9285903Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-12-18T01:09:59.9286041Z 2024-12-18T01:09:59.9286200Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-12-18T01:09:59.9286282Z 2024-12-18T01:09:59.9286400Z 3.3. Allreduces Ps as a batch; 2024-12-18T01:09:59.9286482Z 2024-12-18T01:09:59.9286596Z 3.4. Orthogonalizes each P in Ps; 2024-12-18T01:09:59.9286694Z 2024-12-18T01:09:59.9286892Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-12-18T01:09:59.9286983Z 2024-12-18T01:09:59.9287089Z 3.6. Allreduces Qs as a batch; 2024-12-18T01:09:59.9287177Z 2024-12-18T01:09:59.9287511Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-12-18T01:09:59.9287596Z 2024-12-18T01:09:59.9288180Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-12-18T01:09:59.9288465Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-12-18T01:09:59.9288907Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-12-18T01:09:59.9288989Z 2024-12-18T01:09:59.9289079Z Args: 2024-12-18T01:09:59.9289516Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-12-18T01:09:59.9289870Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-12-18T01:09:59.9289990Z and ``min_compression_rate``. 2024-12-18T01:09:59.9290403Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:09:59.9290659Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:09:59.9290812Z only exactly one tensor is stored in this bucket. 2024-12-18T01:09:59.9290894Z 2024-12-18T01:09:59.9290995Z Returns: 2024-12-18T01:09:59.9291235Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:09:59.9291324Z 2024-12-18T01:09:59.9291420Z Example:: 2024-12-18T01:09:59.9291528Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9291799Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-12-18T01:09:59.9291961Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-12-18T01:09:59.9292140Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-12-18T01:09:59.9292223Z 2024-12-18T01:09:59.9292492Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9292578Z 2024-12-18T01:09:59.9292678Z warnings.warn(msg) 2024-12-18T01:09:59.9292779Z 2024-12-18T01:09:59.9292987Z --- Parse Warning: 44 / 105 --- 2024-12-18T01:09:59.9294088Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=37. 2024-12-18T01:09:59.9294356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9294453Z 2024-12-18T01:09:59.9294645Z Averages parameters periodically after the warm-up stage. 2024-12-18T01:09:59.9294729Z 2024-12-18T01:09:59.9295002Z This can be used for running `post-local SGD `_, 2024-12-18T01:09:59.9295198Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-12-18T01:09:59.9295452Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-12-18T01:09:59.9295538Z 2024-12-18T01:09:59.9295638Z Args: 2024-12-18T01:09:59.9295808Z period (int): The number of steps per model averaging. 2024-12-18T01:09:59.9296076Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-12-18T01:09:59.9296285Z Otherwise, only DDP needs to be used. 2024-12-18T01:09:59.9296497Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-12-18T01:09:59.9296635Z model averaging is skipped. 2024-12-18T01:09:59.9296825Z process_group: The process group to be used for all-reduce. 2024-12-18T01:09:59.9296983Z If ``None``, the default process group, which 2024-12-18T01:09:59.9297205Z is created by :func:`torch.distributed.init_process_group`, 2024-12-18T01:09:59.9297330Z will be used. (default: ``None``) 2024-12-18T01:09:59.9297427Z 2024-12-18T01:09:59.9297520Z Example:: 2024-12-18T01:09:59.9297645Z 2024-12-18T01:09:59.9297778Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9297875Z >>> import torch 2024-12-18T01:09:59.9298014Z >>> import torch.distributed as dist 2024-12-18T01:09:59.9298330Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-12-18T01:09:59.9298610Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:09:59.9298715Z >>> import torch.nn as nn 2024-12-18T01:09:59.9298816Z >>> 2024-12-18T01:09:59.9298996Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:09:59.9299108Z >>> torch.cuda.set_device(rank) 2024-12-18T01:09:59.9299254Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-12-18T01:09:59.9299413Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:09:59.9299566Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:09:59.9299654Z >>> ) 2024-12-18T01:09:59.9299811Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:09:59.9300108Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:09:59.9300271Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:59.9300367Z >>> 2024-12-18T01:09:59.9300630Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:09:59.9300798Z >>> # After 100 steps, run model averaging every 4 steps. 2024-12-18T01:09:59.9301110Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:09:59.9301372Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:09:59.9301478Z >>> for step in range(0, 200): 2024-12-18T01:09:59.9301587Z >>> optimizer.zero_grad() 2024-12-18T01:09:59.9301714Z >>> loss = loss_fn(output, labels) 2024-12-18T01:09:59.9301814Z >>> loss.backward() 2024-12-18T01:09:59.9301926Z >>> optimizer.step() 2024-12-18T01:09:59.9302124Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-12-18T01:09:59.9302341Z >>> # inter-node communication only occurs every 4 iterations after 2024-12-18T01:09:59.9302466Z >>> # the initial ``warmup_steps`` period. 2024-12-18T01:09:59.9302625Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:09:59.9302722Z 2024-12-18T01:09:59.9302973Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9303067Z 2024-12-18T01:09:59.9303165Z warnings.warn(msg) 2024-12-18T01:09:59.9303248Z 2024-12-18T01:09:59.9303456Z --- Parse Warning: 45 / 105 --- 2024-12-18T01:09:59.9304652Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-12-18T01:09:59.9304981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9305065Z 2024-12-18T01:09:59.9305409Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-12-18T01:09:59.9305492Z 2024-12-18T01:09:59.9305799Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-12-18T01:09:59.9306015Z by using different periods concurrently after the warm-up stage. 2024-12-18T01:09:59.9306446Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-12-18T01:09:59.9306790Z that supports `post-local SGD `_, which essentially only supports 2024-12-18T01:09:59.9307123Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-12-18T01:09:59.9307483Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-12-18T01:09:59.9307786Z Similarly, the process groups within this class do not have such an intra-machine process 2024-12-18T01:09:59.9308067Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-12-18T01:09:59.9308151Z 2024-12-18T01:09:59.9308238Z Args: 2024-12-18T01:09:59.9308596Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-12-18T01:09:59.9308800Z process group size, used for initializing process groups of 2024-12-18T01:09:59.9309042Z different sizes in a hierarchy to average parameters concurrently. 2024-12-18T01:09:59.9309260Z Particularly, at each iteration, there will be at most a single 2024-12-18T01:09:59.9309509Z process group that runs averaging -- the period of such group should 2024-12-18T01:09:59.9309726Z have the largest period which the current step can be divided by. 2024-12-18T01:09:59.9309899Z For example, if the dict has three keys: 2, 4, and 8, 2024-12-18T01:09:59.9310123Z then this means totally three process groups will be created to 2024-12-18T01:09:59.9310334Z average parameters every 2, 4, and 8 iterations, respectively. 2024-12-18T01:09:59.9310541Z At the 4th iteration, only the second process group will run 2024-12-18T01:09:59.9310723Z averaging, because the first process group should be a 2024-12-18T01:09:59.9310961Z subset of the second process group, and no need to execute the first 2024-12-18T01:09:59.9311089Z process group redundantly. 2024-12-18T01:09:59.9311303Z On the other hand, the third process group can only be triggered 2024-12-18T01:09:59.9311531Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-12-18T01:09:59.9311832Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-12-18T01:09:59.9312285Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-12-18T01:09:59.9312462Z If ``None``, the default process group, which is created 2024-12-18T01:09:59.9312685Z by :func:`torch.distributed.init_process_group`, will be used. 2024-12-18T01:09:59.9312808Z (default: ``None``) 2024-12-18T01:09:59.9312905Z 2024-12-18T01:09:59.9313002Z Example:: 2024-12-18T01:09:59.9313124Z >>> # xdoctest: +SKIP('undefined rank') 2024-12-18T01:09:59.9313262Z >>> from collections import OrderedDict 2024-12-18T01:09:59.9313429Z >>> import torch 2024-12-18T01:09:59.9313561Z >>> import torch.distributed as dist 2024-12-18T01:09:59.9313834Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:09:59.9313952Z >>> PostLocalSGDState, 2024-12-18T01:09:59.9314055Z >>> post_localSGD_hook, 2024-12-18T01:09:59.9314142Z >>> ) 2024-12-18T01:09:59.9314527Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-12-18T01:09:59.9314629Z >>> import torch.nn as nn 2024-12-18T01:09:59.9314757Z >>> 2024-12-18T01:09:59.9314939Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:09:59.9315077Z >>> torch.cuda.set_device(rank) 2024-12-18T01:09:59.9315234Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:09:59.9315388Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:09:59.9315546Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:09:59.9315632Z >>> ) 2024-12-18T01:09:59.9315793Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:09:59.9316072Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-12-18T01:09:59.9316194Z >>> subgroup, _ = dist.new_subgroups() 2024-12-18T01:09:59.9316515Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-12-18T01:09:59.9316680Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:59.9316781Z >>> 2024-12-18T01:09:59.9317067Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-12-18T01:09:59.9317205Z >>> # the 16 processes every 16 iterations. 2024-12-18T01:09:59.9317392Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-12-18T01:09:59.9317627Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-12-18T01:09:59.9317954Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:09:59.9318220Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:09:59.9318392Z >>> # After 100 steps, run model averaging at two levels. 2024-12-18T01:09:59.9318497Z >>> for step in range(0, 200): 2024-12-18T01:09:59.9318614Z >>> optimizer.zero_grad() 2024-12-18T01:09:59.9318729Z >>> loss = loss_fn(output, labels) 2024-12-18T01:09:59.9318828Z >>> loss.backward() 2024-12-18T01:09:59.9318940Z >>> optimizer.step() 2024-12-18T01:09:59.9319097Z >>> # Average parameters after ``optimizer.step()``. 2024-12-18T01:09:59.9319390Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-12-18T01:09:59.9319554Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:09:59.9319647Z 2024-12-18T01:09:59.9319738Z .. warning :: 2024-12-18T01:09:59.9319988Z The last group size in the dict must be the size of the provided ``process_group``, 2024-12-18T01:09:59.9320232Z which indicates model averaging at the highest level of the hierarchy. 2024-12-18T01:09:59.9320534Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-12-18T01:09:59.9320629Z 2024-12-18T01:09:59.9320720Z .. warning :: 2024-12-18T01:09:59.9320966Z `HierarchicalModelAverager` is experimental and subject to change. 2024-12-18T01:09:59.9321049Z 2024-12-18T01:09:59.9321306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9321402Z 2024-12-18T01:09:59.9321500Z warnings.warn(msg) 2024-12-18T01:09:59.9321596Z 2024-12-18T01:09:59.9321807Z --- Parse Warning: 46 / 105 --- 2024-12-18T01:09:59.9322926Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-12-18T01:09:59.9323202Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9323288Z 2024-12-18T01:09:59.9323594Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-12-18T01:09:59.9323866Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-12-18T01:09:59.9323965Z 2024-12-18T01:09:59.9324128Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-12-18T01:09:59.9324237Z 2024-12-18T01:09:59.9324342Z .. warning:: 2024-12-18T01:09:59.9324514Z Current implementation only supports loading Tensors. 2024-12-18T01:09:59.9324608Z 2024-12-18T01:09:59.9324727Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9324836Z >>> sd = {"mode": model} 2024-12-18T01:09:59.9324926Z >>> dcp.load( 2024-12-18T01:09:59.9325012Z >>> sd, 2024-12-18T01:09:59.9325177Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:09:59.9325302Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:09:59.9325433Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:09:59.9325518Z >>> ) 2024-12-18T01:09:59.9325599Z 2024-12-18T01:09:59.9325863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9325947Z 2024-12-18T01:09:59.9326058Z warnings.warn(msg) 2024-12-18T01:09:59.9326140Z 2024-12-18T01:09:59.9326333Z --- Parse Warning: 47 / 105 --- 2024-12-18T01:09:59.9327375Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-12-18T01:09:59.9327640Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9327736Z 2024-12-18T01:09:59.9328098Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-12-18T01:09:59.9328436Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-12-18T01:09:59.9328554Z metadata file, like Torch Save files. 2024-12-18T01:09:59.9328652Z 2024-12-18T01:09:59.9328832Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-12-18T01:09:59.9328918Z 2024-12-18T01:09:59.9329026Z .. warning:: 2024-12-18T01:09:59.9329202Z Current implementation only supports loading Tensors. 2024-12-18T01:09:59.9329300Z 2024-12-18T01:09:59.9329416Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9329515Z >>> sd = {"mode": model} 2024-12-18T01:09:59.9329625Z >>> dcp.load( 2024-12-18T01:09:59.9329714Z >>> sd, 2024-12-18T01:09:59.9329881Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:09:59.9330007Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:09:59.9330126Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:09:59.9330224Z >>> ) 2024-12-18T01:09:59.9330311Z 2024-12-18T01:09:59.9330823Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9330909Z 2024-12-18T01:09:59.9331024Z warnings.warn(msg) 2024-12-18T01:09:59.9331110Z 2024-12-18T01:09:59.9331311Z --- Parse Warning: 48 / 105 --- 2024-12-18T01:09:59.9332366Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-12-18T01:09:59.9332702Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9332801Z 2024-12-18T01:09:59.9333012Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-12-18T01:09:59.9333111Z 2024-12-18T01:09:59.9333278Z This is the current recommended way to checkpoint FSDP. 2024-12-18T01:09:59.9333378Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9333549Z >>> import torch.distributed.checkpoint as dist_cp 2024-12-18T01:09:59.9333636Z >>> # Save 2024-12-18T01:09:59.9333751Z >>> model: torch.nn.Model 2024-12-18T01:09:59.9333909Z >>> optim_params = model.parameters() 2024-12-18T01:09:59.9334055Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-12-18T01:09:59.9334155Z >>> # Save 2024-12-18T01:09:59.9334396Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:09:59.9334508Z >>> state_dict = { 2024-12-18T01:09:59.9334668Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-12-18T01:09:59.9334794Z >>> "model": model.state_dict() 2024-12-18T01:09:59.9334878Z >>> } 2024-12-18T01:09:59.9334982Z >>> dist_cp.save_state_dict( 2024-12-18T01:09:59.9335098Z >>> state_dict=optim_state, 2024-12-18T01:09:59.9335276Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-12-18T01:09:59.9335421Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-12-18T01:09:59.9335506Z >>> ) 2024-12-18T01:09:59.9335590Z >>> 2024-12-18T01:09:59.9335688Z >>> # Load 2024-12-18T01:09:59.9335914Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:09:59.9336311Z >>> model_state_dict = model_tp.state_dict() 2024-12-18T01:09:59.9336449Z >>> checkpoint = { 2024-12-18T01:09:59.9336610Z >>> "model": model_state_dict 2024-12-18T01:09:59.9336725Z >>> } 2024-12-18T01:09:59.9336856Z >>> dist_cp.load_state_dict( 2024-12-18T01:09:59.9337019Z >>> state_dict=checkpoint, 2024-12-18T01:09:59.9337286Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-12-18T01:09:59.9337477Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-12-18T01:09:59.9337588Z >>> ) 2024-12-18T01:09:59.9337792Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-12-18T01:09:59.9337919Z >>> 2024-12-18T01:09:59.9338153Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-12-18T01:09:59.9338297Z >>> model_state_dict, 2024-12-18T01:09:59.9338449Z >>> optimizer_key="optimizer", 2024-12-18T01:09:59.9338711Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-12-18T01:09:59.9338824Z >>> ) 2024-12-18T01:09:59.9338940Z >>> 2024-12-18T01:09:59.9339157Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-12-18T01:09:59.9339333Z >>> model, optim, optim_state["optimizer"] 2024-12-18T01:09:59.9339474Z >>> ) 2024-12-18T01:09:59.9339591Z >>> 2024-12-18T01:09:59.9339761Z >>> optim.load_state_dict(flattened_osd) 2024-12-18T01:09:59.9339888Z 2024-12-18T01:09:59.9340240Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9340367Z 2024-12-18T01:09:59.9340499Z warnings.warn(msg) 2024-12-18T01:09:59.9340621Z 2024-12-18T01:09:59.9340939Z --- Parse Warning: 49 / 105 --- 2024-12-18T01:09:59.9341900Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-12-18T01:09:59.9342177Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9342259Z 2024-12-18T01:09:59.9342555Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-12-18T01:09:59.9342784Z 2024-12-18T01:09:59.9343079Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-12-18T01:09:59.9343175Z 2024-12-18T01:09:59.9343455Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:09:59.9343584Z will be visible to the whole process. 2024-12-18T01:09:59.9343667Z 2024-12-18T01:09:59.9343953Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-12-18T01:09:59.9344037Z 2024-12-18T01:09:59.9344161Z 1) set_up_planner - called on all ranks. 2024-12-18T01:09:59.9344352Z Signals the start of a checkpoint save. 2024-12-18T01:09:59.9344437Z 2024-12-18T01:09:59.9344581Z 2) create_local_plan - called on all ranks. 2024-12-18T01:09:59.9344907Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-12-18T01:09:59.9344991Z 2024-12-18T01:09:59.9345191Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:09:59.9345390Z Takes the SavePlan from all ranks and make any global decision. 2024-12-18T01:09:59.9345487Z 2024-12-18T01:09:59.9345599Z 4) finish_plan - called on all ranks. 2024-12-18T01:09:59.9345828Z This gives each rank a chance to adjust to global planning decisions. 2024-12-18T01:09:59.9345912Z 2024-12-18T01:09:59.9346067Z 5) resolve_data - called multiple times on each rank 2024-12-18T01:09:59.9346292Z Lookups a value on the `state_dict` for the storage layer to write. 2024-12-18T01:09:59.9346377Z 2024-12-18T01:09:59.9346695Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-12-18T01:09:59.9346881Z most changes can be expressed by changes in a single method. 2024-12-18T01:09:59.9346978Z 2024-12-18T01:09:59.9347105Z There are 3 usual patterns of extension: 2024-12-18T01:09:59.9347189Z 2024-12-18T01:09:59.9347458Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-12-18T01:09:59.9347685Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-12-18T01:09:59.9347780Z 2024-12-18T01:09:59.9347896Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9348031Z >>> class RenamePlanner(DefaultSavePlanner): 2024-12-18T01:09:59.9348143Z >>> def set_up_planner( 2024-12-18T01:09:59.9348230Z >>> self, 2024-12-18T01:09:59.9348452Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:09:59.9348585Z >>> storage_meta: Optional[StorageMeta], 2024-12-18T01:09:59.9348708Z >>> is_coordinator: bool, 2024-12-18T01:09:59.9348800Z >>> ) -> None: 2024-12-18T01:09:59.9348916Z >>> # prefix all keys with `foo_`` 2024-12-18T01:09:59.9349226Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-12-18T01:09:59.9349311Z 2024-12-18T01:09:59.9349660Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-12-18T01:09:59.9349745Z 2024-12-18T01:09:59.9349862Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9350005Z >>> class FP16Planner(DefaultSavePlanner): 2024-12-18T01:09:59.9350116Z >>> def create_local_plan(self): 2024-12-18T01:09:59.9350251Z >>> plan = super().create_local_plan() 2024-12-18T01:09:59.9350351Z >>> for p in plan: 2024-12-18T01:09:59.9350482Z >>> if p.tensor_data is not None: 2024-12-18T01:09:59.9350645Z >>> p.tensor_data.properties.dtype = torch.float16 2024-12-18T01:09:59.9350739Z >>> return plan 2024-12-18T01:09:59.9350835Z >>> 2024-12-18T01:09:59.9350954Z >>> def resolve_data(self, write_item): 2024-12-18T01:09:59.9351092Z >>> item = super().resolve_data(write_item) 2024-12-18T01:09:59.9351363Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-12-18T01:09:59.9351524Z 2024-12-18T01:09:59.9351872Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-12-18T01:09:59.9351955Z 2024-12-18T01:09:59.9352081Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9352195Z >>> from itertools import zip_longest 2024-12-18T01:09:59.9352319Z >>> from dataclasses import replace 2024-12-18T01:09:59.9352488Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-12-18T01:09:59.9352803Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-12-18T01:09:59.9352953Z >>> # This sample doesn't handle ShardedTensors 2024-12-18T01:09:59.9353108Z >>> def create_global_plan(self, all_plans): 2024-12-18T01:09:59.9353274Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2024-12-18T01:09:59.9353373Z >>> items_per_rank = [ 2024-12-18T01:09:59.9353528Z >>> [item for item in items if item is not None] 2024-12-18T01:09:59.9353688Z >>> for items in zip(*zip_longest(*iters), strict=True) 2024-12-18T01:09:59.9353774Z >>> ] 2024-12-18T01:09:59.9353883Z >>> all_plans = [ 2024-12-18T01:09:59.9354000Z >>> replace(plan, items=items) 2024-12-18T01:09:59.9354209Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2024-12-18T01:09:59.9354295Z >>> ] 2024-12-18T01:09:59.9354439Z >>> return super().create_global_plan(all_plans) 2024-12-18T01:09:59.9354538Z 2024-12-18T01:09:59.9354805Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-12-18T01:09:59.9355086Z accomplished by having each rank contribute their data items in the local plan and 2024-12-18T01:09:59.9355202Z the global planner aggregate them: 2024-12-18T01:09:59.9355297Z 2024-12-18T01:09:59.9355414Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9355572Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-12-18T01:09:59.9355715Z >>> def create_local_plan(self) -> SavePlan: 2024-12-18T01:09:59.9355837Z >>> plan = super().create_local_plan() 2024-12-18T01:09:59.9356016Z >>> return replace(plan, planner_data="per-rank-data") 2024-12-18T01:09:59.9356099Z >>> 2024-12-18T01:09:59.9356408Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-12-18T01:09:59.9356604Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-12-18T01:09:59.9356766Z >>> merged_data = [p.planner_data for p in global_plan] 2024-12-18T01:09:59.9356952Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-12-18T01:09:59.9357066Z >>> return global_plan, metadata 2024-12-18T01:09:59.9357159Z 2024-12-18T01:09:59.9357410Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9357496Z 2024-12-18T01:09:59.9357612Z warnings.warn(msg) 2024-12-18T01:09:59.9357694Z 2024-12-18T01:09:59.9357927Z --- Parse Warning: 50 / 105 --- 2024-12-18T01:09:59.9358879Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=272. 2024-12-18T01:09:59.9359156Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9359244Z 2024-12-18T01:09:59.9359530Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-12-18T01:09:59.9359633Z 2024-12-18T01:09:59.9359921Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-12-18T01:09:59.9360023Z 2024-12-18T01:09:59.9360300Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:09:59.9360488Z will be visible to the whole process. 2024-12-18T01:09:59.9360574Z 2024-12-18T01:09:59.9360850Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-12-18T01:09:59.9360950Z 2024-12-18T01:09:59.9361073Z 1) set_up_planner - called on all ranks. 2024-12-18T01:09:59.9361219Z Signals the start of loading a checkpoint. 2024-12-18T01:09:59.9361304Z 2024-12-18T01:09:59.9361432Z 2) create_local_plan - called on all ranks. 2024-12-18T01:09:59.9361757Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-12-18T01:09:59.9361842Z 2024-12-18T01:09:59.9362068Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:09:59.9362267Z Takes the LoadPlan from all ranks and make any global decision. 2024-12-18T01:09:59.9362367Z 2024-12-18T01:09:59.9362518Z 4) load_bytes - called multiple times on each rank 2024-12-18T01:09:59.9362694Z This is called once per non-tensor value in state_dict. 2024-12-18T01:09:59.9362793Z 2024-12-18T01:09:59.9363019Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-12-18T01:09:59.9363213Z They are called in pair for each Tensor value in state_dict. 2024-12-18T01:09:59.9363297Z 2024-12-18T01:09:59.9363612Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-12-18T01:09:59.9363798Z most changes can be expressed by changes in a single method. 2024-12-18T01:09:59.9363883Z 2024-12-18T01:09:59.9364033Z There are two usual patterns of extension: 2024-12-18T01:09:59.9364118Z 2024-12-18T01:09:59.9364384Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-12-18T01:09:59.9364636Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-12-18T01:09:59.9364869Z to keep a reference to the original state_dict as load happens in place so 2024-12-18T01:09:59.9365009Z we need to be able to perform it in place 2024-12-18T01:09:59.9365092Z 2024-12-18T01:09:59.9365222Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9365357Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-12-18T01:09:59.9365473Z >>> def set_up_planner( 2024-12-18T01:09:59.9365560Z >>> self, 2024-12-18T01:09:59.9365672Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:09:59.9365785Z >>> metadata: Metadata, 2024-12-18T01:09:59.9365890Z >>> is_coordinator: bool, 2024-12-18T01:09:59.9365994Z >>> ) -> None: 2024-12-18T01:09:59.9366121Z >>> self.original_state_dict = state_dict 2024-12-18T01:09:59.9366297Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-12-18T01:09:59.9366393Z >>> 2024-12-18T01:09:59.9366511Z >>> if self.flatten_sharded_tensors: 2024-12-18T01:09:59.9366681Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-12-18T01:09:59.9366763Z >>> 2024-12-18T01:09:59.9366886Z >>> if self.flatten_state_dict: 2024-12-18T01:09:59.9367070Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-12-18T01:09:59.9367154Z >>> 2024-12-18T01:09:59.9367277Z >>> self.state_dict = state_dict 2024-12-18T01:09:59.9367387Z >>> self.metadata = metadata 2024-12-18T01:09:59.9367524Z >>> self.is_coordinator = is_coordinator 2024-12-18T01:09:59.9367607Z >>> 2024-12-18T01:09:59.9367731Z >>> def load_bytes(self, read_item, value): 2024-12-18T01:09:59.9367851Z >>> # Remove the "foo_" prefix 2024-12-18T01:09:59.9368167Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-12-18T01:09:59.9368264Z 2024-12-18T01:09:59.9368347Z 2024-12-18T01:09:59.9368621Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-12-18T01:09:59.9368760Z 2024-12-18T01:09:59.9368877Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9369049Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-12-18T01:09:59.9369168Z >>> def resolve_tensor(self, read_item): 2024-12-18T01:09:59.9369312Z >>> tensor = super().resolve_tensor(read_item) 2024-12-18T01:09:59.9369458Z >>> return torch.empty_like(tensor, device="cpu") 2024-12-18T01:09:59.9369543Z >>> 2024-12-18T01:09:59.9369690Z >>> def commit_tensor(self, read_item, tensor): 2024-12-18T01:09:59.9369874Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-12-18T01:09:59.9369972Z 2024-12-18T01:09:59.9370254Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9370348Z 2024-12-18T01:09:59.9370446Z warnings.warn(msg) 2024-12-18T01:09:59.9370530Z 2024-12-18T01:09:59.9370743Z --- Parse Warning: 51 / 105 --- 2024-12-18T01:09:59.9371700Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-12-18T01:09:59.9371972Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9372055Z 2024-12-18T01:09:59.9372213Z Load a distributed ``state_dict`` in SPMD style. 2024-12-18T01:09:59.9372295Z 2024-12-18T01:09:59.9372480Z Each rank will try to read the least amount of data necessary 2024-12-18T01:09:59.9372735Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-12-18T01:09:59.9372986Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-12-18T01:09:59.9373084Z 2024-12-18T01:09:59.9373342Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:09:59.9373615Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-12-18T01:09:59.9373786Z ``load_state_dict`` once the deserialization is complete. 2024-12-18T01:09:59.9374043Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2024-12-18T01:09:59.9374208Z it in the ``state_dict`` with the deserialized object. 2024-12-18T01:09:59.9374291Z 2024-12-18T01:09:59.9374404Z .. warning:: 2024-12-18T01:09:59.9374573Z All tensors in ``state_dict`` must be allocated on their 2024-12-18T01:09:59.9374739Z destination device *prior to* calling this function. 2024-12-18T01:09:59.9374834Z 2024-12-18T01:09:59.9375062Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-12-18T01:09:59.9375169Z on state_dict. 2024-12-18T01:09:59.9375251Z 2024-12-18T01:09:59.9375356Z .. warning:: 2024-12-18T01:09:59.9375560Z Users must call `load_state_dict` on the root module to ensure load 2024-12-18T01:09:59.9375745Z pos-processing and non-tensor data properly propagates. 2024-12-18T01:09:59.9375845Z 2024-12-18T01:09:59.9375931Z .. note: 2024-12-18T01:09:59.9376166Z If no process group is initialized, this function will assume the intent 2024-12-18T01:09:59.9376388Z is to load a checkpoint into the local process. This can be useful in the 2024-12-18T01:09:59.9376633Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-12-18T01:09:59.9376743Z or ShardedTensor) 2024-12-18T01:09:59.9376826Z 2024-12-18T01:09:59.9376925Z .. note: 2024-12-18T01:09:59.9377064Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:09:59.9377161Z 2024-12-18T01:09:59.9377247Z Args: 2024-12-18T01:09:59.9377402Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:09:59.9377560Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:09:59.9377826Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:09:59.9378042Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:09:59.9378209Z It can also be a key if the storage is a key-value store. 2024-12-18T01:09:59.9378307Z (Default: ``None``) 2024-12-18T01:09:59.9378452Z storage_reader (Optional[StorageReader]): 2024-12-18T01:09:59.9378657Z Instance of StorageWriter used to perform reads. If this is not 2024-12-18T01:09:59.9378871Z specified, DCP will automatically infer the reader based on the 2024-12-18T01:09:59.9379099Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:09:59.9379223Z be raised. (Default: ``None``) 2024-12-18T01:09:59.9379388Z planner (Optional[LoadPlanner]): 2024-12-18T01:09:59.9379591Z Instance of LoadPlanner. If this is not specificed, the default 2024-12-18T01:09:59.9379737Z planner will be used. (Default: ``None``) 2024-12-18T01:09:59.9379866Z process_group (Optional[ProcessGroup]): 2024-12-18T01:09:59.9380061Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:09:59.9380159Z (Default: ``None``) 2024-12-18T01:09:59.9380254Z 2024-12-18T01:09:59.9380340Z Returns: 2024-12-18T01:09:59.9380425Z None. 2024-12-18T01:09:59.9380522Z 2024-12-18T01:09:59.9380610Z Examples 2024-12-18T01:09:59.9380725Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9380827Z >>> my_model = MyModule() 2024-12-18T01:09:59.9380967Z >>> optimizer = Adagrad(my_model.parameters()) 2024-12-18T01:09:59.9381109Z >>> model_state_dict = my_model.state_dict() 2024-12-18T01:09:59.9381405Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-12-18T01:09:59.9381497Z 2024-12-18T01:09:59.9381654Z >>> torch.distributed.checkpoint.load_state_dict( 2024-12-18T01:09:59.9381785Z >>> state_dict=model_state_dict, 2024-12-18T01:09:59.9381903Z >>> storage_reader=fs_storage_reader, 2024-12-18T01:09:59.9381988Z >>> ) 2024-12-18T01:09:59.9382082Z 2024-12-18T01:09:59.9382279Z >>> # module.load_state_dict() function might have customized steps 2024-12-18T01:09:59.9382419Z >>> # to flush the state_dict, must call it to 2024-12-18T01:09:59.9382526Z >>> # ensure correct behavior. 2024-12-18T01:09:59.9382658Z >>> my_model.load_state_dict(model_state_dict) 2024-12-18T01:09:59.9382753Z 2024-12-18T01:09:59.9382846Z .. note:: 2024-12-18T01:09:59.9383070Z load_state_dict uses collectives to coordinate reads across ranks. 2024-12-18T01:09:59.9383284Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:09:59.9383524Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:09:59.9383749Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:09:59.9383979Z and it is the user's responsibility to ensure that this is set so that each 2024-12-18T01:09:59.9384175Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:09:59.9384260Z 2024-12-18T01:09:59.9384521Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9384604Z 2024-12-18T01:09:59.9384702Z warnings.warn(msg) 2024-12-18T01:09:59.9384795Z 2024-12-18T01:09:59.9384996Z --- Parse Warning: 52 / 105 --- 2024-12-18T01:09:59.9385958Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-12-18T01:09:59.9386219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9386369Z 2024-12-18T01:09:59.9386492Z Save a distributed model in SPMD style. 2024-12-18T01:09:59.9386576Z 2024-12-18T01:09:59.9386781Z This function is different from ``torch.save()`` as it handles 2024-12-18T01:09:59.9387036Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-12-18T01:09:59.9387131Z 2024-12-18T01:09:59.9387386Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:09:59.9387551Z save will call ``state_dict`` before serialization. 2024-12-18T01:09:59.9387635Z 2024-12-18T01:09:59.9387727Z .. warning:: 2024-12-18T01:09:59.9388004Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-12-18T01:09:59.9388109Z for saved state_dicts. 2024-12-18T01:09:59.9388231Z 2024-12-18T01:09:59.9388413Z .. warning:: 2024-12-18T01:09:59.9388626Z If using the `process_group` argument, make sure that only its ranks 2024-12-18T01:09:59.9388852Z call `save_state_dict` and that all data in state_dict belong to it. 2024-12-18T01:09:59.9388936Z 2024-12-18T01:09:59.9389042Z .. note:: 2024-12-18T01:09:59.9389301Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-12-18T01:09:59.9389573Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-12-18T01:09:59.9389682Z group needs to be passed in. 2024-12-18T01:09:59.9389765Z 2024-12-18T01:09:59.9389868Z .. note:: 2024-12-18T01:09:59.9390136Z If no process group is available, this function assumes the intention is to save the 2024-12-18T01:09:59.9390266Z state_dict in the local process. 2024-12-18T01:09:59.9390351Z 2024-12-18T01:09:59.9390452Z .. note: 2024-12-18T01:09:59.9390594Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:09:59.9390679Z 2024-12-18T01:09:59.9390778Z 2024-12-18T01:09:59.9390865Z Args: 2024-12-18T01:09:59.9391040Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:09:59.9391187Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:09:59.9391399Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:09:59.9391619Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:09:59.9391787Z It can also be a key if the storage is a key-value store. 2024-12-18T01:09:59.9391901Z (Default: ``None``) 2024-12-18T01:09:59.9392033Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:09:59.9392256Z Instance of StorageWriter used to perform writes. If this is not 2024-12-18T01:09:59.9392462Z specified, DCP will automatically infer the writer based on the 2024-12-18T01:09:59.9392666Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:09:59.9392792Z be raised. (Default: ``None``) 2024-12-18T01:09:59.9392914Z planner (Optional[SavePlanner]): 2024-12-18T01:09:59.9393133Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:09:59.9393265Z planner will be used. (Default: ``None``) 2024-12-18T01:09:59.9393410Z process_group (Optional[ProcessGroup]): 2024-12-18T01:09:59.9393591Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:09:59.9393689Z (Default: ``None``) 2024-12-18T01:09:59.9393792Z 2024-12-18T01:09:59.9393882Z Returns: 2024-12-18T01:09:59.9394056Z Metadata: Metadata object for the saved checkpoint. 2024-12-18T01:09:59.9394142Z 2024-12-18T01:09:59.9394230Z Example: 2024-12-18T01:09:59.9394341Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9394441Z >>> my_model = MyModule() 2024-12-18T01:09:59.9394538Z 2024-12-18T01:09:59.9394651Z >>> state_dict = {"model": my_model} 2024-12-18T01:09:59.9394733Z 2024-12-18T01:09:59.9395042Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:09:59.9395233Z >>> torch.distributed.checkpoint.save( 2024-12-18T01:09:59.9395352Z >>> state_dict=state_dict, 2024-12-18T01:09:59.9395472Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:09:59.9395568Z >>> ) 2024-12-18T01:09:59.9395650Z 2024-12-18T01:09:59.9395737Z .. note:: 2024-12-18T01:09:59.9395962Z save_state_dict uses collectives to coordinate writes across ranks. 2024-12-18T01:09:59.9396174Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:09:59.9396442Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:09:59.9396688Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:09:59.9396895Z and it is the user's responsibility to ensure that this is set so that 2024-12-18T01:09:59.9397102Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:09:59.9397189Z 2024-12-18T01:09:59.9397450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9397532Z 2024-12-18T01:09:59.9397645Z warnings.warn(msg) 2024-12-18T01:09:59.9397730Z 2024-12-18T01:09:59.9397936Z --- Parse Warning: 53 / 105 --- 2024-12-18T01:09:59.9398925Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-12-18T01:09:59.9399188Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9399468Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-12-18T01:09:59.9399756Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-12-18T01:09:59.9399859Z 2024-12-18T01:09:59.9399952Z .. warning:: 2024-12-18T01:09:59.9400114Z This feature is experimental and subject to change. 2024-12-18T01:09:59.9400210Z 2024-12-18T01:09:59.9400299Z Args: 2024-12-18T01:09:59.9400468Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:09:59.9400614Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:09:59.9400839Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:09:59.9401043Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:09:59.9401215Z It can also be a key if the storage is a key-value store. 2024-12-18T01:09:59.9401327Z (Default: ``None``) 2024-12-18T01:09:59.9401465Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:09:59.9401686Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-12-18T01:09:59.9401928Z this is not specified, DCP will automatically infer the writer based on the 2024-12-18T01:09:59.9402145Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:09:59.9402259Z be raised. (Default: ``None``) 2024-12-18T01:09:59.9402380Z planner (Optional[SavePlanner]): 2024-12-18T01:09:59.9402595Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:09:59.9402730Z planner will be used. (Default: ``None``) 2024-12-18T01:09:59.9402876Z process_group (Optional[ProcessGroup]): 2024-12-18T01:09:59.9403062Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:09:59.9403173Z (Default: ``None``) 2024-12-18T01:09:59.9403258Z 2024-12-18T01:09:59.9403347Z Returns: 2024-12-18T01:09:59.9403571Z Future: A future holding the resultant Metadata object from `save`. 2024-12-18T01:09:59.9403707Z 2024-12-18T01:09:59.9403809Z Example: 2024-12-18T01:09:59.9403909Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9404012Z >>> my_model = MyModule() 2024-12-18T01:09:59.9404108Z 2024-12-18T01:09:59.9404222Z >>> state_dict = {"model": my_model} 2024-12-18T01:09:59.9404318Z 2024-12-18T01:09:59.9404617Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:09:59.9404841Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-12-18T01:09:59.9404952Z >>> state_dict=state_dict, 2024-12-18T01:09:59.9405104Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:09:59.9405204Z >>> ) 2024-12-18T01:09:59.9405290Z >>> 2024-12-18T01:09:59.9405429Z >>> # ... do some work ... 2024-12-18T01:09:59.9405515Z >>> 2024-12-18T01:09:59.9405629Z >>> checkpoint_future.result() 2024-12-18T01:09:59.9405726Z 2024-12-18T01:09:59.9405813Z 2024-12-18T01:09:59.9406079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9406161Z 2024-12-18T01:09:59.9406258Z warnings.warn(msg) 2024-12-18T01:09:59.9406353Z 2024-12-18T01:09:59.9406549Z --- Parse Warning: 54 / 105 --- 2024-12-18T01:09:59.9407599Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-12-18T01:09:59.9407867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9407964Z 2024-12-18T01:09:59.9408171Z Initialize rendezvous event object and record its operations. 2024-12-18T01:09:59.9408255Z 2024-12-18T01:09:59.9408355Z Args: 2024-12-18T01:09:59.9408487Z run_id (str): The run id of the rendezvous. 2024-12-18T01:09:59.9408653Z message (str): The message describing the event. 2024-12-18T01:09:59.9408909Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-12-18T01:09:59.9409109Z name (str): Event name. (E.g. Current action being performed). 2024-12-18T01:09:59.9409228Z hostname (str): Hostname of the node. 2024-12-18T01:09:59.9409374Z pid (Optional[int]): The process id of the node. 2024-12-18T01:09:59.9409637Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-12-18T01:09:59.9409907Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-12-18T01:09:59.9410080Z rank (Optional[int]): The rank of the node, if known. 2024-12-18T01:09:59.9410170Z Returns: 2024-12-18T01:09:59.9410267Z None 2024-12-18T01:09:59.9410355Z Example: 2024-12-18T01:09:59.9410483Z >>> # See DynamicRendezvousHandler class 2024-12-18T01:09:59.9410590Z >>> def _record( 2024-12-18T01:09:59.9410678Z ... self, 2024-12-18T01:09:59.9410785Z ... message: str, 2024-12-18T01:09:59.9410926Z ... node_state: NodeState = NodeState.RUNNING, 2024-12-18T01:09:59.9411036Z ... rank: Optional[int] = None, 2024-12-18T01:09:59.9411138Z ... ) -> None: 2024-12-18T01:09:59.9411259Z ... construct_and_record_rdzv_event( 2024-12-18T01:09:59.9411438Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-12-18T01:09:59.9411560Z ... run_id=self._settings.run_id, 2024-12-18T01:09:59.9411673Z ... message=message, 2024-12-18T01:09:59.9411784Z ... node_state=node_state, 2024-12-18T01:09:59.9411908Z ... hostname=self._this_node.addr, 2024-12-18T01:09:59.9412034Z ... pid=self._this_node.pid, 2024-12-18T01:09:59.9412160Z ... local_id=self._this_node.local_id, 2024-12-18T01:09:59.9412324Z ... rank=rank, 2024-12-18T01:09:59.9412410Z ... ) 2024-12-18T01:09:59.9412493Z 2024-12-18T01:09:59.9412756Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9412841Z 2024-12-18T01:09:59.9412953Z warnings.warn(msg) 2024-12-18T01:09:59.9413039Z 2024-12-18T01:09:59.9413229Z --- Parse Warning: 55 / 105 --- 2024-12-18T01:09:59.9414192Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-12-18T01:09:59.9414456Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9414581Z 2024-12-18T01:09:59.9414751Z This configures FSDP-native mixed precision training. 2024-12-18T01:09:59.9414847Z 2024-12-18T01:09:59.9414938Z Attributes: 2024-12-18T01:09:59.9415175Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-12-18T01:09:59.9415388Z parameters during forward and backward and thus the dtype for 2024-12-18T01:09:59.9415610Z forward and backward computation. Outside forward and backward, the 2024-12-18T01:09:59.9415815Z *sharded* parameters are kept in full precision (e.g. for the 2024-12-18T01:09:59.9416023Z optimizer step), and for model checkpointing, the parameters are 2024-12-18T01:09:59.9416194Z always saved in full precision. (Default: ``None``) 2024-12-18T01:09:59.9416409Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:09:59.9416625Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-12-18T01:09:59.9416818Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-12-18T01:09:59.9417018Z the ``param_dtype`` value, still running gradient reduction in low 2024-12-18T01:09:59.9417251Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-12-18T01:09:59.9417450Z to force gradient reduction to run in full precision. (Default: 2024-12-18T01:09:59.9417558Z ``None``) 2024-12-18T01:09:59.9417771Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:09:59.9417984Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-12-18T01:09:59.9418184Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-12-18T01:09:59.9418397Z dtype thereafter. For model checkpointing, the buffers are saved 2024-12-18T01:09:59.9418593Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-12-18T01:09:59.9418684Z ``None``) 2024-12-18T01:09:59.9418894Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-12-18T01:09:59.9419110Z gradients to full precision after the backward pass in preparation 2024-12-18T01:09:59.9419337Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-12-18T01:09:59.9419537Z in the dtype used for gradient reduction, which can save memory if 2024-12-18T01:09:59.9419746Z using a custom optimizer that supports running in low precision. 2024-12-18T01:09:59.9419858Z (Default: ``False``) 2024-12-18T01:09:59.9420068Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-12-18T01:09:59.9420283Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-12-18T01:09:59.9420498Z that parameter and input dtypes match for forward computation, as 2024-12-18T01:09:59.9420719Z required by many ops. This may need to be set to ``True`` when only 2024-12-18T01:09:59.9420937Z applying mixed precision to some but not all FSDP modules, in which 2024-12-18T01:09:59.9421146Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-12-18T01:09:59.9421321Z (Default: ``False``) 2024-12-18T01:09:59.9421544Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-12-18T01:09:59.9421761Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-12-18T01:09:59.9421951Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-12-18T01:09:59.9422108Z this does not do anything. (Default: ``True``) 2024-12-18T01:09:59.9422324Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-12-18T01:09:59.9422535Z module classes to ignore for mixed precision when using an 2024-12-18T01:09:59.9422733Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-12-18T01:09:59.9422971Z applied to them separately with mixed precision disabled (meaning 2024-12-18T01:09:59.9423190Z that the final FSDP construction would deviate from the specified 2024-12-18T01:09:59.9423389Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-12-18T01:09:59.9423600Z not do anything. This API is experimental and subject to change. 2024-12-18T01:09:59.9423710Z (Default: ``(_BatchNorm,)``) 2024-12-18T01:09:59.9423795Z 2024-12-18T01:09:59.9423979Z .. note:: This API is experimental and subject to change. 2024-12-18T01:09:59.9424061Z 2024-12-18T01:09:59.9424298Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-12-18T01:09:59.9424383Z 2024-12-18T01:09:59.9424581Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-12-18T01:09:59.9424695Z precision, but buffers are not. 2024-12-18T01:09:59.9424778Z 2024-12-18T01:09:59.9424996Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-12-18T01:09:59.9425204Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-12-18T01:09:59.9425448Z Disabling FSDP's mixed precision for those norm modules only means that 2024-12-18T01:09:59.9425659Z the affine parameters are kept in ``float32``. However, this incurs 2024-12-18T01:09:59.9425909Z separate all-gathers and reduce-scatters for those norm modules, which 2024-12-18T01:09:59.9426126Z may be inefficient, so if the workload permits, the user should prefer 2024-12-18T01:09:59.9426273Z to still apply mixed precision to those modules. 2024-12-18T01:09:59.9426367Z 2024-12-18T01:09:59.9426570Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-12-18T01:09:59.9426788Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-12-18T01:09:59.9427014Z modules will have FSDP applied to them separately with mixed precision 2024-12-18T01:09:59.9427201Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-12-18T01:09:59.9427283Z 2024-12-18T01:09:59.9427490Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-12-18T01:09:59.9427717Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-12-18T01:09:59.9427889Z its ``cast_root_forward_inputs`` takes precedence over its 2024-12-18T01:09:59.9428080Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-12-18T01:09:59.9428293Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-12-18T01:09:59.9428612Z sufficient for the typical case where each FSDP instance has the same 2024-12-18T01:09:59.9428840Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-12-18T01:09:59.9429021Z ``param_dtype`` at the beginning of the model's forward pass. 2024-12-18T01:09:59.9429119Z 2024-12-18T01:09:59.9429331Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-12-18T01:09:59.9429578Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-12-18T01:09:59.9429840Z values to configure casting inputs or not before each instance's 2024-12-18T01:09:59.9430049Z forward. In such a case, since the casts happen before each FSDP 2024-12-18T01:09:59.9430263Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-12-18T01:09:59.9430728Z submodules run before its FSDP submodules to avoid the activation dtype 2024-12-18T01:09:59.9430951Z being changed due to a different ``MixedPrecision`` configuration. 2024-12-18T01:09:59.9431033Z 2024-12-18T01:09:59.9431142Z Example:: 2024-12-18T01:09:59.9431224Z 2024-12-18T01:09:59.9431407Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9431577Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-12-18T01:09:59.9431703Z >>> model[1] = FSDP( 2024-12-18T01:09:59.9431810Z >>> model[1], 2024-12-18T01:09:59.9432114Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-12-18T01:09:59.9432217Z >>> ) 2024-12-18T01:09:59.9432313Z >>> model = FSDP( 2024-12-18T01:09:59.9432402Z >>> model, 2024-12-18T01:09:59.9432716Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-12-18T01:09:59.9432803Z >>> ) 2024-12-18T01:09:59.9432897Z 2024-12-18T01:09:59.9433109Z The above shows a working example. On the other hand, if ``model[1]`` 2024-12-18T01:09:59.9433323Z were replaced with ``model[0]``, meaning that the submodule using 2024-12-18T01:09:59.9433546Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-12-18T01:09:59.9433769Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-12-18T01:09:59.9433869Z ones. 2024-12-18T01:09:59.9433951Z 2024-12-18T01:09:59.9434048Z 2024-12-18T01:09:59.9434297Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9434383Z 2024-12-18T01:09:59.9434493Z warnings.warn(msg) 2024-12-18T01:09:59.9434577Z 2024-12-18T01:09:59.9434811Z --- Parse Warning: 56 / 105 --- 2024-12-18T01:09:59.9435975Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=649. 2024-12-18T01:09:59.9436539Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9436796Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:09:59.9436901Z 2024-12-18T01:09:59.9437167Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-12-18T01:09:59.9437377Z The target module does not have to be a FSDP module. If the target 2024-12-18T01:09:59.9437609Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-12-18T01:09:59.9437694Z 2024-12-18T01:09:59.9437907Z .. note:: This API should be called for only the top-level (root) 2024-12-18T01:09:59.9437998Z module. 2024-12-18T01:09:59.9438081Z 2024-12-18T01:09:59.9438301Z .. note:: This API enables users to transparently use the conventional 2024-12-18T01:09:59.9438492Z ``state_dict`` API to take model checkpoints in cases where the 2024-12-18T01:09:59.9438715Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-12-18T01:09:59.9438923Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-12-18T01:09:59.9439173Z instances, while dispatching into `sharded_state_dict` implementation 2024-12-18T01:09:59.9439265Z for FSDP: 2024-12-18T01:09:59.9439490Z 2024-12-18T01:09:59.9439584Z Example:: 2024-12-18T01:09:59.9439668Z 2024-12-18T01:09:59.9439819Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9439930Z >>> model = DDP(FSDP(...)) 2024-12-18T01:09:59.9440058Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:59.9440153Z >>> model, 2024-12-18T01:09:59.9440286Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:09:59.9440518Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-12-18T01:09:59.9440778Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-12-18T01:09:59.9440883Z >>> ) 2024-12-18T01:09:59.9441048Z >>> param_state_dict = model.state_dict() 2024-12-18T01:09:59.9441236Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:09:59.9441320Z 2024-12-18T01:09:59.9441415Z Args: 2024-12-18T01:09:59.9441558Z module (torch.nn.Module): Root module. 2024-12-18T01:09:59.9441789Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:09:59.9442042Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-12-18T01:09:59.9442158Z target ``state_dict_type``. 2024-12-18T01:09:59.9442419Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-12-18T01:09:59.9442537Z for the optimizer state dict. 2024-12-18T01:09:59.9442622Z 2024-12-18T01:09:59.9442725Z Returns: 2024-12-18T01:09:59.9442945Z A StateDictSettings that include the previous state_dict type and 2024-12-18T01:09:59.9443074Z configuration for the module. 2024-12-18T01:09:59.9443159Z 2024-12-18T01:09:59.9443407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9443505Z 2024-12-18T01:09:59.9443602Z warnings.warn(msg) 2024-12-18T01:09:59.9443695Z 2024-12-18T01:09:59.9443908Z --- Parse Warning: 57 / 105 --- 2024-12-18T01:09:59.9445069Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=805. 2024-12-18T01:09:59.9445328Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9445576Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:09:59.9445671Z 2024-12-18T01:09:59.9445988Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-12-18T01:09:59.9446132Z :meth:`set_state_dict_type` for the detail. 2024-12-18T01:09:59.9446218Z 2024-12-18T01:09:59.9446324Z Example:: 2024-12-18T01:09:59.9446406Z 2024-12-18T01:09:59.9446539Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9446659Z >>> model = DDP(FSDP(...)) 2024-12-18T01:09:59.9446773Z >>> with FSDP.state_dict_type( 2024-12-18T01:09:59.9446882Z >>> model, 2024-12-18T01:09:59.9447014Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:09:59.9447101Z >>> ): 2024-12-18T01:09:59.9447241Z >>> checkpoint = model.state_dict() 2024-12-18T01:09:59.9447326Z 2024-12-18T01:09:59.9447430Z Args: 2024-12-18T01:09:59.9447559Z module (torch.nn.Module): Root module. 2024-12-18T01:09:59.9447806Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:09:59.9448037Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-12-18T01:09:59.9448272Z configuration for the target ``state_dict_type``. 2024-12-18T01:09:59.9448519Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-12-18T01:09:59.9448717Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-12-18T01:09:59.9448816Z 2024-12-18T01:09:59.9449068Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9457728Z 2024-12-18T01:09:59.9457913Z warnings.warn(msg) 2024-12-18T01:09:59.9458001Z 2024-12-18T01:09:59.9458380Z --- Parse Warning: 58 / 105 --- 2024-12-18T01:09:59.9459599Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1818. 2024-12-18T01:09:59.9459887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9459972Z 2024-12-18T01:09:59.9460215Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-12-18T01:09:59.9460314Z 2024-12-18T01:09:59.9460509Z The given state-dict can be transformed to one of three types: 2024-12-18T01:09:59.9460827Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-12-18T01:09:59.9460911Z 2024-12-18T01:09:59.9461157Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-12-18T01:09:59.9461375Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-12-18T01:09:59.9461468Z avoid OOM. 2024-12-18T01:09:59.9461566Z 2024-12-18T01:09:59.9461805Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-12-18T01:09:59.9462023Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-12-18T01:09:59.9462118Z memory. 2024-12-18T01:09:59.9462203Z 2024-12-18T01:09:59.9462436Z For local state_dict, no transformation will be performed. But a state 2024-12-18T01:09:59.9462672Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-12-18T01:09:59.9462804Z nature (this is not supported yet). 2024-12-18T01:09:59.9462888Z 2024-12-18T01:09:59.9463002Z Example:: 2024-12-18T01:09:59.9463086Z 2024-12-18T01:09:59.9463220Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9463474Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:09:59.9463634Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:09:59.9463827Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:09:59.9464029Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:09:59.9464129Z >>> # Save a checkpoint 2024-12-18T01:09:59.9464248Z >>> model, optim = ... 2024-12-18T01:09:59.9464359Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:59.9464464Z >>> model, 2024-12-18T01:09:59.9464586Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:59.9464735Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9464883Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9464970Z >>> ) 2024-12-18T01:09:59.9465098Z >>> state_dict = model.state_dict() 2024-12-18T01:09:59.9465270Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:09:59.9465433Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:09:59.9465531Z >>> # Load a checkpoint 2024-12-18T01:09:59.9465632Z >>> model, optim = ... 2024-12-18T01:09:59.9465795Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:09:59.9465905Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:59.9466038Z >>> model, 2024-12-18T01:09:59.9466192Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:59.9466340Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9466483Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9466568Z >>> ) 2024-12-18T01:09:59.9466699Z >>> model.load_state_dict(state_dict) 2024-12-18T01:09:59.9466850Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:09:59.9466977Z >>> model, optim, optim_state_dict 2024-12-18T01:09:59.9467063Z >>> ) 2024-12-18T01:09:59.9467231Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:09:59.9467314Z 2024-12-18T01:09:59.9467400Z Args: 2024-12-18T01:09:59.9467635Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:09:59.9467838Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:09:59.9467985Z were passed into the optimizer ``optim``. 2024-12-18T01:09:59.9468172Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:09:59.9468265Z parameters. 2024-12-18T01:09:59.9468588Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-12-18T01:09:59.9468798Z transform. If the value is None, optim.state_dict() will be used. ( 2024-12-18T01:09:59.9468911Z Default: ``None``) 2024-12-18T01:09:59.9469151Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:09:59.9469350Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:09:59.9469449Z Default: ``None``) 2024-12-18T01:09:59.9469534Z 2024-12-18T01:09:59.9469637Z Returns: 2024-12-18T01:09:59.9469831Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-12-18T01:09:59.9470016Z ``model``. The sharding of the optimizer state is based on 2024-12-18T01:09:59.9470117Z ``state_dict_type``. 2024-12-18T01:09:59.9470203Z 2024-12-18T01:09:59.9470469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9470554Z 2024-12-18T01:09:59.9470666Z warnings.warn(msg) 2024-12-18T01:09:59.9470749Z 2024-12-18T01:09:59.9470973Z --- Parse Warning: 59 / 105 --- 2024-12-18T01:09:59.9472169Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1916. 2024-12-18T01:09:59.9472435Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9472534Z 2024-12-18T01:09:59.9472886Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-12-18T01:09:59.9472983Z 2024-12-18T01:09:59.9473156Z Given a ``optim_state_dict`` that is transformed through 2024-12-18T01:09:59.9473382Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-12-18T01:09:59.9473592Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-12-18T01:09:59.9473780Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-12-18T01:09:59.9473876Z 2024-12-18T01:09:59.9474012Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9474259Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:09:59.9474420Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:09:59.9474610Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:09:59.9474810Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:09:59.9474911Z >>> # Save a checkpoint 2024-12-18T01:09:59.9475023Z >>> model, optim = ... 2024-12-18T01:09:59.9475198Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:59.9475301Z >>> model, 2024-12-18T01:09:59.9475425Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:59.9475557Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9475715Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9475803Z >>> ) 2024-12-18T01:09:59.9475931Z >>> state_dict = model.state_dict() 2024-12-18T01:09:59.9476050Z >>> original_osd = optim.state_dict() 2024-12-18T01:09:59.9476197Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-12-18T01:09:59.9476317Z >>> model, 2024-12-18T01:09:59.9476406Z >>> optim, 2024-12-18T01:09:59.9476534Z >>> optim_state_dict=original_osd 2024-12-18T01:09:59.9476647Z >>> ) 2024-12-18T01:09:59.9476808Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:09:59.9476905Z >>> # Load a checkpoint 2024-12-18T01:09:59.9477010Z >>> model, optim = ... 2024-12-18T01:09:59.9477175Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:09:59.9477282Z >>> FSDP.set_state_dict_type( 2024-12-18T01:09:59.9477384Z >>> model, 2024-12-18T01:09:59.9477505Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:09:59.9477651Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9477795Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:09:59.9477882Z >>> ) 2024-12-18T01:09:59.9478015Z >>> model.load_state_dict(state_dict) 2024-12-18T01:09:59.9478169Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:09:59.9478299Z >>> model, optim, optim_state_dict 2024-12-18T01:09:59.9478386Z >>> ) 2024-12-18T01:09:59.9478515Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:09:59.9478611Z 2024-12-18T01:09:59.9478700Z Args: 2024-12-18T01:09:59.9478909Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:09:59.9479114Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:09:59.9479264Z were passed into the optimizer ``optim``. 2024-12-18T01:09:59.9479445Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:09:59.9479539Z parameters. 2024-12-18T01:09:59.9479770Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-12-18T01:09:59.9479973Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-12-18T01:09:59.9480183Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-12-18T01:09:59.9480361Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-12-18T01:09:59.9480567Z load_directly (bool): If this is set to True, this API will also 2024-12-18T01:09:59.9480767Z call optim.load_state_dict(result) before returning the result. 2024-12-18T01:09:59.9480992Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-12-18T01:09:59.9481102Z (Default: ``False``) 2024-12-18T01:09:59.9481342Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:09:59.9481539Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:09:59.9481637Z Default: ``None``) 2024-12-18T01:09:59.9481732Z 2024-12-18T01:09:59.9481979Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9482062Z 2024-12-18T01:09:59.9482177Z warnings.warn(msg) 2024-12-18T01:09:59.9482261Z 2024-12-18T01:09:59.9482476Z --- Parse Warning: 60 / 105 --- 2024-12-18T01:09:59.9483467Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-12-18T01:09:59.9483802Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9483886Z 2024-12-18T01:09:59.9484111Z RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:09:59.9484205Z 2024-12-18T01:09:59.9484400Z It creates a user-specified module on a specified remote node. 2024-12-18T01:09:59.9484644Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:09:59.9484746Z executed on the remote node. 2024-12-18T01:09:59.9485010Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:09:59.9485185Z gradients back to the corresponding remote module. 2024-12-18T01:09:59.9485567Z It can be shared across processors using `RPC framework `__, 2024-12-18T01:09:59.9485780Z without incurring any overheads of copying the actual module, 2024-12-18T01:09:59.9485985Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-12-18T01:09:59.9486108Z pointing to the remote module. 2024-12-18T01:09:59.9486192Z 2024-12-18T01:09:59.9486394Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-12-18T01:09:59.9486610Z the ``forward`` method of the module returned by the ``module_cls``. 2024-12-18T01:09:59.9486694Z 2024-12-18T01:09:59.9487014Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-12-18T01:09:59.9487099Z 2024-12-18T01:09:59.9487358Z Particularly, to create a hybrid model, typically the local modules should be 2024-12-18T01:09:59.9487728Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-12-18T01:09:59.9487825Z Hybrid Example: 2024-12-18T01:09:59.9487960Z >>> class HybridModel(nn.Module): 2024-12-18T01:09:59.9488076Z >>> def __init__(self) -> None: 2024-12-18T01:09:59.9488206Z >>> nn.Module.__init__(self) 2024-12-18T01:09:59.9488349Z >>> self.remote_embedding = RemoteModule(...) 2024-12-18T01:09:59.9488487Z >>> self.local_linear = nn.Linear(...) 2024-12-18T01:09:59.9488571Z 2024-12-18T01:09:59.9488772Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:09:59.9489037Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:09:59.9489246Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-12-18T01:09:59.9489392Z ``def forward(input: Tensor) -> Tensor:`` and 2024-12-18T01:09:59.9489558Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-12-18T01:09:59.9489643Z 2024-12-18T01:09:59.9489752Z .. note:: 2024-12-18T01:09:59.9489899Z If the remote module is placed on a cuda device, 2024-12-18T01:09:59.9490150Z any input CPU tensors will be automatically moved to the same cuda device, 2024-12-18T01:09:59.9490547Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-12-18T01:09:59.9490645Z 2024-12-18T01:09:59.9490733Z Args: 2024-12-18T01:09:59.9491028Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:09:59.9491329Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:09:59.9491423Z formats: 2024-12-18T01:09:59.9491523Z 2024-12-18T01:09:59.9491666Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:09:59.9491831Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:09:59.9491918Z 2024-12-18T01:09:59.9492165Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:09:59.9492295Z module_cls (nn.Module): For example, 2024-12-18T01:09:59.9492478Z >>> class MyModule(nn.Module): 2024-12-18T01:09:59.9492595Z >>> def forward(input): 2024-12-18T01:09:59.9492703Z >>> return input + 1 2024-12-18T01:09:59.9492789Z >>> 2024-12-18T01:09:59.9492901Z >>> module_cls = MyModule 2024-12-18T01:09:59.9493123Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:09:59.9493315Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:09:59.9493607Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:09:59.9493862Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:09:59.9494110Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:09:59.9494359Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:09:59.9494448Z 2024-12-18T01:09:59.9494549Z Returns: 2024-12-18T01:09:59.9494793Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:09:59.9495039Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:09:59.9495311Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:09:59.9495456Z on the user-provided module on the remote side. 2024-12-18T01:09:59.9495551Z 2024-12-18T01:09:59.9495643Z Example:: 2024-12-18T01:09:59.9495808Z Run the following code in two different processes: 2024-12-18T01:09:59.9495892Z 2024-12-18T01:09:59.9496009Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9496117Z >>> # On worker 0: 2024-12-18T01:09:59.9496214Z >>> import torch 2024-12-18T01:09:59.9496356Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9496465Z >>> from torch import nn, Tensor 2024-12-18T01:09:59.9496699Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:09:59.9496785Z >>> 2024-12-18T01:09:59.9496925Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:09:59.9497058Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:09:59.9497185Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:09:59.9497285Z >>> ) 2024-12-18T01:09:59.9497394Z >>> input = torch.randn(128, 20) 2024-12-18T01:09:59.9497550Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:09:59.9497663Z >>> ret = ret_fut.wait() 2024-12-18T01:09:59.9497762Z >>> rpc.shutdown() 2024-12-18T01:09:59.9497857Z 2024-12-18T01:09:59.9497950Z >>> # On worker 1: 2024-12-18T01:09:59.9498044Z >>> import torch 2024-12-18T01:09:59.9498181Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9498268Z >>> 2024-12-18T01:09:59.9498420Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:09:59.9498519Z >>> rpc.shutdown() 2024-12-18T01:09:59.9498613Z 2024-12-18T01:09:59.9498867Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9498948Z 2024-12-18T01:09:59.9499061Z warnings.warn(msg) 2024-12-18T01:09:59.9499142Z 2024-12-18T01:09:59.9499359Z --- Parse Warning: 61 / 105 --- 2024-12-18T01:09:59.9500406Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-12-18T01:09:59.9500684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9500766Z 2024-12-18T01:09:59.9501086Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-12-18T01:09:59.9501233Z 2024-12-18T01:09:59.9501553Z This alternate initialization method can be particularly useful if we want to create multiple 2024-12-18T01:09:59.9501883Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-12-18T01:09:59.9501965Z 2024-12-18T01:09:59.9502240Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-12-18T01:09:59.9502428Z which is not supported. The recommended way is as follows: 2024-12-18T01:09:59.9502512Z 2024-12-18T01:09:59.9502644Z 1. the sender creates a RemoteModule; 2024-12-18T01:09:59.9502830Z 2. the sender sends its ``module_rref`` over RPC; 2024-12-18T01:09:59.9503202Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-12-18T01:09:59.9503285Z 2024-12-18T01:09:59.9503379Z Example:: 2024-12-18T01:09:59.9503545Z Run the following code in two different processes: 2024-12-18T01:09:59.9503631Z 2024-12-18T01:09:59.9503759Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9503856Z >>> # On worker 0: 2024-12-18T01:09:59.9503950Z >>> import torch 2024-12-18T01:09:59.9504091Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9504201Z >>> from torch import nn, Tensor 2024-12-18T01:09:59.9504433Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:09:59.9504519Z >>> 2024-12-18T01:09:59.9504675Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:09:59.9504789Z >>> remote_module = RemoteModule( 2024-12-18T01:09:59.9504918Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:09:59.9505016Z >>> ) 2024-12-18T01:09:59.9505100Z >>> 2024-12-18T01:09:59.9505226Z >>> remote_module1 = rpc.rpc_sync( 2024-12-18T01:09:59.9505323Z >>> "worker1/cpu", 2024-12-18T01:09:59.9505447Z >>> RemoteModule.init_from_module_rref, 2024-12-18T01:09:59.9505617Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-12-18T01:09:59.9505702Z >>> ) 2024-12-18T01:09:59.9505812Z >>> rpc.shutdown() 2024-12-18T01:09:59.9505894Z 2024-12-18T01:09:59.9505997Z >>> # On worker 1: 2024-12-18T01:09:59.9506088Z >>> import torch 2024-12-18T01:09:59.9506214Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9506312Z >>> 2024-12-18T01:09:59.9506452Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:09:59.9506558Z >>> rpc.shutdown() 2024-12-18T01:09:59.9506641Z 2024-12-18T01:09:59.9506727Z Args: 2024-12-18T01:09:59.9507034Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:09:59.9507327Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:09:59.9507429Z formats: 2024-12-18T01:09:59.9507514Z 2024-12-18T01:09:59.9507670Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:09:59.9507823Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:09:59.9507906Z 2024-12-18T01:09:59.9508166Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:09:59.9508502Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-12-18T01:09:59.9508630Z the created remote module. 2024-12-18T01:09:59.9508909Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:09:59.9509155Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:09:59.9509377Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:09:59.9509612Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:09:59.9509771Z 2024-12-18T01:09:59.9509859Z Returns: 2024-12-18T01:09:59.9510116Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:09:59.9510353Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-12-18T01:09:59.9510639Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:09:59.9510782Z on the user-provided module on the remote side. 2024-12-18T01:09:59.9510866Z 2024-12-18T01:09:59.9511129Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9511239Z 2024-12-18T01:09:59.9511352Z warnings.warn(msg) 2024-12-18T01:09:59.9511434Z 2024-12-18T01:09:59.9511641Z --- Parse Warning: 62 / 105 --- 2024-12-18T01:09:59.9512644Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-12-18T01:09:59.9512910Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9513008Z 2024-12-18T01:09:59.9513239Z A RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:09:59.9513338Z 2024-12-18T01:09:59.9513533Z It creates a user-specified module on a specified remote node. 2024-12-18T01:09:59.9513765Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:09:59.9513885Z executed on the remote node. 2024-12-18T01:09:59.9514122Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:09:59.9514295Z gradients back to the corresponding remote module. 2024-12-18T01:09:59.9514378Z 2024-12-18T01:09:59.9514608Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-12-18T01:09:59.9514823Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-12-18T01:09:59.9515072Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-12-18T01:09:59.9515282Z and ``forward`` are the same as the ``forward`` method of the module 2024-12-18T01:09:59.9515393Z returned by the ``module_cls``. 2024-12-18T01:09:59.9515487Z 2024-12-18T01:09:59.9515684Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:09:59.9515948Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:09:59.9516175Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-12-18T01:09:59.9516258Z 2024-12-18T01:09:59.9516400Z | ``def forward(input: Tensor) -> Tensor:`` 2024-12-18T01:09:59.9516569Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-12-18T01:09:59.9516664Z 2024-12-18T01:09:59.9516749Z Args: 2024-12-18T01:09:59.9517049Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:09:59.9517405Z The format should be "/", where the device field can be parsed as torch.device type. 2024-12-18T01:09:59.9517546Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-12-18T01:09:59.9517805Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:09:59.9518051Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-12-18T01:09:59.9518147Z 2024-12-18T01:09:59.9518263Z >>> class MyModule(nn.Module): 2024-12-18T01:09:59.9518367Z >>> def forward(input): 2024-12-18T01:09:59.9518482Z >>> return input + 1 2024-12-18T01:09:59.9518570Z >>> 2024-12-18T01:09:59.9518685Z >>> module_cls = MyModule 2024-12-18T01:09:59.9518767Z 2024-12-18T01:09:59.9518979Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:09:59.9519227Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:09:59.9519322Z 2024-12-18T01:09:59.9519409Z Returns: 2024-12-18T01:09:59.9519666Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:09:59.9519900Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:09:59.9520185Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:09:59.9520356Z on the user-provided module on the remote side. 2024-12-18T01:09:59.9520453Z 2024-12-18T01:09:59.9520545Z Example:: 2024-12-18T01:09:59.9520700Z Run the following code in two different processes: 2024-12-18T01:09:59.9520840Z 2024-12-18T01:09:59.9520957Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9521062Z >>> # On worker 0: 2024-12-18T01:09:59.9521158Z >>> import torch 2024-12-18T01:09:59.9521292Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9521413Z >>> from torch import nn, Tensor 2024-12-18T01:09:59.9521632Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:09:59.9521729Z >>> 2024-12-18T01:09:59.9521870Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:09:59.9522003Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:09:59.9522130Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:09:59.9522218Z >>> ) 2024-12-18T01:09:59.9522345Z >>> input = torch.randn(128, 20) 2024-12-18T01:09:59.9522501Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:09:59.9522617Z >>> ret = ret_fut.wait() 2024-12-18T01:09:59.9522714Z >>> rpc.shutdown() 2024-12-18T01:09:59.9522797Z 2024-12-18T01:09:59.9522904Z >>> # On worker 1: 2024-12-18T01:09:59.9522998Z >>> import torch 2024-12-18T01:09:59.9523138Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9523224Z >>> 2024-12-18T01:09:59.9523373Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:09:59.9523470Z >>> rpc.shutdown() 2024-12-18T01:09:59.9523550Z 2024-12-18T01:09:59.9523752Z Furthermore, a more practical example that is combined with 2024-12-18T01:09:59.9524222Z `DistributedDataParallel `__ (DDP) 2024-12-18T01:09:59.9524561Z can be found in this `tutorial `__. 2024-12-18T01:09:59.9524646Z 2024-12-18T01:09:59.9524907Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9524988Z 2024-12-18T01:09:59.9525084Z warnings.warn(msg) 2024-12-18T01:09:59.9525176Z 2024-12-18T01:09:59.9525372Z --- Parse Warning: 63 / 105 --- 2024-12-18T01:09:59.9526368Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-12-18T01:09:59.9526631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9526726Z 2024-12-18T01:09:59.9526965Z DistributedOptimizer takes remote references to parameters scattered 2024-12-18T01:09:59.9527201Z across workers and applies the given optimizer locally for each parameter. 2024-12-18T01:09:59.9527296Z 2024-12-18T01:09:59.9527528Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-12-18T01:09:59.9527697Z to retrieve the gradients for specific parameters. 2024-12-18T01:09:59.9527780Z 2024-12-18T01:09:59.9527877Z Concurrent calls to 2024-12-18T01:09:59.9528091Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-12-18T01:09:59.9528289Z either from the same or different clients, will 2024-12-18T01:09:59.9528518Z be serialized on each worker -- as each worker's optimizer can only work 2024-12-18T01:09:59.9528721Z on one set of gradients at a time. However, there is no guarantee that 2024-12-18T01:09:59.9528975Z the full forward-backward-optimizer sequence will execute for one client 2024-12-18T01:09:59.9529190Z at a time. This means that the gradients being applied may not correspond 2024-12-18T01:09:59.9529414Z to the latest forward pass executed on a given worker. Also, there is no 2024-12-18T01:09:59.9529566Z guaranteed ordering across workers. 2024-12-18T01:09:59.9529648Z 2024-12-18T01:09:59.9529940Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-12-18T01:09:59.9530167Z by default, so that optimizer updates are not blocked by the Python Global 2024-12-18T01:09:59.9530701Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-12-18T01:09:59.9530945Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-12-18T01:09:59.9531196Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-12-18T01:09:59.9531314Z for your own custom optimizers. 2024-12-18T01:09:59.9531397Z 2024-12-18T01:09:59.9531497Z Args: 2024-12-18T01:09:59.9531690Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-12-18T01:09:59.9531813Z instantiate on each worker. 2024-12-18T01:09:59.9532027Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-12-18T01:09:59.9532120Z to optimize. 2024-12-18T01:09:59.9532346Z args: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:09:59.9532568Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:09:59.9532661Z 2024-12-18T01:09:59.9532757Z Example:: 2024-12-18T01:09:59.9532873Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9533044Z >>> import torch.distributed.autograd as dist_autograd 2024-12-18T01:09:59.9533168Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:09:59.9533285Z >>> from torch import optim 2024-12-18T01:09:59.9533470Z >>> from torch.distributed.optim import DistributedOptimizer 2024-12-18T01:09:59.9533567Z >>> 2024-12-18T01:09:59.9533702Z >>> with dist_autograd.context() as context_id: 2024-12-18T01:09:59.9533800Z >>> # Forward pass. 2024-12-18T01:09:59.9534013Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-12-18T01:09:59.9534208Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-12-18T01:09:59.9534345Z >>> loss = rref1.to_here() + rref2.to_here() 2024-12-18T01:09:59.9534430Z >>> 2024-12-18T01:09:59.9534528Z >>> # Backward pass. 2024-12-18T01:09:59.9534692Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-12-18T01:09:59.9534777Z >>> 2024-12-18T01:09:59.9534882Z >>> # Optimizer. 2024-12-18T01:09:59.9535008Z >>> dist_optim = DistributedOptimizer( 2024-12-18T01:09:59.9535112Z >>> optim.SGD, 2024-12-18T01:09:59.9535207Z >>> [rref1, rref2], 2024-12-18T01:09:59.9535298Z >>> lr=0.05, 2024-12-18T01:09:59.9535394Z >>> ) 2024-12-18T01:09:59.9535506Z >>> dist_optim.step(context_id) 2024-12-18T01:09:59.9535600Z 2024-12-18T01:09:59.9535756Z __ https://github.com/pytorch/tutorials/pull/1465 2024-12-18T01:09:59.9535840Z 2024-12-18T01:09:59.9536341Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9536428Z 2024-12-18T01:09:59.9536544Z warnings.warn(msg) 2024-12-18T01:09:59.9536627Z 2024-12-18T01:09:59.9536848Z --- Parse Warning: 64 / 105 --- 2024-12-18T01:09:59.9538015Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-12-18T01:09:59.9538319Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9538413Z 2024-12-18T01:09:59.9538802Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-12-18T01:09:59.9538967Z This optimizer runs local optimizer at every step. 2024-12-18T01:09:59.9539334Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-12-18T01:09:59.9539434Z 2024-12-18T01:09:59.9539519Z Args: 2024-12-18T01:09:59.9539665Z optim: The local optimizer. 2024-12-18T01:09:59.9539901Z averager: A model averager instance to run post-localSGD algorithm. 2024-12-18T01:09:59.9539988Z 2024-12-18T01:09:59.9540096Z Example:: 2024-12-18T01:09:59.9540178Z 2024-12-18T01:09:59.9540308Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9540414Z >>> import torch 2024-12-18T01:09:59.9540532Z >>> import torch.distributed as dist 2024-12-18T01:09:59.9540813Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:09:59.9540915Z >>> import torch.nn as nn 2024-12-18T01:09:59.9541152Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-12-18T01:09:59.9541425Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:09:59.9541531Z >>> PostLocalSGDState, 2024-12-18T01:09:59.9541643Z >>> post_localSGD_hook, 2024-12-18T01:09:59.9541730Z >>> ) 2024-12-18T01:09:59.9541827Z >>> 2024-12-18T01:09:59.9541980Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:09:59.9542124Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:09:59.9542222Z >>> ) 2024-12-18T01:09:59.9542306Z >>> 2024-12-18T01:09:59.9542465Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:09:59.9542759Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:09:59.9542933Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:09:59.9543015Z >>> 2024-12-18T01:09:59.9543217Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-12-18T01:09:59.9543476Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-12-18T01:09:59.9543642Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:09:59.9543862Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-12-18T01:09:59.9543976Z >>> opt = PostLocalSGDOptimizer( 2024-12-18T01:09:59.9544095Z >>> optim=local_optim, 2024-12-18T01:09:59.9544340Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:09:59.9544423Z >>> ) 2024-12-18T01:09:59.9544519Z >>> 2024-12-18T01:09:59.9544740Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-12-18T01:09:59.9545051Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-12-18T01:09:59.9545423Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-12-18T01:09:59.9545544Z >>> for step in range(0, 200): 2024-12-18T01:09:59.9545640Z >>> opt.zero_grad() 2024-12-18T01:09:59.9545759Z >>> loss = loss_fn(output, labels) 2024-12-18T01:09:59.9545868Z >>> loss.backward() 2024-12-18T01:09:59.9545960Z >>> opt.step() 2024-12-18T01:09:59.9546055Z 2024-12-18T01:09:59.9546341Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9546452Z 2024-12-18T01:09:59.9546562Z warnings.warn(msg) 2024-12-18T01:09:59.9546643Z 2024-12-18T01:09:59.9546855Z --- Parse Warning: 65 / 105 --- 2024-12-18T01:09:59.9547932Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-12-18T01:09:59.9548240Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9548401Z 2024-12-18T01:09:59.9548838Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-12-18T01:09:59.9548934Z 2024-12-18T01:09:59.9549065Z The sharing is done as described by ZeRO_. 2024-12-18T01:09:59.9549160Z 2024-12-18T01:09:59.9549312Z The local optimizer instance in each rank is only 2024-12-18T01:09:59.9549566Z responsible for updating approximately ``1 / world_size`` parameters and 2024-12-18T01:09:59.9549767Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-12-18T01:09:59.9550012Z parameters are updated locally, each rank will broadcast its parameters to 2024-12-18T01:09:59.9550208Z all other peers to keep all model replicas in the same state. 2024-12-18T01:09:59.9550403Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-12-18T01:09:59.9550674Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-12-18T01:09:59.9550769Z memory consumption. 2024-12-18T01:09:59.9550854Z 2024-12-18T01:09:59.9551124Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-12-18T01:09:59.9551356Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-12-18T01:09:59.9551607Z not divided among ranks. The partition is arbitrary and might not match the 2024-12-18T01:09:59.9551739Z the parameter registration or usage order. 2024-12-18T01:09:59.9551835Z 2024-12-18T01:09:59.9551926Z Arguments: 2024-12-18T01:09:59.9552115Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-12-18T01:09:59.9552315Z or :class:`dict` s giving all parameters, which will be sharded 2024-12-18T01:09:59.9552409Z across ranks. 2024-12-18T01:09:59.9552504Z 2024-12-18T01:09:59.9552596Z Keyword Args: 2024-12-18T01:09:59.9552834Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-12-18T01:09:59.9552926Z optimizer. 2024-12-18T01:09:59.9553136Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-12-18T01:09:59.9553347Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-12-18T01:09:59.9553499Z :meth:`torch.distributed.init_process_group`). 2024-12-18T01:09:59.9553748Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-12-18T01:09:59.9553960Z packed into buckets to speed up communication, and ``param.data`` 2024-12-18T01:09:59.9554175Z fields point to bucket views at different offsets; if ``False``, 2024-12-18T01:09:59.9554380Z each individual parameter is communicated separately, and each 2024-12-18T01:09:59.9554532Z ``params.data`` stays intact (default: ``False``). 2024-12-18T01:09:59.9554741Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-12-18T01:09:59.9554943Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-12-18T01:09:59.9555171Z synchronization; this requires (1) either a functional optimizer 2024-12-18T01:09:59.9555357Z for the ``optimizer_class`` argument or one with a functional 2024-12-18T01:09:59.9555550Z equivalent and (2) registering a DDP communication hook 2024-12-18T01:09:59.9555807Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-12-18T01:09:59.9555980Z parameters are packed into buckets matching those in 2024-12-18T01:09:59.9556152Z :class:`DistributedDataParallel`, meaning that the 2024-12-18T01:09:59.9556304Z ``parameters_as_bucket_view`` argument is ignored. 2024-12-18T01:09:59.9556506Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-12-18T01:09:59.9556600Z (per normal). 2024-12-18T01:09:59.9556714Z (default: ``False``) 2024-12-18T01:09:59.9556968Z **defaults: any trailing arguments, which are forwarded to the local 2024-12-18T01:09:59.9557060Z optimizer. 2024-12-18T01:09:59.9557158Z 2024-12-18T01:09:59.9557278Z Example:: 2024-12-18T01:09:59.9557375Z 2024-12-18T01:09:59.9557473Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9557573Z >>> import torch.nn as nn 2024-12-18T01:09:59.9557794Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-12-18T01:09:59.9557994Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-12-18T01:09:59.9558233Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-12-18T01:09:59.9558353Z >>> ddp = DDP(model, device_ids=[rank]) 2024-12-18T01:09:59.9558487Z >>> opt = ZeroRedundancyOptimizer( 2024-12-18T01:09:59.9558590Z >>> ddp.parameters(), 2024-12-18T01:09:59.9558714Z >>> optimizer_class=torch.optim.Adam, 2024-12-18T01:09:59.9558816Z >>> lr=0.01 2024-12-18T01:09:59.9558901Z >>> ) 2024-12-18T01:09:59.9559023Z >>> ddp(inputs).sum().backward() 2024-12-18T01:09:59.9559113Z >>> opt.step() 2024-12-18T01:09:59.9559192Z 2024-12-18T01:09:59.9559294Z .. warning:: 2024-12-18T01:09:59.9559503Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-12-18T01:09:59.9559663Z passed-in parameters are the same dense type. 2024-12-18T01:09:59.9559749Z 2024-12-18T01:09:59.9559837Z .. warning:: 2024-12-18T01:09:59.9560059Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-12-18T01:09:59.9560256Z the way that overlapping :class:`DistributedDataParallel` with 2024-12-18T01:09:59.9560498Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-12-18T01:09:59.9560711Z two or three training iterations do not perform parameter updates in 2024-12-18T01:09:59.9560915Z the optimizer step, depending on if ``static_graph=False`` or 2024-12-18T01:09:59.9561099Z ``static_graph=True``, respectively. This is because it needs 2024-12-18T01:09:59.9561284Z information about the gradient bucketing strategy used by 2024-12-18T01:09:59.9561517Z :class:`DistributedDataParallel`, which is not finalized until the 2024-12-18T01:09:59.9561712Z second forward pass if ``static_graph=False`` or until the third 2024-12-18T01:09:59.9561937Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-12-18T01:09:59.9562043Z is to prepend dummy inputs. 2024-12-18T01:09:59.9562140Z 2024-12-18T01:09:59.9562391Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-12-18T01:09:59.9562472Z 2024-12-18T01:09:59.9562614Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-12-18T01:09:59.9562698Z 2024-12-18T01:09:59.9562792Z 2024-12-18T01:09:59.9563039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9563134Z 2024-12-18T01:09:59.9563232Z warnings.warn(msg) 2024-12-18T01:09:59.9563314Z 2024-12-18T01:09:59.9563535Z --- Parse Warning: 66 / 105 --- 2024-12-18T01:09:59.9564508Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-12-18T01:09:59.9564842Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9564924Z 2024-12-18T01:09:59.9565164Z Custom reducer class that can be used to specify a custom operation that 2024-12-18T01:09:59.9565336Z reduces losses of multiple microbatches into one value. 2024-12-18T01:09:59.9565417Z 2024-12-18T01:09:59.9565515Z Example: 2024-12-18T01:09:59.9565611Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9565732Z >>> sum_reducer = _CustomReducer( 2024-12-18T01:09:59.9565856Z >>> torch.tensor(0.0), 2024-12-18T01:09:59.9565956Z >>> lambda a, b: a + b 2024-12-18T01:09:59.9566049Z >>> ) 2024-12-18T01:09:59.9566128Z 2024-12-18T01:09:59.9566417Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9566496Z 2024-12-18T01:09:59.9566594Z warnings.warn(msg) 2024-12-18T01:09:59.9566693Z 2024-12-18T01:09:59.9566878Z --- Parse Warning: 67 / 105 --- 2024-12-18T01:09:59.9567807Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-12-18T01:09:59.9568069Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9568163Z 2024-12-18T01:09:59.9568403Z A decorator for a function indicating that the return value of the function 2024-12-18T01:09:59.9568612Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-12-18T01:09:59.9568863Z function can run asynchronously on the RPC callee. More specifically, the 2024-12-18T01:09:59.9569100Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-12-18T01:09:59.9569349Z function and installs subsequent processing steps as a callback to that 2024-12-18T01:09:59.9569583Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-12-18T01:09:59.9569796Z from the :class:`~torch.futures.Future` when completed and send the 2024-12-18T01:09:59.9569976Z value back as the RPC response. That also means the returned 2024-12-18T01:09:59.9570202Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-12-18T01:09:59.9570437Z sent through RPC. This decorator is useful when the wrapped function's 2024-12-18T01:09:59.9570631Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-12-18T01:09:59.9570870Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-12-18T01:09:59.9570951Z 2024-12-18T01:09:59.9571177Z .. note:: To enable asynchronous execution, applications must pass the 2024-12-18T01:09:59.9571407Z function object returned by this decorator to RPC APIs. If RPC detected 2024-12-18T01:09:59.9571638Z attributes installed by this decorator, it knows that this function 2024-12-18T01:09:59.9571825Z returns a ``Future`` object and will handle that accordingly. 2024-12-18T01:09:59.9572036Z However, this does not mean this decorator has to be outmost one when 2024-12-18T01:09:59.9572272Z defining a function. For example, when combined with ``@staticmethod`` 2024-12-18T01:09:59.9572480Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-12-18T01:09:59.9572711Z inner decorator to allow the target function be recognized as a static 2024-12-18T01:09:59.9572941Z or class function. This target function can still execute asynchronously 2024-12-18T01:09:59.9573176Z because, when accessed, the static or class method preserves attributes 2024-12-18T01:09:59.9573332Z installed by ``@rpc.functions.async_execution``. 2024-12-18T01:09:59.9573412Z 2024-12-18T01:09:59.9573506Z 2024-12-18T01:09:59.9573595Z Example:: 2024-12-18T01:09:59.9573873Z The returned :class:`~torch.futures.Future` object can come from 2024-12-18T01:09:59.9574008Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-12-18T01:09:59.9574230Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-12-18T01:09:59.9574418Z constructor. The example below shows directly using the 2024-12-18T01:09:59.9574548Z :class:`~torch.futures.Future` returned by 2024-12-18T01:09:59.9574682Z :meth:`~torch.futures.Future.then`. 2024-12-18T01:09:59.9574766Z 2024-12-18T01:09:59.9574898Z >>> from torch.distributed import rpc 2024-12-18T01:09:59.9575008Z >>> 2024-12-18T01:09:59.9575123Z >>> # omitting setup and shutdown RPC 2024-12-18T01:09:59.9575221Z >>> 2024-12-18T01:09:59.9575314Z >>> # On all workers 2024-12-18T01:09:59.9575469Z >>> @rpc.functions.async_execution 2024-12-18T01:09:59.9575587Z >>> def async_add_chained(to, x, y, z): 2024-12-18T01:09:59.9575783Z >>> # This function runs on "worker1" and returns immediately when 2024-12-18T01:09:59.9575987Z >>> # the callback is installed through the `then(cb)` API. In the 2024-12-18T01:09:59.9576167Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-12-18T01:09:59.9576340Z >>> # When the return value of that `rpc_async` arrives at 2024-12-18T01:09:59.9576527Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-12-18T01:09:59.9576725Z >>> # and set the value for the previously returned `Future`, which 2024-12-18T01:09:59.9576909Z >>> # will then trigger RPC to send the result back to "worker0". 2024-12-18T01:09:59.9577075Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:59.9577201Z >>> lambda fut: fut.wait() + z 2024-12-18T01:09:59.9577286Z >>> ) 2024-12-18T01:09:59.9577385Z >>> 2024-12-18T01:09:59.9577475Z >>> # On worker0 2024-12-18T01:09:59.9577589Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9577688Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:59.9577776Z >>> "worker1", 2024-12-18T01:09:59.9577887Z >>> async_add_chained, 2024-12-18T01:09:59.9578007Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-12-18T01:09:59.9578103Z >>> ) 2024-12-18T01:09:59.9578224Z >>> print(ret) # prints tensor([3., 3.]) 2024-12-18T01:09:59.9578304Z 2024-12-18T01:09:59.9578540Z When combined with TorchScript decorators, this decorator must be the 2024-12-18T01:09:59.9578632Z outmost one. 2024-12-18T01:09:59.9578727Z 2024-12-18T01:09:59.9578831Z >>> from torch import Tensor 2024-12-18T01:09:59.9578944Z >>> from torch.futures import Future 2024-12-18T01:09:59.9579078Z >>> from torch.distributed import rpc 2024-12-18T01:09:59.9579161Z >>> 2024-12-18T01:09:59.9579289Z >>> # omitting setup and shutdown RPC 2024-12-18T01:09:59.9579372Z >>> 2024-12-18T01:09:59.9579477Z >>> # On all workers 2024-12-18T01:09:59.9579576Z >>> @torch.jit.script 2024-12-18T01:09:59.9579718Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-12-18T01:09:59.9579825Z >>> return x + y 2024-12-18T01:09:59.9579907Z >>> 2024-12-18T01:09:59.9580031Z >>> @rpc.functions.async_execution 2024-12-18T01:09:59.9580125Z >>> @torch.jit.script 2024-12-18T01:09:59.9580310Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-12-18T01:09:59.9580465Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-12-18T01:09:59.9580549Z >>> 2024-12-18T01:09:59.9580649Z >>> # On worker0 2024-12-18T01:09:59.9580757Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:59.9580865Z >>> "worker1", 2024-12-18T01:09:59.9580957Z >>> async_add, 2024-12-18T01:09:59.9581077Z >>> args=("worker2", torch.ones(2), 1) 2024-12-18T01:09:59.9581232Z >>> ) 2024-12-18T01:09:59.9581353Z >>> print(ret) # prints tensor([2., 2.]) 2024-12-18T01:09:59.9581452Z 2024-12-18T01:09:59.9581675Z When combined with static or class method, this decorator must be the 2024-12-18T01:09:59.9581765Z inner one. 2024-12-18T01:09:59.9581864Z 2024-12-18T01:09:59.9581986Z >>> from torch.distributed import rpc 2024-12-18T01:09:59.9582088Z >>> 2024-12-18T01:09:59.9582206Z >>> # omitting setup and shutdown RPC 2024-12-18T01:09:59.9582290Z >>> 2024-12-18T01:09:59.9582401Z >>> # On all workers 2024-12-18T01:09:59.9582541Z >>> class AsyncExecutionClass: 2024-12-18T01:09:59.9582641Z >>> 2024-12-18T01:09:59.9582738Z >>> @staticmethod 2024-12-18T01:09:59.9582896Z >>> @rpc.functions.async_execution 2024-12-18T01:09:59.9583017Z >>> def static_async_add(to, x, y, z): 2024-12-18T01:09:59.9583192Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:59.9583326Z >>> lambda fut: fut.wait() + z 2024-12-18T01:09:59.9583415Z >>> ) 2024-12-18T01:09:59.9583516Z >>> 2024-12-18T01:09:59.9583613Z >>> @classmethod 2024-12-18T01:09:59.9583734Z >>> @rpc.functions.async_execution 2024-12-18T01:09:59.9583875Z >>> def class_async_add(cls, to, x, y, z): 2024-12-18T01:09:59.9584001Z >>> ret_fut = torch.futures.Future() 2024-12-18T01:09:59.9584168Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:59.9584323Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-12-18T01:09:59.9584425Z >>> ) 2024-12-18T01:09:59.9584524Z >>> return ret_fut 2024-12-18T01:09:59.9584610Z >>> 2024-12-18T01:09:59.9584745Z >>> @rpc.functions.async_execution 2024-12-18T01:09:59.9584872Z >>> def bound_async_add(self, to, x, y, z): 2024-12-18T01:09:59.9585062Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:09:59.9585178Z >>> lambda fut: fut.wait() + z 2024-12-18T01:09:59.9585269Z >>> ) 2024-12-18T01:09:59.9585369Z >>> 2024-12-18T01:09:59.9585459Z >>> # On worker0 2024-12-18T01:09:59.9585569Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:59.9585660Z >>> "worker1", 2024-12-18T01:09:59.9585810Z >>> AsyncExecutionClass.static_async_add, 2024-12-18T01:09:59.9585932Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:09:59.9586020Z >>> ) 2024-12-18T01:09:59.9586154Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:59.9586237Z >>> 2024-12-18T01:09:59.9586346Z >>> ret = rpc.rpc_sync( 2024-12-18T01:09:59.9586439Z >>> "worker1", 2024-12-18T01:09:59.9586572Z >>> AsyncExecutionClass.class_async_add, 2024-12-18T01:09:59.9586702Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:09:59.9586790Z >>> ) 2024-12-18T01:09:59.9586920Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:59.9587003Z 2024-12-18T01:09:59.9587164Z This decorator also works with RRef helpers, i.e., . 2024-12-18T01:09:59.9587317Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-12-18T01:09:59.9587469Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-12-18T01:09:59.9587620Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-12-18T01:09:59.9587703Z 2024-12-18T01:09:59.9587834Z >>> from torch.distributed import rpc 2024-12-18T01:09:59.9587919Z >>> 2024-12-18T01:09:59.9588058Z >>> # reuse the AsyncExecutionClass class above 2024-12-18T01:09:59.9588224Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:09:59.9588520Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-12-18T01:09:59.9588656Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:59.9588823Z >>> 2024-12-18T01:09:59.9588989Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:09:59.9589223Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-12-18T01:09:59.9589344Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:59.9589442Z >>> 2024-12-18T01:09:59.9589594Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:09:59.9589841Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-12-18T01:09:59.9589987Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:09:59.9590073Z 2024-12-18T01:09:59.9590339Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9590448Z 2024-12-18T01:09:59.9590558Z warnings.warn(msg) 2024-12-18T01:09:59.9590639Z 2024-12-18T01:09:59.9590868Z --- Parse Warning: 68 / 105 --- 2024-12-18T01:09:59.9591926Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-12-18T01:09:59.9592189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9592284Z 2024-12-18T01:09:59.9592489Z Set device mapping between each RPC caller and callee pair. This 2024-12-18T01:09:59.9592686Z function can be called multiple times to incrementally add 2024-12-18T01:09:59.9592803Z device placement configurations. 2024-12-18T01:09:59.9592897Z 2024-12-18T01:09:59.9592982Z Args: 2024-12-18T01:09:59.9593080Z to (str): Callee name. 2024-12-18T01:09:59.9593292Z device_map (Dict of int, str, or torch.device): Device placement 2024-12-18T01:09:59.9593473Z mappings from this worker to the callee. This map must be 2024-12-18T01:09:59.9593580Z invertible. 2024-12-18T01:09:59.9593662Z 2024-12-18T01:09:59.9593751Z Example: 2024-12-18T01:09:59.9593879Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9593974Z >>> # both workers 2024-12-18T01:09:59.9594082Z >>> def add(x, y): 2024-12-18T01:09:59.9594221Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-12-18T01:09:59.9594340Z >>> return x + y, (x + y).to(2) 2024-12-18T01:09:59.9594424Z >>> 2024-12-18T01:09:59.9594515Z >>> # on worker 0 2024-12-18T01:09:59.9594665Z >>> options = TensorPipeRpcBackendOptions( 2024-12-18T01:09:59.9594770Z >>> num_worker_threads=8, 2024-12-18T01:09:59.9594895Z >>> device_maps={"worker1": {0: 1}} 2024-12-18T01:09:59.9595032Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-12-18T01:09:59.9595118Z >>> ) 2024-12-18T01:09:59.9595261Z >>> options.set_device_map("worker1", {1: 2}) 2024-12-18T01:09:59.9595393Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-12-18T01:09:59.9595488Z >>> 2024-12-18T01:09:59.9595584Z >>> rpc.init_rpc( 2024-12-18T01:09:59.9595675Z >>> "worker0", 2024-12-18T01:09:59.9595775Z >>> rank=0, 2024-12-18T01:09:59.9595870Z >>> world_size=2, 2024-12-18T01:09:59.9596010Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-12-18T01:09:59.9596123Z >>> rpc_backend_options=options 2024-12-18T01:09:59.9596220Z >>> ) 2024-12-18T01:09:59.9596305Z >>> 2024-12-18T01:09:59.9596402Z >>> x = torch.ones(2) 2024-12-18T01:09:59.9596577Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-12-18T01:09:59.9596764Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-12-18T01:09:59.9596964Z >>> # sending the return value back, it will follow the invert of 2024-12-18T01:09:59.9597141Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-12-18T01:09:59.9597293Z >>> # cuda:1 on worker0 2024-12-18T01:09:59.9597454Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-12-18T01:09:59.9597600Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-12-18T01:09:59.9597695Z 2024-12-18T01:09:59.9597949Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9598047Z 2024-12-18T01:09:59.9598149Z warnings.warn(msg) 2024-12-18T01:09:59.9598231Z 2024-12-18T01:09:59.9598438Z --- Parse Warning: 69 / 105 --- 2024-12-18T01:09:59.9599469Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_func_map.py line=32. 2024-12-18T01:09:59.9599746Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9599832Z 2024-12-18T01:09:59.9600111Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2024-12-18T01:09:59.9600387Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2024-12-18T01:09:59.9600651Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2024-12-18T01:09:59.9600825Z :class:`DTensor` according to the ``out_placements``. 2024-12-18T01:09:59.9600908Z 2024-12-18T01:09:59.9601011Z Args: 2024-12-18T01:09:59.9601222Z func (Callable): the function to be applied on each local shard of 2024-12-18T01:09:59.9601336Z :class:`DTensor` s. 2024-12-18T01:09:59.9601565Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-12-18T01:09:59.9601822Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2024-12-18T01:09:59.9602078Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-12-18T01:09:59.9602326Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-12-18T01:09:59.9602585Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-12-18T01:09:59.9602707Z mapping to the flattened ``output``. 2024-12-18T01:09:59.9602923Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-12-18T01:09:59.9603194Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2024-12-18T01:09:59.9603295Z should be `None`. 2024-12-18T01:09:59.9603544Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-12-18T01:09:59.9603766Z in. In this case, even if `out_placements` is not `None`, the result function 2024-12-18T01:09:59.9604033Z should ignore the desired placements because the function is not running with 2024-12-18T01:09:59.9604131Z :class:`DTensor` s. 2024-12-18T01:09:59.9604312Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-12-18T01:09:59.9604589Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2024-12-18T01:09:59.9604822Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2024-12-18T01:09:59.9605065Z placements of each :class:`DTensor` argument is the same as the required 2024-12-18T01:09:59.9605249Z placements or not. If the placements are not the same and 2024-12-18T01:09:59.9605510Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-12-18T01:09:59.9605756Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2024-12-18T01:09:59.9606031Z the required sharding placements before passing its local tensor to ``func``. 2024-12-18T01:09:59.9606256Z The only exception is when required placements are not ``None`` and the 2024-12-18T01:09:59.9606563Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-12-18T01:09:59.9606780Z will be skipped and the argument will be directly passed to ``func``. 2024-12-18T01:09:59.9607002Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-12-18T01:09:59.9607109Z Default: None 2024-12-18T01:09:59.9607250Z device_mesh (:class:`DeviceMesh`, optional): 2024-12-18T01:09:59.9607474Z the device mesh that all the :class:`DTensor` s are placed on. If not 2024-12-18T01:09:59.9607734Z specified, this will be inferred from the input :class:`DTensor` s' device 2024-12-18T01:09:59.9608009Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2024-12-18T01:09:59.9608121Z device mesh. Default: None. 2024-12-18T01:09:59.9608244Z redistribute_inputs (bool, optional): 2024-12-18T01:09:59.9608509Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2024-12-18T01:09:59.9608757Z their placements are different from the required input placements. If this 2024-12-18T01:09:59.9608994Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-12-18T01:09:59.9609134Z an exception will be raised. Default: False. 2024-12-18T01:09:59.9609231Z 2024-12-18T01:09:59.9609318Z Returns: 2024-12-18T01:09:59.9609575Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-12-18T01:09:59.9609829Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-12-18T01:09:59.9609913Z 2024-12-18T01:09:59.9610012Z Raises: 2024-12-18T01:09:59.9610263Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2024-12-18T01:09:59.9610517Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-12-18T01:09:59.9610623Z argument passed in. 2024-12-18T01:09:59.9610709Z 2024-12-18T01:09:59.9610963Z AssertionError: For any non-DTensor output, we require its corresponding 2024-12-18T01:09:59.9611224Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-12-18T01:09:59.9611345Z if this is not the case. 2024-12-18T01:09:59.9611429Z 2024-12-18T01:09:59.9611692Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-12-18T01:09:59.9611861Z a redistribution according to ``in_placements``. 2024-12-18T01:09:59.9611947Z 2024-12-18T01:09:59.9612053Z Example: 2024-12-18T01:09:59.9612172Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9612327Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-12-18T01:09:59.9612453Z >>> partial_sum_tensor = torch.mm(W, X) 2024-12-18T01:09:59.9612695Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-12-18T01:09:59.9612825Z >>> return reduced_tensor 2024-12-18T01:09:59.9612913Z >>> 2024-12-18T01:09:59.9613059Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-12-18T01:09:59.9613189Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-12-18T01:09:59.9613287Z >>> Y = torch.mm(W, X) 2024-12-18T01:09:59.9613492Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-12-18T01:09:59.9613679Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-12-18T01:09:59.9613781Z >>> 2024-12-18T01:09:59.9614053Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-12-18T01:09:59.9614194Z >>> local_mm_allreduce_forward = local_map( 2024-12-18T01:09:59.9614302Z >>> mm_allreduce_forward, 2024-12-18T01:09:59.9614420Z >>> out_placements=[Replicate()], 2024-12-18T01:09:59.9614586Z >>> in_placements=[col_wise, row_wise], 2024-12-18T01:09:59.9614722Z >>> device_mesh=device_mesh, 2024-12-18T01:09:59.9614820Z >>> ) 2024-12-18T01:09:59.9614908Z >>> 2024-12-18T01:09:59.9615177Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-12-18T01:09:59.9615430Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-12-18T01:09:59.9615759Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-12-18T01:09:59.9615860Z 2024-12-18T01:09:59.9616087Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:09:59.9616185Z 2024-12-18T01:09:59.9616464Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9616558Z 2024-12-18T01:09:59.9616655Z warnings.warn(msg) 2024-12-18T01:09:59.9616739Z 2024-12-18T01:09:59.9616960Z --- Parse Warning: 70 / 105 --- 2024-12-18T01:09:59.9618020Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2024-12-18T01:09:59.9618291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9618373Z 2024-12-18T01:09:59.9618665Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2024-12-18T01:09:59.9618908Z strategies for an operator when the tensor inputs and outputs are DTensor. 2024-12-18T01:09:59.9619161Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-12-18T01:09:59.9619414Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-12-18T01:09:59.9619689Z when users would like to overwrite default sharding strategies of existing operators. 2024-12-18T01:09:59.9619785Z 2024-12-18T01:09:59.9619872Z Args: 2024-12-18T01:09:59.9620016Z op (Union[OpOverload, List[OpOverload]]): 2024-12-18T01:09:59.9620208Z An op or a list of ops to register the customized sharding function. 2024-12-18T01:09:59.9620289Z 2024-12-18T01:09:59.9620387Z Returns: 2024-12-18T01:09:59.9620655Z A function decorator which can be used to wrap a function that defines the sharding 2024-12-18T01:09:59.9620938Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-12-18T01:09:59.9621212Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-12-18T01:09:59.9621527Z already implemented the operator. The customized sharding function takes the same inputs 2024-12-18T01:09:59.9621770Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-12-18T01:09:59.9622043Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-12-18T01:09:59.9622327Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-12-18T01:09:59.9622450Z corresponding intput placements. 2024-12-18T01:09:59.9622544Z 2024-12-18T01:09:59.9622632Z Example: 2024-12-18T01:09:59.9622751Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9622900Z >>> @register_sharding(aten._softmax.default) 2024-12-18T01:09:59.9623057Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-12-18T01:09:59.9623214Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-12-18T01:09:59.9623324Z >>> acceptable_shardings = [] 2024-12-18T01:09:59.9623418Z >>> 2024-12-18T01:09:59.9623598Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-12-18T01:09:59.9623739Z >>> acceptable_shardings.append(all_replicate) 2024-12-18T01:09:59.9623906Z >>> 2024-12-18T01:09:59.9624026Z >>> for sharding_dim in range(x.ndim): 2024-12-18T01:09:59.9624158Z >>> if sharding_dim != softmax_dim: 2024-12-18T01:09:59.9624259Z >>> all_sharded = ( 2024-12-18T01:09:59.9624385Z >>> [Shard(sharding_dim)], 2024-12-18T01:09:59.9624510Z >>> [Shard(sharding_dim), None, None], 2024-12-18T01:09:59.9624600Z >>> ) 2024-12-18T01:09:59.9624757Z >>> acceptable_shardings.append(all_sharded) 2024-12-18T01:09:59.9624842Z >>> 2024-12-18T01:09:59.9624997Z >>> return acceptable_shardings 2024-12-18T01:09:59.9625078Z 2024-12-18T01:09:59.9625273Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:09:59.9625393Z 2024-12-18T01:09:59.9625649Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9625744Z 2024-12-18T01:09:59.9625846Z warnings.warn(msg) 2024-12-18T01:09:59.9625930Z 2024-12-18T01:09:59.9626134Z --- Parse Warning: 71 / 105 --- 2024-12-18T01:09:59.9627140Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-12-18T01:09:59.9627414Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9627498Z 2024-12-18T01:09:59.9627893Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:09:59.9628214Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-12-18T01:09:59.9628393Z 2024-12-18T01:09:59.9628489Z Keyword Args: 2024-12-18T01:09:59.9628697Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:09:59.9629045Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-12-18T01:09:59.9629402Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-12-18T01:09:59.9629537Z as a placeholder. default: None. 2024-12-18T01:09:59.9629768Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:09:59.9630157Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:09:59.9630775Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-12-18T01:09:59.9630915Z input_kwarg_layouts (Dict[str, Placement]): 2024-12-18T01:09:59.9631300Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-12-18T01:09:59.9631398Z default: None 2024-12-18T01:09:59.9631572Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-12-18T01:09:59.9631940Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:09:59.9632102Z have the desired DTensor layouts. default: None. 2024-12-18T01:09:59.9632219Z use_local_output (bool, optional): 2024-12-18T01:09:59.9632589Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-12-18T01:09:59.9632679Z Returns: 2024-12-18T01:09:59.9632994Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-12-18T01:09:59.9633091Z 2024-12-18T01:09:59.9633189Z Example:: 2024-12-18T01:09:59.9633309Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:09:59.9633618Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-12-18T01:09:59.9633884Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:09:59.9633982Z >>> ... 2024-12-18T01:09:59.9634285Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:09:59.9634431Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:09:59.9634517Z >>> 2024-12-18T01:09:59.9634864Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-12-18T01:09:59.9635044Z >>> # and then redistributed to Replicated DTensor. 2024-12-18T01:09:59.9635148Z >>> parallelize_module( 2024-12-18T01:09:59.9635295Z >>> block, # this can be a submodule or module 2024-12-18T01:09:59.9635410Z >>> tp_mesh, 2024-12-18T01:09:59.9635526Z >>> parallelize_plan={ 2024-12-18T01:09:59.9635647Z >>> "attn": PrepareModuleInput( 2024-12-18T01:09:59.9635805Z >>> input_layouts=(Shard(0), None, None, ...), 2024-12-18T01:09:59.9635973Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-12-18T01:09:59.9636268Z >>> ), 2024-12-18T01:09:59.9636381Z >>> } 2024-12-18T01:09:59.9636466Z >>> ) 2024-12-18T01:09:59.9636561Z 2024-12-18T01:09:59.9636815Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9636898Z 2024-12-18T01:09:59.9637009Z warnings.warn(msg) 2024-12-18T01:09:59.9637091Z 2024-12-18T01:09:59.9637326Z --- Parse Warning: 72 / 105 --- 2024-12-18T01:09:59.9638337Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-12-18T01:09:59.9638643Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9638761Z 2024-12-18T01:09:59.9639203Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:09:59.9639545Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-12-18T01:09:59.9639628Z 2024-12-18T01:09:59.9639731Z Keyword Args: 2024-12-18T01:09:59.9639901Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:09:59.9640253Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-12-18T01:09:59.9640633Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-12-18T01:09:59.9640779Z ``None`` need to be specified as a placeholder. 2024-12-18T01:09:59.9640991Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:09:59.9641379Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-12-18T01:09:59.9641508Z have the desired DTensor layouts. 2024-12-18T01:09:59.9641624Z use_local_output (bool, optional): 2024-12-18T01:09:59.9641993Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-12-18T01:09:59.9642082Z Returns: 2024-12-18T01:09:59.9642376Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-12-18T01:09:59.9642478Z 2024-12-18T01:09:59.9642573Z Example:: 2024-12-18T01:09:59.9642691Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:09:59.9643005Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-12-18T01:09:59.9643209Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:09:59.9643420Z >>> ... 2024-12-18T01:09:59.9643725Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:09:59.9643869Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:09:59.9643956Z >>> 2024-12-18T01:09:59.9644361Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-12-18T01:09:59.9644498Z >>> # and then redistributed to Sharded DTensor. 2024-12-18T01:09:59.9644614Z >>> parallelize_module( 2024-12-18T01:09:59.9644794Z >>> block, # this can be a submodule or module 2024-12-18T01:09:59.9644885Z >>> tp_mesh, 2024-12-18T01:09:59.9645046Z >>> parallelize_plan = PrepareModuleOutput( 2024-12-18T01:09:59.9645203Z >>> output_layouts=Replicate(), 2024-12-18T01:09:59.9645338Z >>> desired_output_layouts=Shard(0) 2024-12-18T01:09:59.9645428Z >>> ) 2024-12-18T01:09:59.9645518Z >>> ) 2024-12-18T01:09:59.9645616Z 2024-12-18T01:09:59.9645870Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9645969Z 2024-12-18T01:09:59.9646068Z warnings.warn(msg) 2024-12-18T01:09:59.9646166Z 2024-12-18T01:09:59.9646373Z --- Parse Warning: 73 / 105 --- 2024-12-18T01:09:59.9647361Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-12-18T01:09:59.9647638Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9647724Z 2024-12-18T01:09:59.9647966Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-12-18T01:09:59.9648216Z distribution where all component are from different parameterizations of 2024-12-18T01:09:59.9648450Z the same distribution type. It is parameterized by a `Categorical` 2024-12-18T01:09:59.9648652Z "selecting distribution" (over `k` component) and a component 2024-12-18T01:09:59.9648859Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-12-18T01:09:59.9649036Z (equal to `[k]`) which indexes each (batch of) component. 2024-12-18T01:09:59.9649122Z 2024-12-18T01:09:59.9649232Z Examples:: 2024-12-18T01:09:59.9649331Z 2024-12-18T01:09:59.9649512Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:09:59.9649806Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-12-18T01:09:59.9649929Z >>> # weighted normal distributions 2024-12-18T01:09:59.9650066Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:09:59.9650217Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-12-18T01:09:59.9650352Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:09:59.9650440Z 2024-12-18T01:09:59.9650646Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-12-18T01:09:59.9650793Z >>> # weighted bivariate normal distributions 2024-12-18T01:09:59.9650913Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:09:59.9651042Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:09:59.9651175Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-12-18T01:09:59.9651312Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:09:59.9651396Z 2024-12-18T01:09:59.9651581Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-12-18T01:09:59.9651801Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-12-18T01:09:59.9651922Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-12-18T01:09:59.9652049Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:09:59.9652185Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-12-18T01:09:59.9652359Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:09:59.9652469Z 2024-12-18T01:09:59.9652557Z Args: 2024-12-18T01:09:59.9652773Z mixture_distribution: `torch.distributions.Categorical`-like 2024-12-18T01:09:59.9652961Z instance. Manages the probability of selecting component. 2024-12-18T01:09:59.9653143Z The number of categories must match the rightmost batch 2024-12-18T01:09:59.9653329Z dimension of the `component_distribution`. Must have either 2024-12-18T01:09:59.9653472Z scalar `batch_shape` or `batch_shape` matching 2024-12-18T01:09:59.9653649Z `component_distribution.batch_shape[:-1]` 2024-12-18T01:09:59.9653867Z component_distribution: `torch.distributions.Distribution`-like 2024-12-18T01:09:59.9654099Z instance. Right-most batch dimension indexes component. 2024-12-18T01:09:59.9654183Z 2024-12-18T01:09:59.9654444Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9654530Z 2024-12-18T01:09:59.9654629Z warnings.warn(msg) 2024-12-18T01:09:59.9654723Z 2024-12-18T01:09:59.9654932Z --- Parse Warning: 74 / 105 --- 2024-12-18T01:09:59.9655922Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-12-18T01:09:59.9656181Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9656278Z 2024-12-18T01:09:59.9656464Z Creates a RelaxedBernoulli distribution, parametrized by 2024-12-18T01:09:59.9656656Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-12-18T01:09:59.9656894Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-12-18T01:09:59.9657077Z so the values are in (0, 1), and has reparametrizable samples. 2024-12-18T01:09:59.9657178Z 2024-12-18T01:09:59.9657273Z Example:: 2024-12-18T01:09:59.9657360Z 2024-12-18T01:09:59.9657518Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:59.9657652Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-12-18T01:09:59.9657791Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-12-18T01:09:59.9657882Z >>> m.sample() 2024-12-18T01:09:59.9658014Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-12-18T01:09:59.9658098Z 2024-12-18T01:09:59.9658183Z Args: 2024-12-18T01:09:59.9658335Z temperature (Tensor): relaxation temperature 2024-12-18T01:09:59.9658510Z probs (Number, Tensor): the probability of sampling `1` 2024-12-18T01:09:59.9658686Z logits (Number, Tensor): the log-odds of sampling `1` 2024-12-18T01:09:59.9658768Z 2024-12-18T01:09:59.9659018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9659115Z 2024-12-18T01:09:59.9659215Z warnings.warn(msg) 2024-12-18T01:09:59.9659311Z 2024-12-18T01:09:59.9659496Z --- Parse Warning: 75 / 105 --- 2024-12-18T01:09:59.9660534Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-12-18T01:09:59.9660796Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9660878Z 2024-12-18T01:09:59.9661106Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-12-18T01:09:59.9661302Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-12-18T01:09:59.9661554Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-12-18T01:09:59.9661719Z its samples are on simplex, and are reparametrizable. 2024-12-18T01:09:59.9661814Z 2024-12-18T01:09:59.9661965Z Example:: 2024-12-18T01:09:59.9662048Z 2024-12-18T01:09:59.9662202Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:59.9662361Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-12-18T01:09:59.9662503Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-12-18T01:09:59.9662593Z >>> m.sample() 2024-12-18T01:09:59.9662726Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-12-18T01:09:59.9662809Z 2024-12-18T01:09:59.9662895Z Args: 2024-12-18T01:09:59.9663048Z temperature (Tensor): relaxation temperature 2024-12-18T01:09:59.9663193Z probs (Tensor): event probabilities 2024-12-18T01:09:59.9663395Z logits (Tensor): unnormalized log probability for each event 2024-12-18T01:09:59.9663508Z 2024-12-18T01:09:59.9663759Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9663854Z 2024-12-18T01:09:59.9663953Z warnings.warn(msg) 2024-12-18T01:09:59.9664050Z 2024-12-18T01:09:59.9664239Z --- Parse Warning: 76 / 105 --- 2024-12-18T01:09:59.9665240Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-12-18T01:09:59.9665511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9665701Z Return a new dict with new, potentially nested, key value pair 2024-12-18T01:09:59.9665798Z 2024-12-18T01:09:59.9665894Z >>> purchase = { 2024-12-18T01:09:59.9666003Z ... "name": "Alice", 2024-12-18T01:09:59.9666183Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:09:59.9666302Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:09:59.9666400Z ... } 2024-12-18T01:09:59.9666610Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2024-12-18T01:09:59.9666733Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:09:59.9666826Z 'name': 'Alice', 2024-12-18T01:09:59.9667004Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-12-18T01:09:59.9667088Z 2024-12-18T01:09:59.9667337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9667433Z 2024-12-18T01:09:59.9667531Z warnings.warn(msg) 2024-12-18T01:09:59.9667628Z 2024-12-18T01:09:59.9667818Z --- Parse Warning: 77 / 105 --- 2024-12-18T01:09:59.9668960Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2024-12-18T01:09:59.9669221Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9669374Z Update value in a (potentially) nested dictionary 2024-12-18T01:09:59.9669471Z 2024-12-18T01:09:59.9669557Z inputs: 2024-12-18T01:09:59.9669688Z d - dictionary on which to operate 2024-12-18T01:09:59.9669906Z keys - list or tuple giving the location of the value to be changed in d 2024-12-18T01:09:59.9670055Z func - function to operate on that value 2024-12-18T01:09:59.9670140Z 2024-12-18T01:09:59.9670334Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-12-18T01:09:59.9670582Z original dictionary with v replaced by func(v), but does not mutate the 2024-12-18T01:09:59.9670683Z original dictionary. 2024-12-18T01:09:59.9670778Z 2024-12-18T01:09:59.9670987Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-12-18T01:09:59.9671203Z specified by the keys, with the innermost value set to func(default). 2024-12-18T01:09:59.9671359Z 2024-12-18T01:09:59.9671461Z >>> inc = lambda x: x + 1 2024-12-18T01:09:59.9671582Z >>> update_in({"a": 0}, ["a"], inc) 2024-12-18T01:09:59.9671669Z {'a': 1} 2024-12-18T01:09:59.9671750Z 2024-12-18T01:09:59.9671859Z >>> transaction = { 2024-12-18T01:09:59.9671955Z ... "name": "Alice", 2024-12-18T01:09:59.9672160Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:09:59.9672279Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:09:59.9672377Z ... } 2024-12-18T01:09:59.9672622Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2024-12-18T01:09:59.9672733Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:09:59.9672866Z 'name': 'Alice', 2024-12-18T01:09:59.9673034Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-12-18T01:09:59.9673134Z 2024-12-18T01:09:59.9673258Z >>> # updating a value when k0 is not in d 2024-12-18T01:09:59.9673403Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-12-18T01:09:59.9673496Z {1: {2: {3: 'bar'}}} 2024-12-18T01:09:59.9673615Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2024-12-18T01:09:59.9673722Z {1: 'foo', 2: {3: {4: 1}}} 2024-12-18T01:09:59.9673806Z 2024-12-18T01:09:59.9674070Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9674153Z 2024-12-18T01:09:59.9674252Z warnings.warn(msg) 2024-12-18T01:09:59.9674345Z 2024-12-18T01:09:59.9674539Z --- Parse Warning: 78 / 105 --- 2024-12-18T01:09:59.9675538Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2024-12-18T01:09:59.9675800Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9675983Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-12-18T01:09:59.9676066Z 2024-12-18T01:09:59.9676248Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-12-18T01:09:59.9676465Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-12-18T01:09:59.9676547Z 2024-12-18T01:09:59.9676764Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-12-18T01:09:59.9676897Z structures such as dictionaries and lists. 2024-12-18T01:09:59.9676992Z 2024-12-18T01:09:59.9677091Z >>> transaction = { 2024-12-18T01:09:59.9677189Z ... "name": "Alice", 2024-12-18T01:09:59.9677395Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:09:59.9677516Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:09:59.9677616Z ... } 2024-12-18T01:09:59.9677759Z >>> get_in(["purchase", "items", 0], transaction) 2024-12-18T01:09:59.9677848Z 'Apple' 2024-12-18T01:09:59.9677974Z >>> get_in(["name"], transaction) 2024-12-18T01:09:59.9678062Z 'Alice' 2024-12-18T01:09:59.9678208Z >>> get_in(["purchase", "total"], transaction) 2024-12-18T01:09:59.9678359Z >>> get_in(["purchase", "items", "apple"], transaction) 2024-12-18T01:09:59.9678509Z >>> get_in(["purchase", "items", 10], transaction) 2024-12-18T01:09:59.9678649Z >>> get_in(["purchase", "total"], transaction, 0) 2024-12-18T01:09:59.9678738Z 0 2024-12-18T01:09:59.9678867Z >>> get_in(["y"], {}, no_default=True) 2024-12-18T01:09:59.9678984Z Traceback (most recent call last): 2024-12-18T01:09:59.9679084Z ... 2024-12-18T01:09:59.9679180Z KeyError: 'y' 2024-12-18T01:09:59.9679265Z 2024-12-18T01:09:59.9679369Z See Also: 2024-12-18T01:09:59.9679466Z itertoolz.get 2024-12-18T01:09:59.9679613Z operator.getitem 2024-12-18T01:09:59.9679726Z 2024-12-18T01:09:59.9679980Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9680079Z 2024-12-18T01:09:59.9680178Z warnings.warn(msg) 2024-12-18T01:09:59.9680281Z 2024-12-18T01:09:59.9680469Z --- Parse Warning: 79 / 105 --- 2024-12-18T01:09:59.9681483Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2024-12-18T01:09:59.9681770Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9681913Z Group a collection by a key function 2024-12-18T01:09:59.9682011Z 2024-12-18T01:09:59.9682177Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2024-12-18T01:09:59.9682315Z >>> groupby(len, names) # doctest: +SKIP 2024-12-18T01:09:59.9682483Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-12-18T01:09:59.9682581Z 2024-12-18T01:09:59.9682691Z >>> iseven = lambda x: x % 2 == 0 2024-12-18T01:09:59.9682861Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-12-18T01:09:59.9682986Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-12-18T01:09:59.9683067Z 2024-12-18T01:09:59.9683226Z Non-callable keys imply grouping on a member. 2024-12-18T01:09:59.9683308Z 2024-12-18T01:09:59.9683397Z >>> groupby( 2024-12-18T01:09:59.9683499Z ... "gender", 2024-12-18T01:09:59.9683585Z ... [ 2024-12-18T01:09:59.9683717Z ... {"name": "Alice", "gender": "F"}, 2024-12-18T01:09:59.9683835Z ... {"name": "Bob", "gender": "M"}, 2024-12-18T01:09:59.9683968Z ... {"name": "Charlie", "gender": "M"}, 2024-12-18T01:09:59.9684053Z ... ], 2024-12-18T01:09:59.9684154Z ... ) # doctest:+SKIP 2024-12-18T01:09:59.9684282Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-12-18T01:09:59.9684392Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-12-18T01:09:59.9684518Z {'gender': 'M', 'name': 'Charlie'}]} 2024-12-18T01:09:59.9684601Z 2024-12-18T01:09:59.9684744Z Not to be confused with ``itertools.groupby`` 2024-12-18T01:09:59.9684839Z 2024-12-18T01:09:59.9684928Z See Also: 2024-12-18T01:09:59.9685026Z countby 2024-12-18T01:09:59.9685110Z 2024-12-18T01:09:59.9685363Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9685457Z 2024-12-18T01:09:59.9685553Z warnings.warn(msg) 2024-12-18T01:09:59.9685648Z 2024-12-18T01:09:59.9685835Z --- Parse Warning: 80 / 105 --- 2024-12-18T01:09:59.9686743Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-12-18T01:09:59.9687009Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9687186Z Applies Batch Normalization over a N-Dimensional input. 2024-12-18T01:09:59.9687282Z 2024-12-18T01:09:59.9687625Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-12-18T01:09:59.9687866Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-12-18T01:09:59.9688083Z Internal Covariate Shift `__ . 2024-12-18T01:09:59.9688177Z 2024-12-18T01:09:59.9688273Z .. math:: 2024-12-18T01:09:59.9688355Z 2024-12-18T01:09:59.9688589Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-12-18T01:09:59.9688671Z 2024-12-18T01:09:59.9688944Z The mean and standard-deviation are calculated per-dimension over all 2024-12-18T01:09:59.9689200Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-12-18T01:09:59.9689454Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-12-18T01:09:59.9689633Z By default, the elements of :math:`\gamma` are sampled from 2024-12-18T01:09:59.9689839Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-12-18T01:09:59.9690108Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-12-18T01:09:59.9690271Z `torch.var(input, unbiased=False)`. 2024-12-18T01:09:59.9690369Z 2024-12-18T01:09:59.9690602Z Also by default, during training this layer keeps running estimates of its 2024-12-18T01:09:59.9690942Z computed mean and variance, which are then used for normalization during 2024-12-18T01:09:59.9691186Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-12-18T01:09:59.9691280Z of 0.1. 2024-12-18T01:09:59.9691380Z 2024-12-18T01:09:59.9691608Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-12-18T01:09:59.9691844Z keep running estimates, and batch statistics are instead used during 2024-12-18T01:09:59.9691956Z evaluation time as well. 2024-12-18T01:09:59.9692041Z 2024-12-18T01:09:59.9692152Z .. note:: 2024-12-18T01:09:59.9692374Z This :attr:`momentum` argument is different from one used in optimizer 2024-12-18T01:09:59.9692617Z classes and the conventional notion of momentum. Mathematically, the 2024-12-18T01:09:59.9692755Z update rule for running statistics here is 2024-12-18T01:09:59.9693039Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-12-18T01:09:59.9693245Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-12-18T01:09:59.9693350Z new observed value. 2024-12-18T01:09:59.9693445Z 2024-12-18T01:09:59.9693744Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-12-18T01:09:59.9694006Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-12-18T01:09:59.9694183Z Normalization or Spatio-temporal Batch Normalization. 2024-12-18T01:09:59.9694280Z 2024-12-18T01:09:59.9694426Z Currently :class:`SyncBatchNorm` only supports 2024-12-18T01:09:59.9694710Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-12-18T01:09:59.9694935Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-12-18T01:09:59.9695144Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-12-18T01:09:59.9695254Z Network with DDP. 2024-12-18T01:09:59.9695338Z 2024-12-18T01:09:59.9695437Z Args: 2024-12-18T01:09:59.9695608Z num_features: :math:`C` from an expected input of size 2024-12-18T01:09:59.9695706Z :math:`(N, C, +)` 2024-12-18T01:09:59.9695907Z eps: a value added to the denominator for numerical stability. 2024-12-18T01:09:59.9696006Z Default: ``1e-5`` 2024-12-18T01:09:59.9696211Z momentum: the value used for the running_mean and running_var 2024-12-18T01:09:59.9696419Z computation. Can be set to ``None`` for cumulative moving average 2024-12-18T01:09:59.9696540Z (i.e. simple average). Default: 0.1 2024-12-18T01:09:59.9696758Z affine: a boolean value that when set to ``True``, this module has 2024-12-18T01:09:59.9696911Z learnable affine parameters. Default: ``True`` 2024-12-18T01:09:59.9697139Z track_running_stats: a boolean value that when set to ``True``, this 2024-12-18T01:09:59.9697370Z module tracks the running mean and variance, and when set to ``False``, 2024-12-18T01:09:59.9697659Z this module does not track such statistics, and initializes statistics 2024-12-18T01:09:59.9697863Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-12-18T01:09:59.9698106Z When these buffers are ``None``, this module always uses batch statistics. 2024-12-18T01:09:59.9698258Z in both training and eval modes. Default: ``True`` 2024-12-18T01:09:59.9698508Z process_group: synchronization of stats happen within each process group 2024-12-18T01:09:59.9698775Z individually. Default behavior is synchronization across the whole 2024-12-18T01:09:59.9698864Z world 2024-12-18T01:09:59.9698962Z 2024-12-18T01:09:59.9699051Z Shape: 2024-12-18T01:09:59.9699188Z - Input: :math:`(N, C, +)` 2024-12-18T01:09:59.9699351Z - Output: :math:`(N, C, +)` (same shape as input) 2024-12-18T01:09:59.9699436Z 2024-12-18T01:09:59.9699542Z .. note:: 2024-12-18T01:09:59.9699787Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-12-18T01:09:59.9700005Z synchronization is disabled when ``model.eval()`` is set or if 2024-12-18T01:09:59.9700133Z ``self.training`` is otherwise ``False``. 2024-12-18T01:09:59.9700215Z 2024-12-18T01:09:59.9700321Z Examples:: 2024-12-18T01:09:59.9700403Z 2024-12-18T01:09:59.9700519Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9700659Z >>> # With Learnable Parameters 2024-12-18T01:09:59.9700813Z >>> m = nn.SyncBatchNorm(100) 2024-12-18T01:09:59.9700975Z >>> # creating process group (optional) 2024-12-18T01:09:59.9701117Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:09:59.9701240Z >>> ranks = list(range(8)) 2024-12-18T01:09:59.9701349Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:09:59.9701513Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:09:59.9701669Z >>> # process group created, even if that rank is not 2024-12-18T01:09:59.9701773Z >>> # part of the group. 2024-12-18T01:09:59.9702035Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:09:59.9702237Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:09:59.9702372Z >>> # Without Learnable Parameters 2024-12-18T01:09:59.9702579Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-12-18T01:09:59.9702723Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-12-18T01:09:59.9702828Z >>> output = m(input) 2024-12-18T01:09:59.9702912Z 2024-12-18T01:09:59.9703048Z >>> # network is nn.BatchNorm layer 2024-12-18T01:09:59.9703322Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-12-18T01:09:59.9703502Z >>> # only single gpu per process is currently supported 2024-12-18T01:09:59.9703719Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:09:59.9703846Z >>> sync_bn_network, 2024-12-18T01:09:59.9703979Z >>> device_ids=[args.local_rank], 2024-12-18T01:09:59.9704109Z >>> output_device=args.local_rank) 2024-12-18T01:09:59.9704205Z 2024-12-18T01:09:59.9704456Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9704553Z 2024-12-18T01:09:59.9704653Z warnings.warn(msg) 2024-12-18T01:09:59.9704735Z 2024-12-18T01:09:59.9704965Z --- Parse Warning: 81 / 105 --- 2024-12-18T01:09:59.9705973Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-12-18T01:09:59.9706317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9706625Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-12-18T01:09:59.9706723Z 2024-12-18T01:09:59.9706813Z Args: 2024-12-18T01:09:59.9707058Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-12-18T01:09:59.9707297Z process_group (optional): process group to scope synchronization, 2024-12-18T01:09:59.9707438Z default is the whole world 2024-12-18T01:09:59.9707538Z 2024-12-18T01:09:59.9707628Z Returns: 2024-12-18T01:09:59.9707921Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-12-18T01:09:59.9708138Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-12-18T01:09:59.9708431Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-12-18T01:09:59.9708540Z instead. 2024-12-18T01:09:59.9708628Z 2024-12-18T01:09:59.9708741Z Example:: 2024-12-18T01:09:59.9708826Z 2024-12-18T01:09:59.9708966Z >>> # Network with nn.BatchNorm layer 2024-12-18T01:09:59.9709109Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:59.9709231Z >>> module = torch.nn.Sequential( 2024-12-18T01:09:59.9709369Z >>> torch.nn.Linear(20, 100), 2024-12-18T01:09:59.9709497Z >>> torch.nn.BatchNorm1d(100), 2024-12-18T01:09:59.9709609Z >>> ).cuda() 2024-12-18T01:09:59.9709740Z >>> # creating process group (optional) 2024-12-18T01:09:59.9709889Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:09:59.9710011Z >>> ranks = list(range(8)) 2024-12-18T01:09:59.9710128Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:09:59.9710297Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:09:59.9710452Z >>> # process group created, even if that rank is not 2024-12-18T01:09:59.9710573Z >>> # part of the group. 2024-12-18T01:09:59.9710698Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:09:59.9710950Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:09:59.9711172Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:09:59.9711571Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-12-18T01:09:59.9711685Z 2024-12-18T01:09:59.9711774Z 2024-12-18T01:09:59.9712045Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9712132Z 2024-12-18T01:09:59.9712233Z warnings.warn(msg) 2024-12-18T01:09:59.9712334Z 2024-12-18T01:09:59.9712549Z --- Parse Warning: 82 / 105 --- 2024-12-18T01:09:59.9713428Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-12-18T01:09:59.9713693Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9713791Z 2024-12-18T01:09:59.9714104Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-12-18T01:09:59.9714185Z 2024-12-18T01:09:59.9714469Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-12-18T01:09:59.9714693Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-12-18T01:09:59.9714843Z 2024-12-18T01:09:59.9715181Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-12-18T01:09:59.9715448Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-12-18T01:09:59.9715616Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-12-18T01:09:59.9715699Z 2024-12-18T01:09:59.9715798Z Shape: 2024-12-18T01:09:59.9716013Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-12-18T01:09:59.9716299Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-12-18T01:09:59.9716517Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-12-18T01:09:59.9716685Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-12-18T01:09:59.9716768Z 2024-12-18T01:09:59.9716856Z Args: 2024-12-18T01:09:59.9717017Z dim (Union[int, str]): Dimension to be unflattened 2024-12-18T01:09:59.9717363Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-12-18T01:09:59.9717461Z 2024-12-18T01:09:59.9717550Z Examples: 2024-12-18T01:09:59.9717662Z >>> input = torch.randn(2, 50) 2024-12-18T01:09:59.9717774Z >>> # With tuple of ints 2024-12-18T01:09:59.9717873Z >>> m = nn.Sequential( 2024-12-18T01:09:59.9717983Z >>> nn.Linear(50, 50), 2024-12-18T01:09:59.9718090Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-12-18T01:09:59.9718187Z >>> ) 2024-12-18T01:09:59.9718286Z >>> output = m(input) 2024-12-18T01:09:59.9718388Z >>> output.size() 2024-12-18T01:09:59.9718502Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:09:59.9718602Z >>> # With torch.Size 2024-12-18T01:09:59.9718717Z >>> m = nn.Sequential( 2024-12-18T01:09:59.9718818Z >>> nn.Linear(50, 50), 2024-12-18T01:09:59.9718945Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-12-18T01:09:59.9719047Z >>> ) 2024-12-18T01:09:59.9719147Z >>> output = m(input) 2024-12-18T01:09:59.9719255Z >>> output.size() 2024-12-18T01:09:59.9719353Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:09:59.9719476Z >>> # With namedshape (tuple of tuples) 2024-12-18T01:09:59.9719639Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-12-18T01:09:59.9719843Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-12-18T01:09:59.9719965Z >>> output = unflatten(input) 2024-12-18T01:09:59.9720061Z >>> output.size() 2024-12-18T01:09:59.9720173Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:09:59.9720255Z 2024-12-18T01:09:59.9720509Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9720603Z 2024-12-18T01:09:59.9720702Z warnings.warn(msg) 2024-12-18T01:09:59.9720797Z 2024-12-18T01:09:59.9720992Z --- Parse Warning: 83 / 105 --- 2024-12-18T01:09:59.9721968Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1698. 2024-12-18T01:09:59.9722331Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9722534Z Creates a criterion that measures the triplet loss given input 2024-12-18T01:09:59.9722746Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-12-18T01:09:59.9722968Z positive, and negative examples, respectively), and a nonnegative, 2024-12-18T01:09:59.9723234Z real-valued function ("distance function") used to compute the relationship 2024-12-18T01:09:59.9723458Z between the anchor and positive example ("positive distance") and the 2024-12-18T01:09:59.9723627Z anchor and negative example ("negative distance"). 2024-12-18T01:09:59.9723800Z 2024-12-18T01:09:59.9724007Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-12-18T01:09:59.9724123Z can be described as: 2024-12-18T01:09:59.9724211Z 2024-12-18T01:09:59.9724322Z .. math:: 2024-12-18T01:09:59.9724462Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-12-18T01:09:59.9724619Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-12-18T01:09:59.9724716Z 2024-12-18T01:09:59.9724963Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-12-18T01:09:59.9725295Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-12-18T01:09:59.9725561Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-12-18T01:09:59.9725822Z between the positive and negative distances that is required for the loss to 2024-12-18T01:09:59.9726048Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-12-18T01:09:59.9726175Z that the distance function can handle. 2024-12-18T01:09:59.9726269Z 2024-12-18T01:09:59.9726387Z If :attr:`reduction` is not ``'none'`` 2024-12-18T01:09:59.9726504Z (default ``'mean'``), then: 2024-12-18T01:09:59.9726586Z 2024-12-18T01:09:59.9726688Z .. math:: 2024-12-18T01:09:59.9726781Z \ell(x, y) = 2024-12-18T01:09:59.9726874Z \begin{cases} 2024-12-18T01:09:59.9727090Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-12-18T01:09:59.9727288Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-12-18T01:09:59.9727393Z \end{cases} 2024-12-18T01:09:59.9727475Z 2024-12-18T01:09:59.9727712Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-12-18T01:09:59.9727974Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-12-18T01:09:59.9728062Z 2024-12-18T01:09:59.9728163Z Args: 2024-12-18T01:09:59.9728432Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-12-18T01:09:59.9728636Z quantifies the closeness of two tensors. If not specified, 2024-12-18T01:09:59.9728808Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-12-18T01:09:59.9729080Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-12-18T01:09:59.9729363Z between the positive and negative distances required for the loss to be 0. Larger 2024-12-18T01:09:59.9729639Z margins penalize cases where the negative examples are not distant enough from the 2024-12-18T01:09:59.9729829Z anchors, relative to the positives. Default: :math:`1`. 2024-12-18T01:09:59.9730076Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-12-18T01:09:59.9730350Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-12-18T01:09:59.9730868Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-12-18T01:09:59.9731152Z negative example than the anchor is, swaps the positive example and the anchor in 2024-12-18T01:09:59.9731285Z the loss computation. Default: ``False``. 2024-12-18T01:09:59.9731563Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-12-18T01:09:59.9731764Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-12-18T01:09:59.9731946Z ``'mean'``: the sum of the output will be divided by the number of 2024-12-18T01:09:59.9732197Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-12-18T01:09:59.9732279Z 2024-12-18T01:09:59.9732376Z 2024-12-18T01:09:59.9732533Z Shape: 2024-12-18T01:09:59.9732845Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-12-18T01:09:59.9733037Z as supported by the distance function. 2024-12-18T01:09:59.9733353Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-12-18T01:09:59.9733462Z otherwise. 2024-12-18T01:09:59.9733544Z 2024-12-18T01:09:59.9733642Z Examples:: 2024-12-18T01:09:59.9733738Z 2024-12-18T01:09:59.9733844Z >>> # Initialize embeddings 2024-12-18T01:09:59.9734016Z >>> embedding = nn.Embedding(1000, 128) 2024-12-18T01:09:59.9734145Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:09:59.9734321Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:09:59.9734451Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:09:59.9734567Z >>> anchor = embedding(anchor_ids) 2024-12-18T01:09:59.9734709Z >>> positive = embedding(positive_ids) 2024-12-18T01:09:59.9734828Z >>> negative = embedding(negative_ids) 2024-12-18T01:09:59.9734931Z >>> 2024-12-18T01:09:59.9735041Z >>> # Built-in Distance Function 2024-12-18T01:09:59.9735142Z >>> triplet_loss = \ 2024-12-18T01:09:59.9735435Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-12-18T01:09:59.9735591Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:09:59.9735707Z >>> output.backward() 2024-12-18T01:09:59.9735792Z >>> 2024-12-18T01:09:59.9735915Z >>> # Custom Distance Function 2024-12-18T01:09:59.9736017Z >>> def l_infinity(x1, x2): 2024-12-18T01:09:59.9736390Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-12-18T01:09:59.9741524Z >>> 2024-12-18T01:09:59.9741771Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-12-18T01:09:59.9741898Z >>> triplet_loss = ( 2024-12-18T01:09:59.9742194Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-12-18T01:09:59.9742354Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:09:59.9742474Z >>> output.backward() 2024-12-18T01:09:59.9742562Z >>> 2024-12-18T01:09:59.9742698Z >>> # Custom Distance Function (Lambda) 2024-12-18T01:09:59.9742800Z >>> triplet_loss = ( 2024-12-18T01:09:59.9742934Z >>> nn.TripletMarginWithDistanceLoss( 2024-12-18T01:09:59.9743172Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-12-18T01:09:59.9743331Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:09:59.9743446Z >>> output.backward() 2024-12-18T01:09:59.9743532Z 2024-12-18T01:09:59.9743638Z Reference: 2024-12-18T01:09:59.9743942Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-12-18T01:09:59.9744171Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2024-12-18T01:09:59.9744271Z 2024-12-18T01:09:59.9744527Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-12-18T01:09:59.9744625Z 2024-12-18T01:09:59.9744726Z warnings.warn(msg) 2024-12-18T01:09:59.9744811Z 2024-12-18T01:09:59.9745077Z --- Parse Warning: 84 / 105 --- 2024-12-18T01:09:59.9745968Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-12-18T01:09:59.9746245Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9746408Z Computes a partial inverse of :class:`MaxPool2d`. 2024-12-18T01:09:59.9746507Z 2024-12-18T01:09:59.9746772Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-12-18T01:09:59.9747035Z 2024-12-18T01:09:59.9747279Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-12-18T01:09:59.9747519Z including the indices of the maximal values and computes a partial inverse 2024-12-18T01:09:59.9747683Z in which all non-maximal values are set to zero. 2024-12-18T01:09:59.9747768Z 2024-12-18T01:09:59.9747871Z Note: 2024-12-18T01:09:59.9748187Z This operation may behave nondeterministically when the input indices has repeat values. 2024-12-18T01:09:59.9748715Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-12-18T01:09:59.9748818Z 2024-12-18T01:09:59.9749108Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-12-18T01:09:59.9749300Z sizes. Hence, the inversion process can get ambiguous. 2024-12-18T01:09:59.9749495Z To accommodate this, you can provide the needed output size 2024-12-18T01:09:59.9749724Z as an additional argument :attr:`output_size` in the forward call. 2024-12-18T01:09:59.9749847Z See the Inputs and Example below. 2024-12-18T01:09:59.9749933Z 2024-12-18T01:09:59.9750039Z Args: 2024-12-18T01:09:59.9750222Z kernel_size (int or tuple): Size of the max pooling window. 2024-12-18T01:09:59.9750406Z stride (int or tuple): Stride of the max pooling window. 2024-12-18T01:09:59.9750541Z It is set to :attr:`kernel_size` by default. 2024-12-18T01:09:59.9750738Z padding (int or tuple): Padding that was added to the input 2024-12-18T01:09:59.9750824Z 2024-12-18T01:09:59.9750913Z Inputs: 2024-12-18T01:09:59.9751053Z - `input`: the input Tensor to invert 2024-12-18T01:09:59.9751257Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-12-18T01:09:59.9751429Z - `output_size` (optional): the targeted output size 2024-12-18T01:09:59.9751514Z 2024-12-18T01:09:59.9751602Z Shape: 2024-12-18T01:09:59.9751800Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-12-18T01:09:59.9752013Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-12-18T01:09:59.9752107Z 2024-12-18T01:09:59.9752199Z .. math:: 2024-12-18T01:09:59.9752483Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-12-18T01:09:59.9752565Z 2024-12-18T01:09:59.9752657Z .. math:: 2024-12-18T01:09:59.9752929Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-12-18T01:09:59.9753010Z 2024-12-18T01:09:59.9753191Z or as given by :attr:`output_size` in the call operator 2024-12-18T01:09:59.9753275Z 2024-12-18T01:09:59.9753371Z Example:: 2024-12-18T01:09:59.9753468Z 2024-12-18T01:09:59.9753633Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-12-18T01:09:59.9753773Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-12-18T01:09:59.9753907Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-12-18T01:09:59.9754031Z [ 5., 6., 7., 8.], 2024-12-18T01:09:59.9754141Z [ 9., 10., 11., 12.], 2024-12-18T01:09:59.9754254Z [13., 14., 15., 16.]]]]) 2024-12-18T01:09:59.9754389Z >>> output, indices = pool(input) 2024-12-18T01:09:59.9754497Z >>> unpool(output, indices) 2024-12-18T01:09:59.9754620Z tensor([[[[ 0., 0., 0., 0.], 2024-12-18T01:09:59.9754723Z [ 0., 6., 0., 8.], 2024-12-18T01:09:59.9754838Z [ 0., 0., 0., 0.], 2024-12-18T01:09:59.9754971Z [ 0., 14., 0., 16.]]]]) 2024-12-18T01:09:59.9755211Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-12-18T01:09:59.9755370Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-12-18T01:09:59.9755484Z [ 6., 7., 8., 9., 10.], 2024-12-18T01:09:59.9755609Z [11., 12., 13., 14., 15.], 2024-12-18T01:09:59.9755723Z [16., 17., 18., 19., 20.]]]]) 2024-12-18T01:09:59.9755854Z >>> output, indices = pool(input) 2024-12-18T01:09:59.9756055Z >>> # This call will not work without specifying output_size 2024-12-18T01:09:59.9756216Z >>> unpool(output, indices, output_size=input.size()) 2024-12-18T01:09:59.9756360Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-12-18T01:09:59.9756461Z [ 0., 7., 0., 9., 0.], 2024-12-18T01:09:59.9756574Z [ 0., 0., 0., 0., 0.], 2024-12-18T01:09:59.9756681Z [ 0., 17., 0., 19., 0.]]]]) 2024-12-18T01:09:59.9756766Z 2024-12-18T01:09:59.9756864Z 2024-12-18T01:09:59.9756950Z 2024-12-18T01:09:59.9757217Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9757300Z 2024-12-18T01:09:59.9757414Z warnings.warn(msg) 2024-12-18T01:09:59.9757498Z 2024-12-18T01:09:59.9757715Z --- Parse Warning: 85 / 105 --- 2024-12-18T01:09:59.9758617Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-12-18T01:09:59.9758882Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9759211Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-12-18T01:09:59.9759300Z 2024-12-18T01:09:59.9759633Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-12-18T01:09:59.9759743Z and with 2D inputs, this class 2024-12-18T01:09:59.9759827Z 2024-12-18T01:09:59.9760147Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-12-18T01:09:59.9760461Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-12-18T01:09:59.9760771Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-12-18T01:09:59.9760855Z 2024-12-18T01:09:59.9761225Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-12-18T01:09:59.9761320Z operations. 2024-12-18T01:09:59.9761403Z 2024-12-18T01:09:59.9761674Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-12-18T01:09:59.9761916Z pass. This scales the output of the Embedding before performing a weighted 2024-12-18T01:09:59.9762179Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-12-18T01:09:59.9762414Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-12-18T01:09:59.9762536Z :attr:`per_sample_weights`. 2024-12-18T01:09:59.9762620Z 2024-12-18T01:09:59.9762708Z Args: 2024-12-18T01:09:59.9762907Z num_embeddings (int): size of the dictionary of embeddings 2024-12-18T01:09:59.9763082Z embedding_dim (int): the size of each embedding vector 2024-12-18T01:09:59.9763410Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-12-18T01:09:59.9763566Z is renormalized to have norm :attr:`max_norm`. 2024-12-18T01:09:59.9764369Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-12-18T01:09:59.9764699Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-12-18T01:09:59.9764864Z the words in the mini-batch. Default ``False``. 2024-12-18T01:09:59.9765067Z Note: this option is not supported when ``mode="max"``. 2024-12-18T01:09:59.9765349Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-12-18T01:09:59.9765588Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-12-18T01:09:59.9765850Z into consideration. ``"mean"`` computes the average of the values 2024-12-18T01:09:59.9766044Z in the bag, ``"max"`` computes the max value over each bag. 2024-12-18T01:09:59.9766167Z Default: ``"mean"`` 2024-12-18T01:09:59.9766502Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-12-18T01:09:59.9766758Z Notes for more details regarding sparse gradients. Note: this option is not 2024-12-18T01:09:59.9766890Z supported when ``mode="max"``. 2024-12-18T01:09:59.9767277Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-12-18T01:09:59.9767502Z is equivalent to the size of `indices`. This matches the CSR format. 2024-12-18T01:09:59.9767845Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-12-18T01:09:59.9768121Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-12-18T01:09:59.9768384Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-12-18T01:09:59.9768655Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-12-18T01:09:59.9768915Z zeros, but can be updated to another value to be used as the padding vector. 2024-12-18T01:09:59.9769164Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-12-18T01:09:59.9769288Z reduction. 2024-12-18T01:09:59.9769371Z 2024-12-18T01:09:59.9769462Z Attributes: 2024-12-18T01:09:59.9769793Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-12-18T01:09:59.9769936Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-12-18T01:09:59.9770036Z 2024-12-18T01:09:59.9770132Z Examples:: 2024-12-18T01:09:59.9770215Z 2024-12-18T01:09:59.9770398Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-12-18T01:09:59.9770560Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-12-18T01:09:59.9770700Z >>> # a batch of 2 samples of 4 indices each 2024-12-18T01:09:59.9770890Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:09:59.9771058Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:09:59.9771203Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:09:59.9771322Z >>> embedding_sum(input, offsets) 2024-12-18T01:09:59.9771452Z tensor([[-0.8861, -5.4350, -0.0523], 2024-12-18T01:09:59.9771561Z [ 1.1306, -2.5798, -1.0044]]) 2024-12-18T01:09:59.9771659Z 2024-12-18T01:09:59.9771830Z >>> # Example with padding_idx 2024-12-18T01:09:59.9772039Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-12-18T01:09:59.9772238Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:09:59.9772388Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:09:59.9772515Z >>> embedding_sum(input, offsets) 2024-12-18T01:09:59.9772623Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-12-18T01:09:59.9772741Z [-0.7082, 3.2145, -2.6251]]) 2024-12-18T01:09:59.9772824Z 2024-12-18T01:09:59.9773029Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-12-18T01:09:59.9773193Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-12-18T01:09:59.9773379Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-12-18T01:09:59.9773500Z embedding.weight, 2024-12-18T01:09:59.9773632Z padding_idx=embedding.padding_idx, 2024-12-18T01:09:59.9773741Z mode='sum') 2024-12-18T01:09:59.9773826Z 2024-12-18T01:09:59.9774079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9774179Z 2024-12-18T01:09:59.9774279Z warnings.warn(msg) 2024-12-18T01:09:59.9774374Z 2024-12-18T01:09:59.9774585Z --- Parse Warning: 86 / 105 --- 2024-12-18T01:09:59.9775588Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-12-18T01:09:59.9775865Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9775950Z 2024-12-18T01:09:59.9776203Z Context manager for training with uneven inputs across processes in DDP. 2024-12-18T01:09:59.9776289Z 2024-12-18T01:09:59.9776531Z This context manager will keep track of already-joined DDP processes, 2024-12-18T01:09:59.9776742Z and "shadow" the forward and backward passes by inserting collective 2024-12-18T01:09:59.9776974Z communication operations to match with the ones created by non-joined 2024-12-18T01:09:59.9777220Z DDP processes. This will ensure each collective call has a corresponding 2024-12-18T01:09:59.9777439Z call by already-joined DDP processes, preventing hangs or errors that 2024-12-18T01:09:59.9777654Z would otherwise happen when training with uneven inputs across 2024-12-18T01:09:59.9777892Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-12-18T01:09:59.9778116Z specified to be ``True``, all trainers will throw an error once one rank 2024-12-18T01:09:59.9778316Z runs out of inputs, allowing these errors to be caught and handled 2024-12-18T01:09:59.9778429Z according to application logic. 2024-12-18T01:09:59.9778530Z 2024-12-18T01:09:59.9778752Z Once all DDP processes have joined, the context manager will broadcast 2024-12-18T01:09:59.9778991Z the model corresponding to the last joined process to all processes to 2024-12-18T01:09:59.9779141Z ensure the model is the same across all processes 2024-12-18T01:09:59.9779258Z (which is guaranteed by DDP). 2024-12-18T01:09:59.9779343Z 2024-12-18T01:09:59.9779560Z To use this to enable training with uneven inputs across processes, 2024-12-18T01:09:59.9779780Z simply wrap this context manager around your training loop. No further 2024-12-18T01:09:59.9779968Z modifications to the model or data loading is required. 2024-12-18T01:09:59.9780050Z 2024-12-18T01:09:59.9780146Z .. warning:: 2024-12-18T01:09:59.9780369Z If the model or training loop this context manager is wrapped around 2024-12-18T01:09:59.9780552Z has additional distributed collective operations, such as 2024-12-18T01:09:59.9780755Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-12-18T01:09:59.9781023Z ``throw_on_early_termination`` must be enabled. This is because this 2024-12-18T01:09:59.9781247Z context manager is not aware of non-DDP collective communication. 2024-12-18T01:09:59.9781424Z This flag will cause all ranks to throw when any one rank 2024-12-18T01:09:59.9781634Z exhausts inputs, allowing these errors to be caught and recovered 2024-12-18T01:09:59.9781750Z from across all ranks. 2024-12-18T01:09:59.9781834Z 2024-12-18T01:09:59.9781932Z Args: 2024-12-18T01:09:59.9782143Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-12-18T01:09:59.9782364Z gradients by the initial ``world_size`` DDP training was launched 2024-12-18T01:09:59.9782564Z with. If ``False``, will compute the effective world size 2024-12-18T01:09:59.9782752Z (number of ranks that have not depleted their inputs yet) and 2024-12-18T01:09:59.9782915Z divide gradients by that during allreduce. Set 2024-12-18T01:09:59.9783097Z ``divide_by_initial_world_size=True`` to ensure every input 2024-12-18T01:09:59.9783321Z sample including the uneven inputs have equal weight in terms of 2024-12-18T01:09:59.9783493Z how much they contribute to the global gradient. This is 2024-12-18T01:09:59.9783680Z achieved by always dividing the gradient by the initial 2024-12-18T01:09:59.9783913Z ``world_size`` even when we encounter uneven inputs. If you set 2024-12-18T01:09:59.9784152Z this to ``False``, we divide the gradient by the remaining 2024-12-18T01:09:59.9784364Z number of nodes. This ensures parity with training on a smaller 2024-12-18T01:09:59.9784551Z ``world_size`` although it also means the uneven inputs would 2024-12-18T01:09:59.9784765Z contribute more towards the global gradient. Typically, you 2024-12-18T01:09:59.9784954Z would want to set this to ``True`` for cases where the last few 2024-12-18T01:09:59.9785175Z inputs of your training job are uneven. In extreme cases, where 2024-12-18T01:09:59.9785366Z there is a large discrepancy in the number of inputs, setting 2024-12-18T01:09:59.9785512Z this to ``False`` might provide better results. 2024-12-18T01:09:59.9785739Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-12-18T01:09:59.9785918Z in ``enable=False`` to disable in cases where you know that 2024-12-18T01:09:59.9786121Z inputs are even across participating processes. Default is 2024-12-18T01:09:59.9786215Z ``True``. 2024-12-18T01:09:59.9786414Z throw_on_early_termination (bool): Whether to throw an error 2024-12-18T01:09:59.9786598Z or continue training when at least one rank has exhausted 2024-12-18T01:09:59.9786785Z inputs. If ``True``, will throw upon the first rank reaching end 2024-12-18T01:09:59.9786974Z of data. If ``False``, will continue training with a smaller 2024-12-18T01:09:59.9787168Z effective world size until all ranks are joined. Note that if 2024-12-18T01:09:59.9787303Z this flag is specified, then the flag 2024-12-18T01:09:59.9787476Z ``divide_by_initial_world_size`` would be ignored. Default 2024-12-18T01:09:59.9787582Z is ``False``. 2024-12-18T01:09:59.9787664Z 2024-12-18T01:09:59.9787747Z 2024-12-18T01:09:59.9787853Z Example:: 2024-12-18T01:09:59.9787936Z 2024-12-18T01:09:59.9788065Z >>> # xdoctest: +SKIP("Distributed") 2024-12-18T01:09:59.9788160Z >>> import torch 2024-12-18T01:09:59.9788279Z >>> import torch.distributed as dist 2024-12-18T01:09:59.9788464Z >>> import os 2024-12-18T01:09:59.9788595Z >>> import torch.multiprocessing as mp 2024-12-18T01:09:59.9788713Z >>> import torch.nn as nn 2024-12-18T01:09:59.9788816Z >>> # On each spawned worker 2024-12-18T01:09:59.9788991Z >>> def worker(rank): 2024-12-18T01:09:59.9789179Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-12-18T01:09:59.9789293Z >>> torch.cuda.set_device(rank) 2024-12-18T01:09:59.9789441Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:09:59.9789616Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:09:59.9789772Z >>> model, device_ids=[rank], output_device=rank 2024-12-18T01:09:59.9789859Z >>> ) 2024-12-18T01:09:59.9789989Z >>> # Rank 1 gets one more input than rank 0. 2024-12-18T01:09:59.9790240Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-12-18T01:09:59.9790344Z >>> with model.join(): 2024-12-18T01:09:59.9790511Z >>> for _ in range(5): 2024-12-18T01:09:59.9790618Z >>> for inp in inputs: 2024-12-18T01:09:59.9790751Z >>> loss = model(inp).sum() 2024-12-18T01:09:59.9790862Z >>> loss.backward() 2024-12-18T01:09:59.9791055Z >>> # Without the join() API, the below synchronization will hang 2024-12-18T01:09:59.9791208Z >>> # blocking for rank 1's allreduce to complete. 2024-12-18T01:09:59.9791334Z >>> torch.cuda.synchronize(device=rank) 2024-12-18T01:09:59.9791431Z 2024-12-18T01:09:59.9791684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9791766Z 2024-12-18T01:09:59.9791877Z warnings.warn(msg) 2024-12-18T01:09:59.9791959Z 2024-12-18T01:09:59.9792204Z --- Parse Warning: 87 / 105 --- 2024-12-18T01:09:59.9793279Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-12-18T01:09:59.9793557Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9793639Z 2024-12-18T01:09:59.9793941Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-12-18T01:09:59.9794037Z 2024-12-18T01:09:59.9794246Z Registers an optimizer with DDP such that the optimization for a 2024-12-18T01:09:59.9794469Z parameter will run immediately when that parameter's gradient is 2024-12-18T01:09:59.9794757Z finished with reduction, instead of waiting for all parameters' 2024-12-18T01:09:59.9795057Z gradients to finish reduction. This can result in a training speedup 2024-12-18T01:09:59.9795274Z depending on your workload since the optimizer can run while gradient 2024-12-18T01:09:59.9795503Z reduction for other parameters are still ongoing. In addition, this has 2024-12-18T01:09:59.9795739Z the potential to reduce peak memory consumption during training, as it 2024-12-18T01:09:59.9795943Z only needs to load the per-parameter optimizer states of a single 2024-12-18T01:09:59.9796168Z parameter at a time, instead of loading all per-parameter optimizer 2024-12-18T01:09:59.9796260Z states at once. 2024-12-18T01:09:59.9796355Z 2024-12-18T01:09:59.9796443Z Args: 2024-12-18T01:09:59.9796644Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-12-18T01:09:59.9796761Z as a fused optimizer. 2024-12-18T01:09:59.9796928Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-12-18T01:09:59.9797152Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-12-18T01:09:59.9797375Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-12-18T01:09:59.9797592Z Optimizers. If this is omitted, all DDP model parameters will be 2024-12-18T01:09:59.9797681Z optimized. 2024-12-18T01:09:59.9797879Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-12-18T01:09:59.9798044Z 2024-12-18T01:09:59.9798141Z .. warning :: 2024-12-18T01:09:59.9798367Z _register_fused_optim should only be called once on a DDP instance, 2024-12-18T01:09:59.9798576Z and registering multiple fused optimizers for the same DDP model 2024-12-18T01:09:59.9798705Z is not currently supported. Please ping 2024-12-18T01:09:59.9798949Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:09:59.9799045Z for your use case. 2024-12-18T01:09:59.9799140Z 2024-12-18T01:09:59.9799232Z .. warning :: 2024-12-18T01:09:59.9799467Z _register_fused_optim and register_comm_hook currently do not 2024-12-18T01:09:59.9799683Z compose together, meaning that custom DDP communication hooks are 2024-12-18T01:09:59.9799882Z not supported with overlapped optimizers. Please ping 2024-12-18T01:09:59.9800123Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:09:59.9800223Z for your use case. 2024-12-18T01:09:59.9800317Z 2024-12-18T01:09:59.9800408Z .. warning :: 2024-12-18T01:09:59.9800632Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-12-18T01:09:59.9800773Z with overlapped optimizer. Please ping 2024-12-18T01:09:59.9800999Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:09:59.9801107Z for your use case. 2024-12-18T01:09:59.9801191Z 2024-12-18T01:09:59.9801295Z Example:: 2024-12-18T01:09:59.9801379Z 2024-12-18T01:09:59.9801512Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-12-18T01:09:59.9801826Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-12-18T01:09:59.9802027Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-12-18T01:09:59.9802130Z >>> lr = 1e-2 2024-12-18T01:09:59.9802224Z >>> betas = (0.9, 0.99) 2024-12-18T01:09:59.9802334Z >>> eps = 1e-6 2024-12-18T01:09:59.9802558Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-12-18T01:09:59.9802679Z >>> # Example with subset of parameters 2024-12-18T01:09:59.9802829Z >>> params_to_opt = [list(net.parameters())[0]] 2024-12-18T01:09:59.9802939Z >>> net._register_fused_optim( 2024-12-18T01:09:59.9803182Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-12-18T01:09:59.9803267Z ... ) 2024-12-18T01:09:59.9803350Z 2024-12-18T01:09:59.9803616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9803698Z 2024-12-18T01:09:59.9803811Z warnings.warn(msg) 2024-12-18T01:09:59.9803893Z 2024-12-18T01:09:59.9804114Z --- Parse Warning: 88 / 105 --- 2024-12-18T01:09:59.9805112Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-12-18T01:09:59.9805377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9805608Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-12-18T01:09:59.9805692Z 2024-12-18T01:09:59.9805979Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:09:59.9806249Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:09:59.9806518Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:09:59.9806826Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:09:59.9806911Z 2024-12-18T01:09:59.9807027Z .. note:: 2024-12-18T01:09:59.9807262Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-12-18T01:09:59.9807539Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-12-18T01:09:59.9807763Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:09:59.9807988Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:09:59.9808224Z One place we are confident in is that NHWC(channels_last) conversion for 2024-12-18T01:09:59.9808443Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-12-18T01:09:59.9808694Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:09:59.9808779Z 2024-12-18T01:09:59.9809038Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:09:59.9809169Z channels_last. This ensures that; 2024-12-18T01:09:59.9809387Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:09:59.9809636Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:09:59.9809872Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:09:59.9810003Z from memory_format conversion. 2024-12-18T01:09:59.9810087Z 2024-12-18T01:09:59.9810312Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:09:59.9810565Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:09:59.9810796Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:09:59.9811050Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:09:59.9811137Z 2024-12-18T01:09:59.9811375Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:09:59.9811588Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:09:59.9811815Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:09:59.9812054Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:09:59.9812263Z another convolution layer. There's no point in propagating that 2024-12-18T01:09:59.9812495Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:09:59.9812597Z ``memory_format``. 2024-12-18T01:09:59.9812698Z 2024-12-18T01:09:59.9812928Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:09:59.9813149Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:09:59.9813286Z immediately before a convolution. 2024-12-18T01:09:59.9813371Z 2024-12-18T01:09:59.9813474Z Args: 2024-12-18T01:09:59.9813688Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-12-18T01:09:59.9813814Z ``nn.Module`` 2024-12-18T01:09:59.9813966Z memory_format: user specified ``memory_format``, 2024-12-18T01:09:59.9814147Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:09:59.9814240Z 2024-12-18T01:09:59.9814330Z Returns: 2024-12-18T01:09:59.9814486Z The original module with updated ``nn.Conv2d`` 2024-12-18T01:09:59.9814568Z 2024-12-18T01:09:59.9814657Z Example: 2024-12-18T01:09:59.9814805Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:59.9814963Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:09:59.9815205Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:09:59.9815311Z >>> model = nn.Sequential( 2024-12-18T01:09:59.9815439Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-12-18T01:09:59.9815599Z >>> # This is identical to: 2024-12-18T01:09:59.9815840Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:09:59.9816115Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:09:59.9816215Z >>> out = model(input) 2024-12-18T01:09:59.9816314Z 2024-12-18T01:09:59.9816567Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9816661Z 2024-12-18T01:09:59.9816758Z warnings.warn(msg) 2024-12-18T01:09:59.9816841Z 2024-12-18T01:09:59.9817078Z --- Parse Warning: 89 / 105 --- 2024-12-18T01:09:59.9818089Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-12-18T01:09:59.9818367Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9818579Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-12-18T01:09:59.9818867Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:09:59.9819141Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:09:59.9819396Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:09:59.9819715Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:09:59.9819802Z 2024-12-18T01:09:59.9819910Z .. note:: 2024-12-18T01:09:59.9820156Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-12-18T01:09:59.9820389Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-12-18T01:09:59.9820601Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:09:59.9820827Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:09:59.9821075Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-12-18T01:09:59.9821295Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-12-18T01:09:59.9821510Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:09:59.9821591Z 2024-12-18T01:09:59.9821827Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:09:59.9821949Z channels_last_3d. This ensures that; 2024-12-18T01:09:59.9822168Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:09:59.9822411Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:09:59.9822645Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:09:59.9822775Z from memory_format conversion. 2024-12-18T01:09:59.9822859Z 2024-12-18T01:09:59.9823091Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:09:59.9823326Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:09:59.9823552Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:09:59.9823799Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:09:59.9823882Z 2024-12-18T01:09:59.9824117Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:09:59.9824331Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:09:59.9824566Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:09:59.9824820Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:09:59.9825057Z another convolution layer. There's no point in propagating that 2024-12-18T01:09:59.9825287Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:09:59.9825388Z ``memory_format``. 2024-12-18T01:09:59.9825486Z 2024-12-18T01:09:59.9825715Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:09:59.9825947Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:09:59.9826108Z immediately before a convolution. 2024-12-18T01:09:59.9826191Z 2024-12-18T01:09:59.9826290Z Args: 2024-12-18T01:09:59.9826527Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-12-18T01:09:59.9826644Z ``nn.Module`` 2024-12-18T01:09:59.9826795Z memory_format: user specified ``memory_format``, 2024-12-18T01:09:59.9826990Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:09:59.9827073Z 2024-12-18T01:09:59.9827163Z Returns: 2024-12-18T01:09:59.9827325Z The original module with updated ``nn.Conv3d`` 2024-12-18T01:09:59.9827409Z 2024-12-18T01:09:59.9827514Z Example: 2024-12-18T01:09:59.9827656Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:09:59.9827812Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:09:59.9828057Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:09:59.9828164Z >>> model = nn.Sequential( 2024-12-18T01:09:59.9828390Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-12-18T01:09:59.9828505Z >>> # This is identical to: 2024-12-18T01:09:59.9828772Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:09:59.9829050Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:09:59.9829151Z >>> out = model(input) 2024-12-18T01:09:59.9829250Z 2024-12-18T01:09:59.9829503Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9829600Z 2024-12-18T01:09:59.9829700Z warnings.warn(msg) 2024-12-18T01:09:59.9829783Z 2024-12-18T01:09:59.9830006Z --- Parse Warning: 90 / 105 --- 2024-12-18T01:09:59.9831144Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936. 2024-12-18T01:09:59.9831425Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9831654Z Prune tensor by removing random channels along the specified dimension. 2024-12-18T01:09:59.9831755Z 2024-12-18T01:09:59.9831987Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:09:59.9832216Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:09:59.9832359Z along the specified ``dim`` selected at random. 2024-12-18T01:09:59.9832557Z Modifies module in place (and also return the modified module) 2024-12-18T01:09:59.9832657Z by: 2024-12-18T01:09:59.9832739Z 2024-12-18T01:09:59.9832957Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:59.9833178Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:59.9833386Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:59.9833606Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:59.9833703Z ``name+'_orig'``. 2024-12-18T01:09:59.9833889Z 2024-12-18T01:09:59.9833973Z Args: 2024-12-18T01:09:59.9834164Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:09:59.9834346Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:09:59.9834440Z will act. 2024-12-18T01:09:59.9834622Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:09:59.9834798Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:09:59.9835012Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:09:59.9835240Z absolute number of parameters to prune. 2024-12-18T01:09:59.9835456Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:09:59.9835565Z 2024-12-18T01:09:59.9835653Z Returns: 2024-12-18T01:09:59.9835884Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:09:59.9835971Z 2024-12-18T01:09:59.9836295Z Examples: 2024-12-18T01:09:59.9836414Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9836535Z >>> m = prune.random_structured( 2024-12-18T01:09:59.9836693Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-12-18T01:09:59.9836778Z ... ) 2024-12-18T01:09:59.9836981Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-12-18T01:09:59.9837090Z >>> print(columns_pruned) 2024-12-18T01:09:59.9837191Z 3 2024-12-18T01:09:59.9837275Z 2024-12-18T01:09:59.9837530Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9837625Z 2024-12-18T01:09:59.9837726Z warnings.warn(msg) 2024-12-18T01:09:59.9837821Z 2024-12-18T01:09:59.9838039Z --- Parse Warning: 91 / 105 --- 2024-12-18T01:09:59.9838898Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977. 2024-12-18T01:09:59.9839178Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9839481Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-12-18T01:09:59.9839578Z 2024-12-18T01:09:59.9839809Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:09:59.9840042Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:09:59.9840221Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-12-18T01:09:59.9840417Z Modifies module in place (and also return the modified module) 2024-12-18T01:09:59.9840519Z by: 2024-12-18T01:09:59.9840603Z 2024-12-18T01:09:59.9840822Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:59.9841049Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:59.9841274Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:59.9841484Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:59.9841579Z ``name+'_orig'``. 2024-12-18T01:09:59.9841676Z 2024-12-18T01:09:59.9841762Z Args: 2024-12-18T01:09:59.9841953Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:09:59.9842135Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:09:59.9842243Z will act. 2024-12-18T01:09:59.9842412Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:09:59.9842592Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:09:59.9842810Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:09:59.9843084Z absolute number of parameters to prune. 2024-12-18T01:09:59.9843291Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-12-18T01:09:59.9843448Z entries for argument ``p`` in :func:`torch.norm`. 2024-12-18T01:09:59.9843664Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:09:59.9843899Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-12-18T01:09:59.9844092Z shape as module parameter) used to compute mask for pruning. 2024-12-18T01:09:59.9844372Z The values in this tensor indicate the importance of the corresponding 2024-12-18T01:09:59.9844505Z elements in the parameter being pruned. 2024-12-18T01:09:59.9844781Z If unspecified or None, the module parameter will be used in its place. 2024-12-18T01:09:59.9844867Z 2024-12-18T01:09:59.9844969Z Returns: 2024-12-18T01:09:59.9845192Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:09:59.9845274Z 2024-12-18T01:09:59.9845379Z Examples: 2024-12-18T01:09:59.9845503Z >>> from torch.nn.utils import prune 2024-12-18T01:09:59.9845627Z >>> m = prune.ln_structured( 2024-12-18T01:09:59.9845814Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-12-18T01:09:59.9845921Z ... ) 2024-12-18T01:09:59.9846019Z 2024-12-18T01:09:59.9846271Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9846370Z 2024-12-18T01:09:59.9846471Z warnings.warn(msg) 2024-12-18T01:09:59.9846555Z 2024-12-18T01:09:59.9846765Z --- Parse Warning: 92 / 105 --- 2024-12-18T01:09:59.9847657Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024. 2024-12-18T01:09:59.9847935Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9848018Z 2024-12-18T01:09:59.9848454Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-12-18T01:09:59.9848536Z 2024-12-18T01:09:59.9848662Z Modifies modules in place by: 2024-12-18T01:09:59.9848747Z 2024-12-18T01:09:59.9848957Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:59.9849189Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:59.9849397Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:59.9849618Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:59.9849714Z ``name+'_orig'``. 2024-12-18T01:09:59.9849796Z 2024-12-18T01:09:59.9849895Z Args: 2024-12-18T01:09:59.9850098Z parameters (Iterable of (module, name) tuples): parameters of 2024-12-18T01:09:59.9850300Z the model to prune in a global fashion, i.e. by aggregating all 2024-12-18T01:09:59.9850506Z weights prior to deciding which ones to prune. module must be of 2024-12-18T01:09:59.9850669Z type :class:`nn.Module`, and name must be a string. 2024-12-18T01:09:59.9850891Z pruning_method (function): a valid pruning function from this module, 2024-12-18T01:09:59.9851065Z or a custom one implemented by the user that satisfies the 2024-12-18T01:09:59.9851306Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-12-18T01:09:59.9851535Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-12-18T01:09:59.9851770Z the corresponding parameter's importance scores tensor. The tensor 2024-12-18T01:09:59.9851977Z should be the same shape as the parameter, and is used for computing 2024-12-18T01:09:59.9852143Z mask for pruning. 2024-12-18T01:09:59.9852352Z If unspecified or None, the parameter will be used in place of its 2024-12-18T01:09:59.9852451Z importance scores. 2024-12-18T01:09:59.9852589Z kwargs: other keyword arguments such as: 2024-12-18T01:09:59.9852785Z amount (int or float): quantity of parameters to prune across the 2024-12-18T01:09:59.9852902Z specified parameters. 2024-12-18T01:09:59.9853075Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:09:59.9853309Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:09:59.9853440Z absolute number of parameters to prune. 2024-12-18T01:09:59.9853521Z 2024-12-18T01:09:59.9853646Z Raises: 2024-12-18T01:09:59.9853801Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-12-18T01:09:59.9853894Z 2024-12-18T01:09:59.9853980Z Note: 2024-12-18T01:09:59.9854196Z Since global structured pruning doesn't make much sense unless the 2024-12-18T01:09:59.9854410Z norm is normalized by the size of the parameter, we now limit the 2024-12-18T01:09:59.9854559Z scope of global pruning to unstructured methods. 2024-12-18T01:09:59.9854654Z 2024-12-18T01:09:59.9854742Z Examples: 2024-12-18T01:09:59.9854872Z >>> from torch.nn.utils import prune 2024-12-18T01:09:59.9854993Z >>> from collections import OrderedDict 2024-12-18T01:09:59.9855109Z >>> net = nn.Sequential(OrderedDict([ 2024-12-18T01:09:59.9855231Z ... ('first', nn.Linear(10, 4)), 2024-12-18T01:09:59.9855338Z ... ('second', nn.Linear(4, 1)), 2024-12-18T01:09:59.9855464Z ... ])) 2024-12-18T01:09:59.9855612Z >>> parameters_to_prune = ( 2024-12-18T01:09:59.9855745Z ... (net.first, 'weight'), 2024-12-18T01:09:59.9855863Z ... (net.second, 'weight'), 2024-12-18T01:09:59.9855952Z ... ) 2024-12-18T01:09:59.9856074Z >>> prune.global_unstructured( 2024-12-18T01:09:59.9856177Z ... parameters_to_prune, 2024-12-18T01:09:59.9856312Z ... pruning_method=prune.L1Unstructured, 2024-12-18T01:09:59.9856417Z ... amount=10, 2024-12-18T01:09:59.9856503Z ... ) 2024-12-18T01:09:59.9856736Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-12-18T01:09:59.9856824Z tensor(10) 2024-12-18T01:09:59.9856918Z 2024-12-18T01:09:59.9857001Z 2024-12-18T01:09:59.9857259Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9857356Z 2024-12-18T01:09:59.9857455Z warnings.warn(msg) 2024-12-18T01:09:59.9857549Z 2024-12-18T01:09:59.9857762Z --- Parse Warning: 93 / 105 --- 2024-12-18T01:09:59.9858633Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143. 2024-12-18T01:09:59.9858914Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9859304Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-12-18T01:09:59.9859403Z 2024-12-18T01:09:59.9859616Z Modifies module in place (and also return the modified module) by: 2024-12-18T01:09:59.9859712Z 2024-12-18T01:09:59.9859921Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:09:59.9860145Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:09:59.9860369Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:09:59.9860575Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:09:59.9860684Z ``name+'_orig'``. 2024-12-18T01:09:59.9860883Z 2024-12-18T01:09:59.9860984Z Args: 2024-12-18T01:09:59.9861169Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:09:59.9861351Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:09:59.9861457Z will act. 2024-12-18T01:09:59.9861634Z mask (Tensor): binary mask to be applied to the parameter. 2024-12-18T01:09:59.9861731Z 2024-12-18T01:09:59.9861820Z Returns: 2024-12-18T01:09:59.9862055Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:09:59.9862140Z 2024-12-18T01:09:59.9862260Z Examples: 2024-12-18T01:09:59.9862397Z >>> from torch.nn.utils import prune 2024-12-18T01:09:59.9862537Z >>> m = prune.custom_from_mask( 2024-12-18T01:09:59.9862733Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-12-18T01:09:59.9862819Z ... ) 2024-12-18T01:09:59.9862925Z >>> print(m.bias_mask) 2024-12-18T01:09:59.9863037Z tensor([0., 1., 0.]) 2024-12-18T01:09:59.9863119Z 2024-12-18T01:09:59.9863216Z 2024-12-18T01:09:59.9863468Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9863551Z 2024-12-18T01:09:59.9863663Z warnings.warn(msg) 2024-12-18T01:09:59.9863745Z 2024-12-18T01:09:59.9863950Z --- Parse Warning: 94 / 105 --- 2024-12-18T01:09:59.9864816Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=116. 2024-12-18T01:09:59.9865089Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9865448Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-12-18T01:09:59.9865534Z 2024-12-18T01:09:59.9865787Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-12-18T01:09:59.9866001Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-12-18T01:09:59.9866305Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-12-18T01:09:59.9866428Z (UAI 2018). 2024-12-18T01:09:59.9866560Z 2024-12-18T01:09:59.9866806Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-12-18T01:09:59.9867045Z but using exponential weights instead of equal weights across iterations. 2024-12-18T01:09:59.9867138Z 2024-12-18T01:09:59.9867374Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-12-18T01:09:59.9867611Z on the device :attr:`device` and allows to compute running averages of the 2024-12-18T01:09:59.9867721Z parameters of the :attr:`model`. 2024-12-18T01:09:59.9867815Z 2024-12-18T01:09:59.9867900Z Args: 2024-12-18T01:09:59.9868061Z model (torch.nn.Module): model to use with SWA/EMA 2024-12-18T01:09:59.9868382Z device (torch.device, optional): if provided, the averaged model will be 2024-12-18T01:09:59.9868498Z stored on the :attr:`device` 2024-12-18T01:09:59.9868717Z avg_fn (function, optional): the averaging function used to update 2024-12-18T01:09:59.9868917Z parameters; the function must take in the current value of the 2024-12-18T01:09:59.9869152Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-12-18T01:09:59.9869356Z parameter, and the number of models already averaged; if None, 2024-12-18T01:09:59.9869517Z an equally weighted average is used (default: None) 2024-12-18T01:09:59.9869761Z multi_avg_fn (function, optional): the averaging function used to update 2024-12-18T01:09:59.9869998Z parameters inplace; the function must take in the current values of the 2024-12-18T01:09:59.9870357Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-12-18T01:09:59.9870585Z parameters as a list, and the number of models already averaged; if None, 2024-12-18T01:09:59.9870760Z an equally weighted average is used (default: None) 2024-12-18T01:09:59.9870963Z use_buffers (bool): if ``True``, it will compute running averages for 2024-12-18T01:09:59.9871190Z both the parameters and the buffers of the model. (default: ``False``) 2024-12-18T01:09:59.9871287Z 2024-12-18T01:09:59.9871404Z Example: 2024-12-18T01:09:59.9871552Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9871680Z >>> loader, optimizer, model, loss_fn = ... 2024-12-18T01:09:59.9871889Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-12-18T01:09:59.9872113Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-12-18T01:09:59.9872234Z >>> T_max=300) 2024-12-18T01:09:59.9872343Z >>> swa_start = 160 2024-12-18T01:09:59.9872489Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-12-18T01:09:59.9872601Z >>> for i in range(300): 2024-12-18T01:09:59.9872729Z >>> for input, target in loader: 2024-12-18T01:09:59.9872844Z >>> optimizer.zero_grad() 2024-12-18T01:09:59.9872992Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:09:59.9873104Z >>> optimizer.step() 2024-12-18T01:09:59.9873224Z >>> if i > swa_start: 2024-12-18T01:09:59.9873357Z >>> swa_model.update_parameters(model) 2024-12-18T01:09:59.9873487Z >>> swa_scheduler.step() 2024-12-18T01:09:59.9873580Z >>> else: 2024-12-18T01:09:59.9873688Z >>> scheduler.step() 2024-12-18T01:09:59.9873795Z >>> 2024-12-18T01:09:59.9873952Z >>> # Update bn statistics for the swa_model at the end 2024-12-18T01:09:59.9874131Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-12-18T01:09:59.9874215Z 2024-12-18T01:09:59.9874529Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-12-18T01:09:59.9874729Z If no averaging function is provided, the default is to compute 2024-12-18T01:09:59.9874880Z equally-weighted average of the weights (SWA). 2024-12-18T01:09:59.9874980Z 2024-12-18T01:09:59.9875071Z Example: 2024-12-18T01:09:59.9875222Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:09:59.9875435Z >>> # Compute exponential moving averages of the weights and buffers 2024-12-18T01:09:59.9875608Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-12-18T01:09:59.9875842Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-12-18T01:09:59.9875931Z 2024-12-18T01:09:59.9876053Z .. note:: 2024-12-18T01:09:59.9876275Z When using SWA/EMA with models containing Batch Normalization you may 2024-12-18T01:09:59.9876497Z need to update the activation statistics for Batch Normalization. 2024-12-18T01:09:59.9876731Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-12-18T01:09:59.9876956Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-12-18T01:09:59.9877215Z statistics in a post-training step by passing data through the model. The 2024-12-18T01:09:59.9877454Z second does it during the parameter update phase by averaging all buffers. 2024-12-18T01:09:59.9877711Z Empirical evidence has shown that updating the statistics in normalization 2024-12-18T01:09:59.9877940Z layers increases accuracy, but you may wish to empirically test which 2024-12-18T01:09:59.9878171Z approach yields the best results in your problem. 2024-12-18T01:09:59.9878256Z 2024-12-18T01:09:59.9878347Z .. note:: 2024-12-18T01:09:59.9878615Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-12-18T01:09:59.9878699Z 2024-12-18T01:09:59.9878799Z .. note:: 2024-12-18T01:09:59.9879001Z When :meth:`update_parameters` is called for the first time (i.e. 2024-12-18T01:09:59.9879201Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-12-18T01:09:59.9879430Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-12-18T01:09:59.9879617Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-12-18T01:09:59.9879763Z to update the parameters. 2024-12-18T01:09:59.9879846Z 2024-12-18T01:09:59.9880084Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:09:59.9880216Z https://arxiv.org/abs/1803.05407 2024-12-18T01:09:59.9880465Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-12-18T01:09:59.9880553Z Average: 2024-12-18T01:09:59.9880672Z https://arxiv.org/abs/1806.05594 2024-12-18T01:09:59.9880885Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-12-18T01:09:59.9881000Z https://arxiv.org/abs/1904.11943 2024-12-18T01:09:59.9881234Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-12-18T01:09:59.9881335Z Generalizes Well: 2024-12-18T01:09:59.9881462Z https://arxiv.org/abs/2001.02312 2024-12-18T01:09:59.9881561Z .. _Polyak averaging: 2024-12-18T01:09:59.9881733Z https://paperswithcode.com/method/polyak-averaging 2024-12-18T01:09:59.9881831Z 2024-12-18T01:09:59.9882078Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9882177Z 2024-12-18T01:09:59.9882275Z warnings.warn(msg) 2024-12-18T01:09:59.9882358Z 2024-12-18T01:09:59.9882599Z --- Parse Warning: 95 / 105 --- 2024-12-18T01:09:59.9883428Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=368. 2024-12-18T01:09:59.9883703Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9883921Z Anneals the learning rate in each parameter group to a fixed value. 2024-12-18T01:09:59.9884018Z 2024-12-18T01:09:59.9884248Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-12-18T01:09:59.9884461Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-12-18T01:09:59.9884562Z 2024-12-18T01:09:59.9884651Z Args: 2024-12-18T01:09:59.9884844Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-12-18T01:09:59.9885051Z swa_lrs (float or list): the learning rate value for all param groups 2024-12-18T01:09:59.9885194Z together or separately for each group. 2024-12-18T01:09:59.9885400Z annealing_epochs (int): number of epochs in the annealing phase 2024-12-18T01:09:59.9885498Z (default: 10) 2024-12-18T01:09:59.9885726Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-12-18T01:09:59.9885936Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-12-18T01:09:59.9886050Z (default: "cos") 2024-12-18T01:09:59.9886232Z last_epoch (int): the index of the last epoch (default: -1) 2024-12-18T01:09:59.9886329Z 2024-12-18T01:09:59.9886508Z The :class:`SWALR` scheduler can be used together with other 2024-12-18T01:09:59.9886725Z schedulers to switch to a constant learning rate late in the training 2024-12-18T01:09:59.9886897Z as in the example below. 2024-12-18T01:09:59.9886979Z 2024-12-18T01:09:59.9887081Z Example: 2024-12-18T01:09:59.9887213Z >>> # xdoctest: +SKIP("Undefined variables") 2024-12-18T01:09:59.9887331Z >>> loader, optimizer, model = ... 2024-12-18T01:09:59.9887456Z >>> lr_lambda = lambda epoch: 0.9 2024-12-18T01:09:59.9887677Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-12-18T01:09:59.9887797Z >>> lr_lambda=lr_lambda) 2024-12-18T01:09:59.9887998Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-12-18T01:09:59.9888191Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-12-18T01:09:59.9888314Z >>> swa_start = 160 2024-12-18T01:09:59.9888416Z >>> for i in range(300): 2024-12-18T01:09:59.9888547Z >>> for input, target in loader: 2024-12-18T01:09:59.9888666Z >>> optimizer.zero_grad() 2024-12-18T01:09:59.9888815Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:09:59.9888925Z >>> optimizer.step() 2024-12-18T01:09:59.9889027Z >>> if i > swa_start: 2024-12-18T01:09:59.9889152Z >>> swa_scheduler.step() 2024-12-18T01:09:59.9889244Z >>> else: 2024-12-18T01:09:59.9889365Z >>> scheduler.step() 2024-12-18T01:09:59.9889447Z 2024-12-18T01:09:59.9889683Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:09:59.9889805Z https://arxiv.org/abs/1803.05407 2024-12-18T01:09:59.9889890Z 2024-12-18T01:09:59.9890155Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9890237Z 2024-12-18T01:09:59.9890346Z warnings.warn(msg) 2024-12-18T01:09:59.9890429Z 2024-12-18T01:09:59.9890621Z --- Parse Warning: 96 / 105 --- 2024-12-18T01:09:59.9891522Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274. 2024-12-18T01:09:59.9891782Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9891945Z Asserts that ``actual`` and ``expected`` are close. 2024-12-18T01:09:59.9892028Z 2024-12-18T01:09:59.9892407Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-12-18T01:09:59.9892490Z 2024-12-18T01:09:59.9892583Z .. math:: 2024-12-18T01:09:59.9892677Z 2024-12-18T01:09:59.9893041Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-12-18T01:09:59.9893137Z 2024-12-18T01:09:59.9893479Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-12-18T01:09:59.9893833Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-12-18T01:09:59.9893918Z 2024-12-18T01:09:59.9894122Z In addition, they are only considered close if they have the same 2024-12-18T01:09:59.9894219Z 2024-12-18T01:09:59.9894415Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-12-18T01:09:59.9894571Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-12-18T01:09:59.9894775Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-12-18T01:09:59.9894929Z - stride (if ``check_stride`` is ``True``). 2024-12-18T01:09:59.9895014Z 2024-12-18T01:09:59.9895325Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-12-18T01:09:59.9895408Z 2024-12-18T01:09:59.9895763Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-12-18T01:09:59.9896235Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-12-18T01:09:59.9896465Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-12-18T01:09:59.9896863Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-12-18T01:09:59.9896946Z 2024-12-18T01:09:59.9897243Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-12-18T01:09:59.9897631Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-12-18T01:09:59.9897759Z definition above. 2024-12-18T01:09:59.9897854Z 2024-12-18T01:09:59.9898160Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-12-18T01:09:59.9898558Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-12-18T01:09:59.9898921Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-12-18T01:09:59.9899319Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-12-18T01:09:59.9899556Z their elements are considered close according to the above definition. 2024-12-18T01:09:59.9899643Z 2024-12-18T01:09:59.9899746Z .. note:: 2024-12-18T01:09:59.9899835Z 2024-12-18T01:09:59.9900188Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-12-18T01:09:59.9900519Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-12-18T01:09:59.9900815Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-12-18T01:09:59.9900902Z 2024-12-18T01:09:59.9901000Z Args: 2024-12-18T01:09:59.9901128Z actual (Any): Actual input. 2024-12-18T01:09:59.9901247Z expected (Any): Expected input. 2024-12-18T01:09:59.9901628Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-12-18T01:09:59.9901795Z are allowed. Otherwise type equality is required. 2024-12-18T01:09:59.9902178Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-12-18T01:09:59.9902447Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:09:59.9902826Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-12-18T01:09:59.9903102Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:09:59.9903372Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-12-18T01:09:59.9903680Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-12-18T01:09:59.9903931Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-12-18T01:09:59.9904182Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-12-18T01:09:59.9904540Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-12-18T01:09:59.9904904Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-12-18T01:09:59.9905072Z :func:`torch.promote_types`) before being compared. 2024-12-18T01:09:59.9905444Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-12-18T01:09:59.9905863Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-12-18T01:09:59.9905958Z compared. 2024-12-18T01:09:59.9906338Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-12-18T01:09:59.9906700Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-12-18T01:09:59.9907110Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-12-18T01:09:59.9907263Z should return the new message. 2024-12-18T01:09:59.9907361Z 2024-12-18T01:09:59.9907451Z Raises: 2024-12-18T01:09:59.9907691Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-12-18T01:09:59.9907881Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-12-18T01:09:59.9908223Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-12-18T01:09:59.9908682Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-12-18T01:09:59.9908789Z different types. 2024-12-18T01:09:59.9909169Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-12-18T01:09:59.9909537Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-12-18T01:09:59.9909876Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-12-18T01:09:59.9910178Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-12-18T01:09:59.9910302Z :attr:`~torch.Tensor.layout`. 2024-12-18T01:09:59.9910537Z AssertionError: If only one of corresponding tensors is quantized. 2024-12-18T01:09:59.9910933Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-12-18T01:09:59.9911272Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-12-18T01:09:59.9911405Z :attr:`~torch.Tensor.device`. 2024-12-18T01:09:59.9911755Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-12-18T01:09:59.9912112Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-12-18T01:09:59.9912490Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-12-18T01:09:59.9912576Z 2024-12-18T01:09:59.9912940Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-12-18T01:09:59.9913112Z ``dtype``'s, the maximum of both tolerances is used. 2024-12-18T01:09:59.9913197Z 2024-12-18T01:09:59.9913347Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9913479Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-12-18T01:09:59.9913605Z +===========================+============+==========+ 2024-12-18T01:09:59.9913745Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:09:59.9913877Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9914033Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-12-18T01:09:59.9914163Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9914312Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9914477Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9914679Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:09:59.9914803Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9914942Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:09:59.9915083Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9915221Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9915360Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9915527Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:09:59.9915665Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9915825Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9915951Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9916099Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9916227Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9916374Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9916496Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9916643Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9916767Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9916901Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:09:59.9917037Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9917160Z | other | ``0.0`` | ``0.0`` | 2024-12-18T01:09:59.9917297Z +---------------------------+------------+----------+ 2024-12-18T01:09:59.9917379Z 2024-12-18T01:09:59.9917486Z .. note:: 2024-12-18T01:09:59.9917569Z 2024-12-18T01:09:59.9917946Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-12-18T01:09:59.9918315Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-12-18T01:09:59.9918574Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-12-18T01:09:59.9918668Z 2024-12-18T01:09:59.9918768Z >>> import functools 2024-12-18T01:09:59.9919028Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-12-18T01:09:59.9919154Z >>> assert_equal(1e-9, 1e-10) 2024-12-18T01:09:59.9919273Z Traceback (most recent call last): 2024-12-18T01:09:59.9919370Z ... 2024-12-18T01:09:59.9919500Z AssertionError: Scalars are not equal! 2024-12-18T01:09:59.9919603Z 2024-12-18T01:09:59.9919713Z Expected 1e-10 but got 1e-09. 2024-12-18T01:09:59.9919843Z Absolute difference: 9.000000000000001e-10 2024-12-18T01:09:59.9919967Z Relative difference: 9.0 2024-12-18T01:09:59.9920050Z 2024-12-18T01:09:59.9920152Z Examples: 2024-12-18T01:09:59.9920266Z >>> # tensor to tensor comparison 2024-12-18T01:09:59.9920400Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-12-18T01:09:59.9920545Z >>> actual = torch.acos(torch.cos(expected)) 2024-12-18T01:09:59.9920690Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:59.9920784Z 2024-12-18T01:09:59.9920897Z >>> # scalar to scalar comparison 2024-12-18T01:09:59.9921005Z >>> import math 2024-12-18T01:09:59.9921112Z >>> expected = math.sqrt(2.0) 2024-12-18T01:09:59.9921222Z >>> actual = 2.0 / math.sqrt(2.0) 2024-12-18T01:09:59.9921378Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:59.9921463Z 2024-12-18T01:09:59.9921602Z >>> # numpy array to numpy array comparison 2024-12-18T01:09:59.9921759Z >>> import numpy as np 2024-12-18T01:09:59.9921883Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-12-18T01:09:59.9922021Z >>> actual = np.arccos(np.cos(expected)) 2024-12-18T01:09:59.9922166Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:59.9922260Z 2024-12-18T01:09:59.9922381Z >>> # sequence to sequence comparison 2024-12-18T01:09:59.9922496Z >>> import numpy as np 2024-12-18T01:09:59.9922749Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-12-18T01:09:59.9922909Z >>> # length and their elements have to match. 2024-12-18T01:09:59.9923123Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-12-18T01:09:59.9923232Z >>> actual = tuple(expected) 2024-12-18T01:09:59.9923411Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:59.9923499Z 2024-12-18T01:09:59.9923637Z >>> # mapping to mapping comparison 2024-12-18T01:09:59.9923763Z >>> from collections import OrderedDict 2024-12-18T01:09:59.9923865Z >>> import numpy as np 2024-12-18T01:09:59.9923983Z >>> foo = torch.tensor(1.0) 2024-12-18T01:09:59.9924074Z >>> bar = 2.0 2024-12-18T01:09:59.9924190Z >>> baz = np.array(3.0) 2024-12-18T01:09:59.9924442Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-12-18T01:09:59.9924647Z >>> # have to have the same set of keys and their elements have to match. 2024-12-18T01:09:59.9924863Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-12-18T01:09:59.9925003Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-12-18T01:09:59.9925161Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:59.9925245Z 2024-12-18T01:09:59.9925385Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:09:59.9925500Z >>> actual = expected.clone() 2024-12-18T01:09:59.9925668Z >>> # By default, directly related instances can be compared 2024-12-18T01:09:59.9925898Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-12-18T01:09:59.9926091Z >>> # This check can be made more strict with allow_subclasses=False 2024-12-18T01:09:59.9926218Z >>> torch.testing.assert_close( 2024-12-18T01:09:59.9926418Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-12-18T01:09:59.9926518Z ... ) 2024-12-18T01:09:59.9926637Z Traceback (most recent call last): 2024-12-18T01:09:59.9926721Z ... 2024-12-18T01:09:59.9926945Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:09:59.9927161Z and . 2024-12-18T01:09:59.9927403Z >>> # If the inputs are not directly related, they are never considered close 2024-12-18T01:09:59.9927577Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-12-18T01:09:59.9927708Z Traceback (most recent call last): 2024-12-18T01:09:59.9927795Z ... 2024-12-18T01:09:59.9928085Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:09:59.9928207Z and . 2024-12-18T01:09:59.9928469Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-12-18T01:09:59.9928608Z >>> # their type if check_dtype=False. 2024-12-18T01:09:59.9928781Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-12-18T01:09:59.9928881Z 2024-12-18T01:09:59.9928985Z >>> # NaN != NaN by default. 2024-12-18T01:09:59.9929129Z >>> expected = torch.tensor(float("Nan")) 2024-12-18T01:09:59.9929308Z >>> actual = expected.clone() 2024-12-18T01:09:59.9929454Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:09:59.9929584Z Traceback (most recent call last): 2024-12-18T01:09:59.9929670Z ... 2024-12-18T01:09:59.9929797Z AssertionError: Scalars are not close! 2024-12-18T01:09:59.9929900Z 2024-12-18T01:09:59.9930028Z Expected nan but got nan. 2024-12-18T01:09:59.9930190Z Absolute difference: nan (up to 1e-05 allowed) 2024-12-18T01:09:59.9930360Z Relative difference: nan (up to 1.3e-06 allowed) 2024-12-18T01:09:59.9930895Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-12-18T01:09:59.9930985Z 2024-12-18T01:09:59.9931153Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:09:59.9931273Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-12-18T01:09:59.9931434Z >>> # The default error message can be overwritten. 2024-12-18T01:09:59.9931728Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-12-18T01:09:59.9931861Z Traceback (most recent call last): 2024-12-18T01:09:59.9931946Z ... 2024-12-18T01:09:59.9932098Z AssertionError: Argh, the tensors are not close! 2024-12-18T01:09:59.9932334Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-12-18T01:09:59.9932436Z >>> # extra information 2024-12-18T01:09:59.9932564Z >>> torch.testing.assert_close( 2024-12-18T01:09:59.9932766Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-12-18T01:09:59.9932867Z ... ) 2024-12-18T01:09:59.9932991Z Traceback (most recent call last): 2024-12-18T01:09:59.9933079Z ... 2024-12-18T01:09:59.9933199Z AssertionError: Header 2024-12-18T01:09:59.9933291Z 2024-12-18T01:09:59.9933417Z Tensor-likes are not close! 2024-12-18T01:09:59.9933508Z 2024-12-18T01:09:59.9933622Z Mismatched elements: 2 / 3 (66.7%) 2024-12-18T01:09:59.9933881Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-12-18T01:09:59.9934114Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-12-18T01:09:59.9934216Z 2024-12-18T01:09:59.9934301Z Footer 2024-12-18T01:09:59.9934398Z 2024-12-18T01:09:59.9934648Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9934732Z 2024-12-18T01:09:59.9934842Z warnings.warn(msg) 2024-12-18T01:09:59.9934925Z 2024-12-18T01:09:59.9935183Z --- Parse Warning: 97 / 105 --- 2024-12-18T01:09:59.9936353Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=110. 2024-12-18T01:09:59.9936641Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9936787Z Register a container-like type as pytree node. 2024-12-18T01:09:59.9936871Z 2024-12-18T01:09:59.9936987Z Args: 2024-12-18T01:09:59.9937178Z cls (type): A Python type to treat as an internal pytree node. 2024-12-18T01:09:59.9937462Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-12-18T01:09:59.9937716Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-12-18T01:09:59.9938008Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-12-18T01:09:59.9938142Z passed to the ``unflatten_fn``. 2024-12-18T01:09:59.9938421Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-12-18T01:09:59.9938826Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-12-18T01:09:59.9938987Z The function should return an instance of ``cls``. 2024-12-18T01:09:59.9939267Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-12-18T01:09:59.9939434Z qualified name used when serializing the tree spec. 2024-12-18T01:09:59.9939756Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-12-18T01:09:59.9940070Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-12-18T01:09:59.9940384Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-12-18T01:09:59.9940700Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-12-18T01:09:59.9940967Z how to convert the custom json dumpable representation of the context back to the 2024-12-18T01:09:59.9941239Z original context. This is used for json deserialization, which is being used in 2024-12-18T01:09:59.9941359Z :mod:`torch.export` right now. 2024-12-18T01:09:59.9941458Z 2024-12-18T01:09:59.9941560Z Example:: 2024-12-18T01:09:59.9941645Z 2024-12-18T01:09:59.9941766Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9941917Z >>> # Registry a Python type with lambda functions 2024-12-18T01:09:59.9942042Z >>> register_pytree_node( 2024-12-18T01:09:59.9942136Z ... set, 2024-12-18T01:09:59.9942279Z ... lambda s: (sorted(s), None, None), 2024-12-18T01:09:59.9942410Z ... lambda children, _: set(children), 2024-12-18T01:09:59.9942498Z ... ) 2024-12-18T01:09:59.9942600Z 2024-12-18T01:09:59.9942860Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9942966Z 2024-12-18T01:09:59.9943065Z warnings.warn(msg) 2024-12-18T01:09:59.9943150Z 2024-12-18T01:09:59.9943382Z --- Parse Warning: 98 / 105 --- 2024-12-18T01:09:59.9944353Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-12-18T01:09:59.9944629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9944723Z 2024-12-18T01:09:59.9944955Z Context passed to policy function during selective checkpointing. 2024-12-18T01:09:59.9945041Z 2024-12-18T01:09:59.9945273Z This class is used to pass relevant metadata to the policy function during 2024-12-18T01:09:59.9945549Z selective checkpointing. The metadata includes whether the current invocation 2024-12-18T01:09:59.9945717Z of the policy function is during recomputation or not. 2024-12-18T01:09:59.9945813Z 2024-12-18T01:09:59.9945903Z Example: 2024-12-18T01:09:59.9946017Z >>> # xdoctest: +SKIP(stub) 2024-12-18T01:09:59.9946103Z >>> 2024-12-18T01:09:59.9946234Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:09:59.9946354Z >>> print(ctx.is_recompute) 2024-12-18T01:09:59.9946440Z >>> 2024-12-18T01:09:59.9946745Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:09:59.9946831Z >>> 2024-12-18T01:09:59.9946982Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:09:59.9947086Z >>> fn, x, y, 2024-12-18T01:09:59.9947191Z >>> use_reentrant=False, 2024-12-18T01:09:59.9947309Z >>> context_fn=context_fn, 2024-12-18T01:09:59.9947395Z >>> ) 2024-12-18T01:09:59.9947482Z 2024-12-18T01:09:59.9947749Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9947912Z 2024-12-18T01:09:59.9948039Z warnings.warn(msg) 2024-12-18T01:09:59.9948122Z 2024-12-18T01:09:59.9948407Z --- Parse Warning: 99 / 105 --- 2024-12-18T01:09:59.9949391Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335. 2024-12-18T01:09:59.9949691Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9949790Z 2024-12-18T01:09:59.9950031Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-12-18T01:09:59.9950131Z 2024-12-18T01:09:59.9950392Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-12-18T01:09:59.9950568Z operations are recomputed during the backward pass. 2024-12-18T01:09:59.9950656Z 2024-12-18T01:09:59.9950743Z Args: 2024-12-18T01:09:59.9950876Z policy_fn_or_list (Callable or List): 2024-12-18T01:09:59.9951043Z - If a policy function is provided, it should accept a 2024-12-18T01:09:59.9951296Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-12-18T01:09:59.9951631Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-12-18T01:09:59.9951872Z indicating whether the execution of the op should be recomputed or not. 2024-12-18T01:09:59.9952120Z - If a list of operations is provided, it is equivalent to a policy 2024-12-18T01:09:59.9952307Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-12-18T01:09:59.9952540Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-12-18T01:09:59.9952634Z operations. 2024-12-18T01:09:59.9952862Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-12-18T01:09:59.9953077Z raised if any tensors cached by selective activation checkpoint are 2024-12-18T01:09:59.9953300Z mutated in order to ensure correctness. If set to `True`, this check 2024-12-18T01:09:59.9953397Z is disabled. 2024-12-18T01:09:59.9953487Z Returns: 2024-12-18T01:09:59.9953615Z A tuple of two context managers. 2024-12-18T01:09:59.9953699Z 2024-12-18T01:09:59.9953799Z Example: 2024-12-18T01:09:59.9953908Z >>> # xdoctest: +REQUIRES(LINUX) 2024-12-18T01:09:59.9954008Z >>> import functools 2024-12-18T01:09:59.9954106Z >>> 2024-12-18T01:09:59.9954234Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:09:59.9954372Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:09:59.9954460Z >>> 2024-12-18T01:09:59.9954557Z >>> ops_to_save = [ 2024-12-18T01:09:59.9954683Z >>> torch.ops.aten.mm.default, 2024-12-18T01:09:59.9954773Z >>> ] 2024-12-18T01:09:59.9954870Z >>> 2024-12-18T01:09:59.9955000Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:09:59.9955101Z >>> if op in ops_to_save: 2024-12-18T01:09:59.9955243Z >>> return CheckpointPolicy.MUST_SAVE 2024-12-18T01:09:59.9955329Z >>> else: 2024-12-18T01:09:59.9955485Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-12-18T01:09:59.9955571Z >>> 2024-12-18T01:09:59.9955853Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:09:59.9955938Z >>> 2024-12-18T01:09:59.9956040Z >>> # or equivalently 2024-12-18T01:09:59.9956322Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-12-18T01:09:59.9956410Z >>> 2024-12-18T01:09:59.9956516Z >>> def fn(x, y): 2024-12-18T01:09:59.9956717Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-12-18T01:09:59.9956857Z >>> 2024-12-18T01:09:59.9957014Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:09:59.9957105Z >>> fn, x, y, 2024-12-18T01:09:59.9957221Z >>> use_reentrant=False, 2024-12-18T01:09:59.9957326Z >>> context_fn=context_fn, 2024-12-18T01:09:59.9957424Z >>> ) 2024-12-18T01:09:59.9957510Z 2024-12-18T01:09:59.9957763Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9957860Z 2024-12-18T01:09:59.9957961Z warnings.warn(msg) 2024-12-18T01:09:59.9958059Z 2024-12-18T01:09:59.9958325Z --- Parse Warning: 100 / 105 --- 2024-12-18T01:09:59.9959246Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=957. 2024-12-18T01:09:59.9959520Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9959608Z 2024-12-18T01:09:59.9959769Z Create a :class:`setuptools.Extension` for C++. 2024-12-18T01:09:59.9959851Z 2024-12-18T01:09:59.9960107Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:09:59.9960331Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-12-18T01:09:59.9960414Z 2024-12-18T01:09:59.9960631Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:09:59.9960781Z constructor. Full list arguments can be found at 2024-12-18T01:09:59.9961117Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:09:59.9961203Z 2024-12-18T01:09:59.9961309Z .. note:: 2024-12-18T01:09:59.9961540Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:09:59.9961748Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:09:59.9961962Z the user's responsibility in their library to not use APIs from 2024-12-18T01:09:59.9962192Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:09:59.9962419Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:09:59.9962634Z example, to give access to custom ops from python, the library should 2024-12-18T01:09:59.9962775Z register the ops through the dispatcher. 2024-12-18T01:09:59.9962857Z 2024-12-18T01:09:59.9962945Z Example: 2024-12-18T01:09:59.9963057Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9963211Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:59.9963335Z >>> from setuptools import setup 2024-12-18T01:09:59.9963555Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-12-18T01:09:59.9963643Z >>> setup( 2024-12-18T01:09:59.9963755Z ... name='extension', 2024-12-18T01:09:59.9963855Z ... ext_modules=[ 2024-12-18T01:09:59.9963970Z ... CppExtension( 2024-12-18T01:09:59.9964077Z ... name='extension', 2024-12-18T01:09:59.9964214Z ... sources=['extension.cpp'], 2024-12-18T01:09:59.9964334Z ... extra_compile_args=['-g'], 2024-12-18T01:09:59.9964490Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2024-12-18T01:09:59.9964593Z ... ], 2024-12-18T01:09:59.9964685Z ... cmdclass={ 2024-12-18T01:09:59.9964813Z ... 'build_ext': BuildExtension 2024-12-18T01:09:59.9964900Z ... }) 2024-12-18T01:09:59.9964984Z 2024-12-18T01:09:59.9965247Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9965332Z 2024-12-18T01:09:59.9965442Z warnings.warn(msg) 2024-12-18T01:09:59.9965525Z 2024-12-18T01:09:59.9965720Z --- Parse Warning: 101 / 105 --- 2024-12-18T01:09:59.9966688Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1019. 2024-12-18T01:09:59.9966951Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9967050Z 2024-12-18T01:09:59.9967210Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-12-18T01:09:59.9967308Z 2024-12-18T01:09:59.9967550Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:09:59.9967794Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-12-18T01:09:59.9968028Z extension. This includes the CUDA include path, library path and runtime 2024-12-18T01:09:59.9968140Z library. 2024-12-18T01:09:59.9968236Z 2024-12-18T01:09:59.9968444Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:09:59.9968612Z constructor. Full list arguments can be found at 2024-12-18T01:09:59.9968936Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:09:59.9969020Z 2024-12-18T01:09:59.9969124Z .. note:: 2024-12-18T01:09:59.9969356Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:09:59.9969580Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:09:59.9969778Z the user's responsibility in their library to not use APIs from 2024-12-18T01:09:59.9970027Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:09:59.9970243Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:09:59.9970458Z example, to give access to custom ops from python, the library should 2024-12-18T01:09:59.9970603Z register the ops through the dispatcher. 2024-12-18T01:09:59.9970689Z 2024-12-18T01:09:59.9970799Z Example: 2024-12-18T01:09:59.9970900Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9971066Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:59.9971182Z >>> from setuptools import setup 2024-12-18T01:09:59.9971408Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-12-18T01:09:59.9971512Z >>> setup( 2024-12-18T01:09:59.9971621Z ... name='cuda_extension', 2024-12-18T01:09:59.9971733Z ... ext_modules=[ 2024-12-18T01:09:59.9971837Z ... CUDAExtension( 2024-12-18T01:09:59.9971959Z ... name='cuda_extension', 2024-12-18T01:09:59.9972143Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:09:59.9972277Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:09:59.9972416Z ... 'nvcc': ['-O2']}, 2024-12-18T01:09:59.9972574Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2024-12-18T01:09:59.9972678Z ... ], 2024-12-18T01:09:59.9972772Z ... cmdclass={ 2024-12-18T01:09:59.9972891Z ... 'build_ext': BuildExtension 2024-12-18T01:09:59.9972994Z ... }) 2024-12-18T01:09:59.9973077Z 2024-12-18T01:09:59.9973195Z Compute capabilities: 2024-12-18T01:09:59.9973280Z 2024-12-18T01:09:59.9973582Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-12-18T01:09:59.9973886Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-12-18T01:09:59.9974178Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-12-18T01:09:59.9974495Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2024-12-18T01:09:59.9974778Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-12-18T01:09:59.9975032Z support (see below for details on PTX). 2024-12-18T01:09:59.9975119Z 2024-12-18T01:09:59.9975431Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-12-18T01:09:59.9975567Z CCs you want the extension to support: 2024-12-18T01:09:59.9975651Z 2024-12-18T01:09:59.9975855Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-12-18T01:09:59.9976088Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-12-18T01:09:59.9976186Z 2024-12-18T01:09:59.9976527Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-12-18T01:09:59.9976864Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-12-18T01:09:59.9977180Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-12-18T01:09:59.9977471Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-12-18T01:09:59.9977810Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-12-18T01:09:59.9978083Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-12-18T01:09:59.9978414Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-12-18T01:09:59.9978725Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-12-18T01:09:59.9978837Z "8.0 8.6" would be better. 2024-12-18T01:09:59.9978921Z 2024-12-18T01:09:59.9979221Z Note that while it's possible to include all supported archs, the more archs get included the 2024-12-18T01:09:59.9979528Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-12-18T01:09:59.9979612Z 2024-12-18T01:09:59.9979955Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-12-18T01:09:59.9980175Z To workaround the issue, move python binding logic to pure C++ file. 2024-12-18T01:09:59.9980271Z 2024-12-18T01:09:59.9980364Z Example use: 2024-12-18T01:09:59.9980464Z #include 2024-12-18T01:09:59.9980630Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-12-18T01:09:59.9980713Z 2024-12-18T01:09:59.9980814Z Instead of: 2024-12-18T01:09:59.9980923Z #include 2024-12-18T01:09:59.9981082Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-12-18T01:09:59.9981180Z 2024-12-18T01:09:59.9981455Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-12-18T01:09:59.9981974Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-12-18T01:09:59.9982058Z 2024-12-18T01:09:59.9982181Z Relocatable device code linking: 2024-12-18T01:09:59.9982266Z 2024-12-18T01:09:59.9982544Z If you want to reference device symbols across compilation units (across object files), 2024-12-18T01:09:59.9982812Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-12-18T01:09:59.9983172Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-12-18T01:09:59.9983512Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-12-18T01:09:59.9983835Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-12-18T01:09:59.9984030Z helps reduce the protentional perf degradation of `-rdc`. 2024-12-18T01:09:59.9984199Z Note that it needs to be used at both steps to be useful. 2024-12-18T01:09:59.9984282Z 2024-12-18T01:09:59.9984660Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-12-18T01:09:59.9984885Z There is also a case where `-dlink` is used without `-rdc`: 2024-12-18T01:09:59.9985151Z when an extension is linked against a static lib containing rdc-compiled objects 2024-12-18T01:09:59.9985366Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-12-18T01:09:59.9985464Z 2024-12-18T01:09:59.9985666Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-12-18T01:09:59.9985751Z 2024-12-18T01:09:59.9985852Z Example: 2024-12-18T01:09:59.9985953Z >>> # xdoctest: +SKIP 2024-12-18T01:09:59.9986142Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:09:59.9986240Z >>> CUDAExtension( 2024-12-18T01:09:59.9986373Z ... name='cuda_extension', 2024-12-18T01:09:59.9986547Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:09:59.9986644Z ... dlink=True, 2024-12-18T01:09:59.9986782Z ... dlink_libraries=["dlink_lib"], 2024-12-18T01:09:59.9986905Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:09:59.9987042Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-12-18T01:09:59.9987125Z 2024-12-18T01:09:59.9987378Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:09:59.9987475Z 2024-12-18T01:09:59.9987571Z warnings.warn(msg) 2024-12-18T01:09:59.9987667Z 2024-12-18T01:09:59.9987875Z --- Parse Warning: 102 / 105 --- 2024-12-18T01:09:59.9988838Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1300. 2024-12-18T01:09:59.9989116Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:09:59.9989199Z 2024-12-18T01:09:59.9989363Z Load a PyTorch C++ extension just-in-time (JIT). 2024-12-18T01:09:59.9989449Z 2024-12-18T01:09:59.9989673Z To load an extension, a Ninja build file is emitted, which is used to 2024-12-18T01:09:59.9989883Z compile the given sources into a dynamic library. This library is 2024-12-18T01:09:59.9990103Z subsequently loaded into the current Python process as a module and 2024-12-18T01:09:59.9990246Z returned from this function, ready for use. 2024-12-18T01:09:59.9990331Z 2024-12-18T01:09:59.9990550Z By default, the directory to which the build file is emitted and the 2024-12-18T01:09:59.9990784Z resulting library compiled to is ``/torch_extensions/``, where 2024-12-18T01:09:59.9991001Z ```` is the temporary folder on the current platform and ```` 2024-12-18T01:09:59.9991216Z the name of the extension. This location can be overridden in two ways. 2024-12-18T01:09:59.9991422Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-12-18T01:09:59.9991657Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-12-18T01:09:59.9991873Z into subfolders of this directory. Second, if the ``build_directory`` 2024-12-18T01:09:59.9992119Z argument to this function is supplied, it overrides the entire path, i.e. 2024-12-18T01:09:59.9992285Z the library will be compiled into that folder directly. 2024-12-18T01:09:59.9992383Z 2024-12-18T01:09:59.9992597Z To compile the sources, the default system compiler (``c++``) is used, 2024-12-18T01:09:59.9992837Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-12-18T01:09:59.9993073Z additional arguments to the compilation process, ``extra_cflags`` or 2024-12-18T01:09:59.9993298Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-12-18T01:09:59.9993525Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-12-18T01:09:59.9993682Z ``extra_cflags`` to pass further include directories. 2024-12-18T01:09:59.9993859Z 2024-12-18T01:09:59.9994094Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-12-18T01:09:59.9994281Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-12-18T01:09:59.9994533Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-12-18T01:09:59.9994746Z passing the CUDA lib64 directory as a library directory, and linking 2024-12-18T01:09:59.9994912Z ``cudart``. You can pass additional flags to nvcc via 2024-12-18T01:09:59.9995143Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-12-18T01:09:59.9995392Z heuristics for finding the CUDA install directory are used, which usually 2024-12-18T01:09:59.9995628Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-12-18T01:09:59.9995722Z safest option. 2024-12-18T01:09:59.9995819Z 2024-12-18T01:09:59.9995906Z Args: 2024-12-18T01:09:59.9996132Z name: The name of the extension to build. This MUST be the same as the 2024-12-18T01:09:59.9996242Z name of the pybind11 module! 2024-12-18T01:09:59.9996458Z sources: A list of relative or absolute paths to C++ source files. 2024-12-18T01:09:59.9996689Z extra_cflags: optional list of compiler flags to forward to the build. 2024-12-18T01:09:59.9996907Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-12-18T01:09:59.9997027Z when building CUDA sources. 2024-12-18T01:09:59.9997245Z extra_ldflags: optional list of linker flags to forward to the build. 2024-12-18T01:09:59.9997480Z extra_include_paths: optional list of include directories to forward 2024-12-18T01:09:59.9997575Z to the build. 2024-12-18T01:09:59.9997773Z build_directory: optional path to use as build workspace. 2024-12-18T01:09:59.9997954Z verbose: If ``True``, turns on verbose logging of load steps. 2024-12-18T01:09:59.9998180Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:09:59.9998359Z the build. If set to ``None`` (default), this value is 2024-12-18T01:09:59.9998565Z automatically determined based on the existence of ``.cu`` or 2024-12-18T01:09:59.9998751Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-12-18T01:09:59.9998865Z and libraries to be included. 2024-12-18T01:09:59.9999081Z is_python_module: If ``True`` (default), imports the produced shared 2024-12-18T01:09:59.9999272Z library as a Python module. If ``False``, behavior depends on 2024-12-18T01:09:59.9999371Z ``is_standalone``. 2024-12-18T01:09:59.9999589Z is_standalone: If ``False`` (default) loads the constructed extension 2024-12-18T01:09:59.9999787Z into the process as a plain dynamic library. If ``True``, build a 2024-12-18T01:09:59.9999901Z standalone executable. 2024-12-18T01:09:59.9999989Z 2024-12-18T01:10:00.0000078Z Returns: 2024-12-18T01:10:00.0000204Z If ``is_python_module`` is ``True``: 2024-12-18T01:10:00.0000382Z Returns the loaded PyTorch extension as a Python module. 2024-12-18T01:10:00.0000477Z 2024-12-18T01:10:00.0000678Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-12-18T01:10:00.0000904Z Returns nothing. (The shared library is loaded into the process as 2024-12-18T01:10:00.0000999Z a side effect.) 2024-12-18T01:10:00.0001080Z 2024-12-18T01:10:00.0001212Z If ``is_standalone`` is ``True``. 2024-12-18T01:10:00.0001414Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-12-18T01:10:00.0001604Z added to the PATH environment variable as a side effect.) 2024-12-18T01:10:00.0001688Z 2024-12-18T01:10:00.0001778Z Example: 2024-12-18T01:10:00.0001891Z >>> # xdoctest: +SKIP 2024-12-18T01:10:00.0002060Z >>> from torch.utils.cpp_extension import load 2024-12-18T01:10:00.0002191Z >>> module = load( 2024-12-18T01:10:00.0002292Z ... name='extension', 2024-12-18T01:10:00.0002468Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:10:00.0002573Z ... extra_cflags=['-O2'], 2024-12-18T01:10:00.0002669Z ... verbose=True) 2024-12-18T01:10:00.0002767Z 2024-12-18T01:10:00.0003019Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:10:00.0003119Z 2024-12-18T01:10:00.0003220Z warnings.warn(msg) 2024-12-18T01:10:00.0003303Z 2024-12-18T01:10:00.0003562Z --- Parse Warning: 103 / 105 --- 2024-12-18T01:10:00.0004472Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1593. 2024-12-18T01:10:00.0004756Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:10:00.0004844Z 2024-12-18T01:10:00.0005071Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-12-18T01:10:00.0005155Z 2024-12-18T01:10:00.0005389Z This function behaves exactly like :func:`load`, but takes its sources as 2024-12-18T01:10:00.0005636Z strings rather than filenames. These strings are stored to files in the 2024-12-18T01:10:00.0005848Z build directory, after which the behavior of :func:`load_inline` is 2024-12-18T01:10:00.0005968Z identical to :func:`load`. 2024-12-18T01:10:00.0006054Z 2024-12-18T01:10:00.0006157Z See `the 2024-12-18T01:10:00.0006485Z tests `_ 2024-12-18T01:10:00.0006614Z for good examples of using this function. 2024-12-18T01:10:00.0006714Z 2024-12-18T01:10:00.0006948Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-12-18T01:10:00.0007207Z the necessary header includes, as well as the (pybind11) binding code. More 2024-12-18T01:10:00.0007449Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-12-18T01:10:00.0007651Z single ``.cpp`` file. This file is then prepended with ``#include 2024-12-18T01:10:00.0007753Z ``. 2024-12-18T01:10:00.0007838Z 2024-12-18T01:10:00.0008076Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-12-18T01:10:00.0008309Z automatically generated for each function specified. ``functions`` can 2024-12-18T01:10:00.0008542Z either be a list of function names, or a dictionary mapping from function 2024-12-18T01:10:00.0008766Z names to docstrings. If a list is given, the name of each function is used 2024-12-18T01:10:00.0008861Z as its docstring. 2024-12-18T01:10:00.0008956Z 2024-12-18T01:10:00.0009169Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-12-18T01:10:00.0009360Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-12-18T01:10:00.0009569Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-12-18T01:10:00.0009803Z separately, but ultimately linked into a single library. Note that no 2024-12-18T01:10:00.0010028Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-12-18T01:10:00.0010245Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-12-18T01:10:00.0010467Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-12-18T01:10:00.0010579Z include its name in ``functions``). 2024-12-18T01:10:00.0010674Z 2024-12-18T01:10:00.0010858Z See :func:`load` for a description of arguments omitted below. 2024-12-18T01:10:00.0010955Z 2024-12-18T01:10:00.0011042Z Args: 2024-12-18T01:10:00.0011260Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-12-18T01:10:00.0011548Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-12-18T01:10:00.0011752Z functions: A list of function names for which to generate function 2024-12-18T01:10:00.0011975Z bindings. If a dictionary is given, it should map function names to 2024-12-18T01:10:00.0012157Z docstrings (which are otherwise just the function names). 2024-12-18T01:10:00.0012391Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:10:00.0012549Z the build. If set to ``None`` (default), this value is 2024-12-18T01:10:00.0012778Z automatically determined based on whether ``cuda_sources`` is 2024-12-18T01:10:00.0012945Z provided. Set it to ``True`` to force CUDA headers 2024-12-18T01:10:00.0013079Z and libraries to be included. 2024-12-18T01:10:00.0013302Z with_pytorch_error_handling: Determines whether pytorch error and 2024-12-18T01:10:00.0013502Z warning macros are handled by pytorch instead of pybind. To do 2024-12-18T01:10:00.0013735Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-12-18T01:10:00.0013934Z function. This redirection might cause issues in obscure cases 2024-12-18T01:10:00.0014120Z of cpp. This flag should be set to ``False`` when this redirect 2024-12-18T01:10:00.0014227Z causes issues. 2024-12-18T01:10:00.0014310Z 2024-12-18T01:10:00.0014410Z Example: 2024-12-18T01:10:00.0014560Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:10:00.0014720Z >>> from torch.utils.cpp_extension import load_inline 2024-12-18T01:10:00.0014824Z >>> source = """ 2024-12-18T01:10:00.0014976Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-12-18T01:10:00.0015090Z return x.sin() + y.sin(); 2024-12-18T01:10:00.0015175Z } 2024-12-18T01:10:00.0015272Z """ 2024-12-18T01:10:00.0015415Z >>> module = load_inline(name='inline_extension', 2024-12-18T01:10:00.0015538Z ... cpp_sources=[source], 2024-12-18T01:10:00.0015668Z ... functions=['sin_add']) 2024-12-18T01:10:00.0015749Z 2024-12-18T01:10:00.0015853Z .. note:: 2024-12-18T01:10:00.0016088Z Since load_inline will just-in-time compile the source code, please ensure 2024-12-18T01:10:00.0016313Z that you have the right toolchains installed in the runtime. For example, 2024-12-18T01:10:00.0016540Z when loading C++, make sure a C++ compiler is available. If you're loading 2024-12-18T01:10:00.0016786Z a CUDA extension, you will need to additionally install the corresponding CUDA 2024-12-18T01:10:00.0017047Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2024-12-18T01:10:00.0017288Z are not included when you install torch and must be additionally installed. 2024-12-18T01:10:00.0017383Z 2024-12-18T01:10:00.0017643Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2024-12-18T01:10:00.0017874Z the extension. This may use up too many resources on some systems. One 2024-12-18T01:10:00.0018094Z can control the number of workers by setting the `MAX_JOBS` environment 2024-12-18T01:10:00.0018210Z variable to a non-negative number. 2024-12-18T01:10:00.0018305Z 2024-12-18T01:10:00.0018558Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:10:00.0018654Z 2024-12-18T01:10:00.0018752Z warnings.warn(msg) 2024-12-18T01:10:00.0018837Z 2024-12-18T01:10:00.0019058Z --- Parse Warning: 104 / 105 --- 2024-12-18T01:10:00.0020024Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-12-18T01:10:00.0020353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:10:00.0020437Z 2024-12-18T01:10:00.0020739Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-12-18T01:10:00.0020825Z 2024-12-18T01:10:00.0021118Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-12-18T01:10:00.0021377Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-12-18T01:10:00.0021607Z server like load. It can emulate multiple calling threads to a single module 2024-12-18T01:10:00.0021885Z provided. In the future we plan to enhance this component to support inter and 2024-12-18T01:10:00.0022152Z intra-op parallelism as well as multiple models running in a single process. 2024-12-18T01:10:00.0022249Z 2024-12-18T01:10:00.0022499Z Please note that even though nn.Module is supported, it might incur an overhead 2024-12-18T01:10:00.0022735Z from the need to hold GIL every time we execute Python code or pass around 2024-12-18T01:10:00.0022982Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-12-18T01:10:00.0023218Z model for inference deployment it is better to switch to using it in this 2024-12-18T01:10:00.0023320Z benchmark. 2024-12-18T01:10:00.0023405Z 2024-12-18T01:10:00.0023510Z Example:: 2024-12-18T01:10:00.0023592Z 2024-12-18T01:10:00.0023715Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:10:00.0023870Z >>> from torch.utils import ThroughputBenchmark 2024-12-18T01:10:00.0024005Z >>> bench = ThroughputBenchmark(my_module) 2024-12-18T01:10:00.0024188Z >>> # Pre-populate benchmark's data set with the inputs 2024-12-18T01:10:00.0024296Z >>> for input in inputs: 2024-12-18T01:10:00.0024530Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-12-18T01:10:00.0024659Z ... bench.add_input(input[0], x2=input[1]) 2024-12-18T01:10:00.0024860Z >>> # Inputs supplied above are randomly used during the execution 2024-12-18T01:10:00.0024979Z >>> stats = bench.benchmark( 2024-12-18T01:10:00.0025083Z ... num_calling_threads=4, 2024-12-18T01:10:00.0025199Z ... num_warmup_iters = 100, 2024-12-18T01:10:00.0025298Z ... num_iters = 1000, 2024-12-18T01:10:00.0025383Z ... ) 2024-12-18T01:10:00.0025574Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-12-18T01:10:00.0025749Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-12-18T01:10:00.0025844Z 2024-12-18T01:10:00.0026096Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:10:00.0026190Z 2024-12-18T01:10:00.0026290Z warnings.warn(msg) 2024-12-18T01:10:00.0026374Z 2024-12-18T01:10:00.0026584Z --- Parse Warning: 105 / 105 --- 2024-12-18T01:10:00.0027515Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17. 2024-12-18T01:10:00.0027791Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:10:00.0027990Z Sampler that restricts data loading to a subset of the dataset. 2024-12-18T01:10:00.0028089Z 2024-12-18T01:10:00.0028225Z It is especially useful in conjunction with 2024-12-18T01:10:00.0028563Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-12-18T01:10:00.0028841Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-12-18T01:10:00.0029068Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-12-18T01:10:00.0029220Z original dataset that is exclusive to it. 2024-12-18T01:10:00.0029302Z 2024-12-18T01:10:00.0029463Z .. note:: 2024-12-18T01:10:00.0029733Z Dataset is assumed to be of constant size and that any instance of it always 2024-12-18T01:10:00.0029876Z returns the same elements in the same order. 2024-12-18T01:10:00.0029977Z 2024-12-18T01:10:00.0030063Z Args: 2024-12-18T01:10:00.0030202Z dataset: Dataset used for sampling. 2024-12-18T01:10:00.0030664Z num_replicas (int, optional): Number of processes participating in 2024-12-18T01:10:00.0030921Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-12-18T01:10:00.0031088Z current distributed group. 2024-12-18T01:10:00.0031331Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-12-18T01:10:00.0031584Z By default, :attr:`rank` is retrieved from the current distributed 2024-12-18T01:10:00.0031679Z group. 2024-12-18T01:10:00.0031914Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-12-18T01:10:00.0032009Z indices. 2024-12-18T01:10:00.0032201Z seed (int, optional): random seed used to shuffle the sampler if 2024-12-18T01:10:00.0039126Z :attr:`shuffle=True`. This number should be identical across all 2024-12-18T01:10:00.0039407Z processes in the distributed group. Default: ``0``. 2024-12-18T01:10:00.0039650Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-12-18T01:10:00.0039853Z tail of the data to make it evenly divisible across the number of 2024-12-18T01:10:00.0040087Z replicas. If ``False``, the sampler will add extra indices to make 2024-12-18T01:10:00.0040304Z the data evenly divisible across the replicas. Default: ``False``. 2024-12-18T01:10:00.0040390Z 2024-12-18T01:10:00.0040505Z .. warning:: 2024-12-18T01:10:00.0040698Z In distributed mode, calling the :meth:`set_epoch` method at 2024-12-18T01:10:00.0040979Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-12-18T01:10:00.0041240Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-12-18T01:10:00.0041380Z the same ordering will be always used. 2024-12-18T01:10:00.0041467Z 2024-12-18T01:10:00.0041562Z Example:: 2024-12-18T01:10:00.0041662Z 2024-12-18T01:10:00.0041764Z >>> # xdoctest: +SKIP 2024-12-18T01:10:00.0042001Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-12-18T01:10:00.0042178Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-12-18T01:10:00.0042298Z ... sampler=sampler) 2024-12-18T01:10:00.0042453Z >>> for epoch in range(start_epoch, n_epochs): 2024-12-18T01:10:00.0042559Z ... if is_distributed: 2024-12-18T01:10:00.0042690Z ... sampler.set_epoch(epoch) 2024-12-18T01:10:00.0042792Z ... train(loader) 2024-12-18T01:10:00.0042879Z 2024-12-18T01:10:00.0043146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:10:00.0043229Z 2024-12-18T01:10:00.0043344Z warnings.warn(msg) 2024-12-18T01:10:00.0043429Z 2024-12-18T01:10:00.0043612Z  2024-12-18T01:10:00.0043793Z === Found 9 run-time warnings === 2024-12-18T01:10:00.0043984Z --- Runtime Warning: 1 / 9 --- 2024-12-18T01:10:00.0044264Z example = 2024-12-18T01:10:00.0045598Z /opt/conda/envs/py_3.12/lib/python3.12/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.) 2024-12-18T01:10:00.0045920Z return super().refine_names(names) 2024-12-18T01:10:00.0046006Z 2024-12-18T01:10:00.0046208Z --- Runtime Warning: 2 / 9 --- 2024-12-18T01:10:00.0046521Z example = 2024-12-18T01:10:00.0047143Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:272: UserWarning: Warning only once for all operators, other operators may also be overridden. 2024-12-18T01:10:00.0047468Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-12-18T01:10:00.0047712Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-12-18T01:10:00.0048075Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-12-18T01:10:00.0048175Z dispatch key: CPU 2024-12-18T01:10:00.0048617Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-12-18T01:10:00.0049170Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:161.) 2024-12-18T01:10:00.0049344Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-12-18T01:10:00.0049429Z 2024-12-18T01:10:00.0049612Z --- Runtime Warning: 3 / 9 --- 2024-12-18T01:10:00.0049860Z example = 2024-12-18T01:10:00.0051670Z /opt/conda/envs/py_3.12/lib/python3.12/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.) 2024-12-18T01:10:00.0051933Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-12-18T01:10:00.0052018Z 2024-12-18T01:10:00.0052213Z --- Runtime Warning: 4 / 9 --- 2024-12-18T01:10:00.0052463Z example = 2024-12-18T01:10:00.0054058Z :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.) 2024-12-18T01:10:00.0054142Z 2024-12-18T01:10:00.0054339Z --- Runtime Warning: 5 / 9 --- 2024-12-18T01:10:00.0054642Z example = 2024-12-18T01:10:00.0056121Z /opt/conda/envs/py_3.12/lib/python3.12/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 2024-12-18T01:10:00.0056294Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-12-18T01:10:00.0056393Z 2024-12-18T01:10:00.0056573Z --- Runtime Warning: 6 / 9 --- 2024-12-18T01:10:00.0056861Z example = 2024-12-18T01:10:00.0057937Z /opt/conda/envs/py_3.12/lib/python3.12/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) 2024-12-18T01:10:00.0058088Z warnings.warn( 2024-12-18T01:10:00.0058184Z 2024-12-18T01:10:00.0058366Z --- Runtime Warning: 7 / 9 --- 2024-12-18T01:10:00.0058706Z example = 2024-12-18T01:10:00.0059766Z /opt/conda/envs/py_3.12/lib/python3.12/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) 2024-12-18T01:10:00.0059882Z warnings.warn( 2024-12-18T01:10:00.0059993Z 2024-12-18T01:10:00.0060176Z --- Runtime Warning: 8 / 9 --- 2024-12-18T01:10:00.0060489Z example = 2024-12-18T01:10:00.0061286Z /opt/conda/envs/py_3.12/lib/python3.12/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`. 2024-12-18T01:10:00.0061423Z WeightNorm.apply(module, name, dim) 2024-12-18T01:10:00.0061508Z 2024-12-18T01:10:00.0061701Z --- Runtime Warning: 9 / 9 --- 2024-12-18T01:10:00.0062004Z example = 2024-12-18T01:10:00.0062797Z /opt/conda/envs/py_3.12/lib/python3.12/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`. 2024-12-18T01:10:00.0062933Z WeightNorm.apply(module, name, dim) 2024-12-18T01:10:00.0063016Z 2024-12-18T01:10:00.0063340Z === 338 passed, 367 skipped, 114 warnings in 11.97 seconds === 2024-12-18T01:10:00.0063556Z Running test_autoload_enable 1/1 ... [2024-12-18 01:09:59.852332] 2024-12-18T01:10:02.6438206Z running install 2024-12-18T01:10:02.6439790Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T01:10:02.6441315Z !! 2024-12-18T01:10:02.6441495Z 2024-12-18T01:10:02.6441714Z ******************************************************************************** 2024-12-18T01:10:02.6442350Z Please avoid running ``setup.py`` directly. 2024-12-18T01:10:02.6443078Z Instead, use pypa/build, pypa/installer or other 2024-12-18T01:10:02.6443741Z standards-based tools. 2024-12-18T01:10:02.6444082Z 2024-12-18T01:10:02.6444645Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T01:10:02.6445601Z ******************************************************************************** 2024-12-18T01:10:02.6446044Z 2024-12-18T01:10:02.6446193Z !! 2024-12-18T01:10:02.6446573Z self.initialize_options() 2024-12-18T01:10:02.6573899Z running build 2024-12-18T01:10:02.6574204Z running build_py 2024-12-18T01:10:02.6651050Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T01:10:02.6654272Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-12-18T01:10:02.6658136Z running build_ext 2024-12-18T01:10:02.7452234Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T01:10:02.7453656Z creating build/temp.linux-x86_64-cpython-312 2024-12-18T01:10:02.7460785Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T01:10:03.7588951Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T01:10:03.7589979Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T01:10:03.7591076Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-12-18T01:10:03.7591656Z from extension.cpp:1: 2024-12-18T01:10:03.7593038Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T01:10:03.7593893Z extension.cpp:45:53: required from here 2024-12-18T01:10:03.7595354Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T01:10:03.7596804Z 1539 | class class_ : public detail::generic_type { 2024-12-18T01:10:03.7597286Z | ^~~~~~ 2024-12-18T01:10:03.7599056Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T01:10:03.7600431Z extension.cpp:45:53: required from here 2024-12-18T01:10:03.7603681Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2024-12-18T01:10:03.7606371Z 1599 | with_internals([&](internals &internals) { 2024-12-18T01:10:03.7606767Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:10:03.7607291Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T01:10:03.7607883Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:10:03.7608339Z 1601 | : internals.registered_types_cpp; 2024-12-18T01:10:03.7608756Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:10:03.7609179Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T01:10:03.7609589Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:10:03.7609987Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T01:10:03.7610381Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:10:03.7610711Z 1604 | }); 2024-12-18T01:10:03.7610954Z | ~ 2024-12-18T01:10:03.7614120Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-12-18T01:10:04.1672021Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T01:10:04.1676848Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T01:10:05.1596111Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-12-18T01:10:05.5305590Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T01:10:05.5310314Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/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 2024-12-18T01:10:06.6904243Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:10:06.6905168Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:10:06.6905970Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:10:06.6906908Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:10:06.6907581Z from rng_extension.cpp:6: 2024-12-18T01:10:06.6908483Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:10:06.6909491Z 1123 | # pragma unroll 2024-12-18T01:10:06.6909750Z | 2024-12-18T01:10:06.6910308Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T01:10:06.6911360Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:10:06.6912455Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:10:06.6913246Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:10:06.6914234Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:10:06.6914959Z from rng_extension.cpp:6: 2024-12-18T01:10:06.6915748Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:10:06.6916531Z 59 | #pragma unroll 2024-12-18T01:10:06.6916789Z | 2024-12-18T01:10:06.6917461Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:10:06.6918223Z 72 | #pragma unroll 2024-12-18T01:10:06.6918460Z | 2024-12-18T01:10:06.6919127Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:10:06.6919880Z 87 | #pragma unroll 2024-12-18T01:10:06.6920128Z | 2024-12-18T01:10:06.6920667Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T01:10:06.6921554Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:10:06.6922362Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:10:06.6923154Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:10:06.6924055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:10:06.6924728Z from rng_extension.cpp:6: 2024-12-18T01:10:06.6925531Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:10:06.6926322Z 153 | #pragma unroll 2024-12-18T01:10:06.6926558Z | 2024-12-18T01:10:06.6929642Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-12-18T01:10:07.0973772Z running install_lib 2024-12-18T01:10:07.1057023Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T01:10:07.1161553Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T01:10:07.1260384Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-12-18T01:10:07.1365167Z running install_egg_info 2024-12-18T01:10:07.1539347Z running egg_info 2024-12-18T01:10:07.1609102Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T01:10:07.1612518Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T01:10:07.1614724Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T01:10:07.1617262Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T01:10:07.1692838Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:10:07.1702030Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:10:07.1703547Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2024-12-18T01:10:07.1705331Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-12-18T01:10:07.1711761Z running install_scripts 2024-12-18T01:10:10.4142633Z 2024-12-18T01:10:10.4143262Z Running tests... 2024-12-18T01:10:10.4143637Z ---------------------------------------------------------------------- 2024-12-18T01:10:10.5202282Z . 2024-12-18T01:10:10.5202659Z ---------------------------------------------------------------------- 2024-12-18T01:10:10.5203069Z Ran 1 test in 0.106s 2024-12-18T01:10:10.5203227Z 2024-12-18T01:10:10.5203359Z OK 2024-12-18T01:10:10.5203505Z 2024-12-18T01:10:10.5203627Z Generating XML reports... 2024-12-18T01:10:11.0814874Z Running test_cuda_expandable_segments 1/1 ... [2024-12-18 01:10:11.081108] 2024-12-18T01:10:11.0815390Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:11.0818141Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_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 01:10:11.081515] 2024-12-18T01:10:15.4436651Z 2024-12-18T01:10:15.4438192Z test_cuda_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_expandable_segments_1.1_b7784b4f0e74a5b4_.log 2024-12-18T01:10:15.4439329Z 2024-12-18T01:10:15.4440680Z Running dynamo/test_higher_order_ops 1/1 ... [2024-12-18 01:10:15.443862] 2024-12-18T01:10:15.4441422Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:15.4446376Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:10:15.444297] 2024-12-18T01:10:18.7211304Z 2024-12-18T01:10:18.7212384Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_aae11b91ba52449a_.log 2024-12-18T01:10:18.7213175Z 2024-12-18T01:10:18.7214933Z Running dynamo/test_misc 1/1 ... [2024-12-18 01:10:18.721330] 2024-12-18T01:10:18.7215388Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:18.7218987Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_misc.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 01:10:18.721677] 2024-12-18T01:10:22.8721238Z 2024-12-18T01:10:22.8722368Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_b9e62a0f9b27ea67_.log 2024-12-18T01:10:22.8723016Z 2024-12-18T01:10:22.8724863Z Running dynamo/test_frame_init 1/1 ... [2024-12-18 01:10:22.872323] 2024-12-18T01:10:22.8725558Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:22.8728966Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:10:22.872678] 2024-12-18T01:10:26.0181030Z 2024-12-18T01:10:26.0182120Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_6a88ca1123cefff2_.log 2024-12-18T01:10:26.0182806Z 2024-12-18T01:10:26.0185292Z Running dynamo/test_nops 1/1 ... [2024-12-18 01:10:26.018296] 2024-12-18T01:10:26.0185796Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:26.0188890Z Executing ['/opt/conda/envs/py_3.12/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 01:10:26.018645] 2024-12-18T01:10:29.2096929Z 2024-12-18T01:10:29.2097982Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_32d6fd0882aa6f0d_.log 2024-12-18T01:10:29.2098618Z 2024-12-18T01:10:29.2100316Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-12-18 01:10:29.209868] 2024-12-18T01:10:29.2100844Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:29.2104579Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.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 01:10:29.210233] 2024-12-18T01:10:32.4117341Z 2024-12-18T01:10:32.4118350Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_95bcabb53c259651_.log 2024-12-18T01:10:32.4119115Z 2024-12-18T01:10:32.4120799Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-12-18 01:10:32.411920] 2024-12-18T01:10:32.4121259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:32.4124834Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_skip_non_tensor.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 01:10:32.412260] 2024-12-18T01:10:35.5953807Z 2024-12-18T01:10:35.5954794Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_879974c57901c737_.log 2024-12-18T01:10:35.5955502Z 2024-12-18T01:10:35.5957334Z Running dynamo/test_reconstruct 1/1 ... [2024-12-18 01:10:35.595570] 2024-12-18T01:10:35.5957768Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:35.5961362Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_reconstruct.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 01:10:35.595928] 2024-12-18T01:10:38.7853564Z 2024-12-18T01:10:38.7854704Z dynamo/test_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reconstruct_1.1_12d5ac4f82fff335_.log 2024-12-18T01:10:38.7855415Z 2024-12-18T01:10:38.7857180Z Running dynamo/test_sdpa 1/1 ... [2024-12-18 01:10:38.785559] 2024-12-18T01:10:38.7857625Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:38.7861374Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sdpa.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 01:10:38.785919] 2024-12-18T01:10:42.0122513Z 2024-12-18T01:10:42.0123630Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_b16c541ca9512b9b_.log 2024-12-18T01:10:42.0124316Z 2024-12-18T01:10:42.0125488Z Running dynamo/test_recompiles 1/1 ... [2024-12-18 01:10:42.012388] 2024-12-18T01:10:42.0125940Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:42.0130069Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:10:42.012717] 2024-12-18T01:10:45.3588832Z 2024-12-18T01:10:45.3592693Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_7f6f7648f8354a1f_.log 2024-12-18T01:10:45.3593469Z 2024-12-18T01:10:45.3593708Z Running dynamo/test_pre_dispatch 1/1 ... [2024-12-18 01:10:45.359001] 2024-12-18T01:10:45.3594227Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:45.3595398Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_pre_dispatch.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 01:10:45.359308] 2024-12-18T01:10:48.5769985Z 2024-12-18T01:10:48.5771386Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_31ae4b8c688a56f9_.log 2024-12-18T01:10:48.5772646Z 2024-12-18T01:10:48.5773040Z Running dynamo/test_cudagraphs 1/1 ... [2024-12-18 01:10:48.577104] 2024-12-18T01:10:48.5773710Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:48.5778562Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:10:48.577527] 2024-12-18T01:10:51.7784188Z 2024-12-18T01:10:51.7785226Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_e69efb87bb639f5b_.log 2024-12-18T01:10:51.7785958Z 2024-12-18T01:10:51.7787896Z Running dynamo/test_graph_region_tracker 1/1 ... [2024-12-18 01:10:51.778590] 2024-12-18T01:10:51.7788452Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:51.7791473Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_graph_region_tracker.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 01:10:51.778925] 2024-12-18T01:10:54.9351933Z 2024-12-18T01:10:54.9352958Z dynamo/test_graph_region_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_region_tracker_1.1_070f7f09fabd345e_.log 2024-12-18T01:10:54.9353781Z 2024-12-18T01:10:54.9354812Z Running dynamo/test_deviceguard 1/1 ... [2024-12-18 01:10:54.935324] 2024-12-18T01:10:54.9355257Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:54.9358311Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_deviceguard.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 01:10:54.935608] 2024-12-18T01:10:58.0783042Z 2024-12-18T01:10:58.0784036Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_1c4b639ba98f6282_.log 2024-12-18T01:10:58.0784998Z 2024-12-18T01:10:58.0785257Z Running dynamo/test_sources 1/1 ... [2024-12-18 01:10:58.078377] 2024-12-18T01:10:58.0785731Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:10:58.0789035Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:10:58.078678] 2024-12-18T01:11:01.3125110Z 2024-12-18T01:11:01.3126170Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_93eb0db6bbe35f45_.log 2024-12-18T01:11:01.3126897Z 2024-12-18T01:11:01.3127895Z Running dynamo/test_structured_trace 1/1 ... [2024-12-18 01:11:01.312619] 2024-12-18T01:11:01.3128589Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:01.3131354Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_structured_trace.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 01:11:01.312924] 2024-12-18T01:11:04.5096971Z 2024-12-18T01:11:04.5098198Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_4dd808f0f1977ff0_.log 2024-12-18T01:11:04.5099493Z 2024-12-18T01:11:04.5100506Z Running dynamo/test_modes 1/1 ... [2024-12-18 01:11:04.509867] 2024-12-18T01:11:04.5100961Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:04.5103938Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:04.510178] 2024-12-18T01:11:07.6747576Z 2024-12-18T01:11:07.6748564Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_e0f6b1320e2c0b94_.log 2024-12-18T01:11:07.6749340Z 2024-12-18T01:11:07.6749767Z Running dynamo/test_graph_deduplication 1/1 ... [2024-12-18 01:11:07.674823] 2024-12-18T01:11:07.6750267Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:07.6754457Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_graph_deduplication.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 01:11:07.675145] 2024-12-18T01:11:10.8483822Z 2024-12-18T01:11:10.8485358Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_8c33889c2788d960_.log 2024-12-18T01:11:10.8486545Z 2024-12-18T01:11:10.8487594Z Running dynamo/test_ctx_manager 1/1 ... [2024-12-18 01:11:10.848598] 2024-12-18T01:11:10.8488298Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:10.8492479Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.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 01:11:10.849011] 2024-12-18T01:11:14.0382270Z 2024-12-18T01:11:14.0383188Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_71332997f2b8a7a3_.log 2024-12-18T01:11:14.0383873Z 2024-12-18T01:11:14.0386264Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-12-18 01:11:14.038389] 2024-12-18T01:11:14.0386815Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:14.0390086Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.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 01:11:14.038715] 2024-12-18T01:11:17.2307740Z 2024-12-18T01:11:17.2308999Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_e953bf10acce79da_.log 2024-12-18T01:11:17.2310326Z 2024-12-18T01:11:17.2310998Z Running dynamo/test_trace_rules 1/1 ... [2024-12-18 01:11:17.230931] 2024-12-18T01:11:17.2311435Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:17.2314503Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_trace_rules.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 01:11:17.231233] 2024-12-18T01:11:20.4315143Z 2024-12-18T01:11:20.4316194Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_54bff829b823991c_.log 2024-12-18T01:11:20.4317054Z 2024-12-18T01:11:20.4317658Z Running dynamo/test_debug_utils 1/1 ... [2024-12-18 01:11:20.431618] 2024-12-18T01:11:20.4318219Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:20.4321379Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_debug_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 01:11:20.431933] 2024-12-18T01:11:23.6205153Z 2024-12-18T01:11:23.6206137Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_b644fe2006310748_.log 2024-12-18T01:11:23.6206853Z 2024-12-18T01:11:23.6208154Z Running dynamo/test_bytecode_utils 1/1 ... [2024-12-18 01:11:23.620635] 2024-12-18T01:11:23.6208683Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:23.6211687Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_bytecode_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 01:11:23.620952] 2024-12-18T01:11:26.8001605Z 2024-12-18T01:11:26.8003249Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_5f33e0fac97abd05_.log 2024-12-18T01:11:26.8004378Z 2024-12-18T01:11:26.8005352Z Running dynamo/test_recompile_ux 1/1 ... [2024-12-18 01:11:26.800356] 2024-12-18T01:11:26.8006034Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:26.8011052Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:26.800774] 2024-12-18T01:11:30.0784335Z 2024-12-18T01:11:30.0785811Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_b1eabf5e56fdcc27_.log 2024-12-18T01:11:30.0787060Z 2024-12-18T01:11:30.0787769Z Running dynamo/test_minifier 1/1 ... [2024-12-18 01:11:30.078572] 2024-12-18T01:11:30.0788671Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:30.0792067Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:30.078938] 2024-12-18T01:11:33.2976407Z 2024-12-18T01:11:33.2977420Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_5d44f7166ba6a007_.log 2024-12-18T01:11:33.2978105Z 2024-12-18T01:11:33.2980030Z Running dynamo/test_comptime 1/1 ... [2024-12-18 01:11:33.297835] 2024-12-18T01:11:33.2980534Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:33.2983918Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_comptime.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 01:11:33.298188] 2024-12-18T01:11:36.4405677Z 2024-12-18T01:11:36.4407067Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_d738b9a06dbcf1ec_.log 2024-12-18T01:11:36.4408113Z 2024-12-18T01:11:36.4409938Z Running test_hub 1/1 ... [2024-12-18 01:11:36.440794] 2024-12-18T01:11:36.4414092Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:36.4415908Z Executing ['/opt/conda/envs/py_3.12/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 01:11:36.441205] 2024-12-18T01:11:39.6104480Z 2024-12-18T01:11:39.6105662Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_b96e1c103d9797fb_.log 2024-12-18T01:11:39.6106565Z 2024-12-18T01:11:39.6108402Z Running optim/test_swa_utils 1/1 ... [2024-12-18 01:11:39.610651] 2024-12-18T01:11:39.6109076Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:39.6113943Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_swa_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 01:11:39.611066] 2024-12-18T01:11:42.7504203Z 2024-12-18T01:11:42.7505193Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_3d17848b4d06419a_.log 2024-12-18T01:11:42.7505874Z 2024-12-18T01:11:42.7508035Z Running test_quantization 3/4 ... [2024-12-18 01:11:42.750656] 2024-12-18T01:11:42.7508536Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:42.7518776Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:42.751669] 2024-12-18T01:11:47.0217205Z 2024-12-18T01:11:47.0218404Z test_quantization 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.4_f74cc6fd817b62ad_.log 2024-12-18T01:11:47.0219161Z Running 0 items in this shard: 2024-12-18T01:11:47.0219371Z 2024-12-18T01:11:47.0285981Z Running test_cuda_expandable_segments 1/1 ... [2024-12-18 01:11:47.028275] 2024-12-18T01:11:47.0286977Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:47.0287478Z Running dynamo/test_higher_order_ops 1/1 ... [2024-12-18 01:11:47.028435] 2024-12-18T01:11:47.0287948Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:47.0289505Z Running dynamo/test_misc 1/1 ... [2024-12-18 01:11:47.028740] 2024-12-18T01:11:47.0292032Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_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 01:11:47.028770] 2024-12-18T01:11:47.0293344Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:47.0294766Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:47.028899] 2024-12-18T01:11:47.0296651Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_misc.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 01:11:47.029212] 2024-12-18T01:11:50.5197071Z 2024-12-18T01:11:50.5198667Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_b7576e66baa46fb8_.log 2024-12-18T01:11:50.5200118Z 2024-12-18T01:11:51.3437607Z Uploading artifacts took 0.82 seconds 2024-12-18T01:11:51.5448066Z 2024-12-18T01:11:51.5449394Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_825e90299790d455_.log 2024-12-18T01:11:51.5450542Z 2024-12-18T01:11:51.6430162Z 2024-12-18T01:11:51.6431590Z test_cuda_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_expandable_segments_1.1_1ff1e3f0df15237d_.log 2024-12-18T01:11:51.6432338Z 2024-12-18T01:11:54.2872431Z Running dynamo/test_frame_init 1/1 ... [2024-12-18 01:11:54.286804] 2024-12-18T01:11:54.2873268Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:54.2875318Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:54.287160] 2024-12-18T01:11:55.2268246Z Running dynamo/test_nops 1/1 ... [2024-12-18 01:11:55.226430] 2024-12-18T01:11:55.2268848Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:55.2271624Z Executing ['/opt/conda/envs/py_3.12/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 01:11:55.226803] 2024-12-18T01:11:55.3053577Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-12-18 01:11:55.304953] 2024-12-18T01:11:55.3054095Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:55.3056589Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.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 01:11:55.305348] 2024-12-18T01:11:57.7018731Z 2024-12-18T01:11:57.7020359Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_c683a7487cdea14f_.log 2024-12-18T01:11:57.7021742Z 2024-12-18T01:11:58.8179606Z 2024-12-18T01:11:58.8187581Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_d28a6be5346c8db8_.log 2024-12-18T01:11:58.8194523Z 2024-12-18T01:11:58.8227101Z 2024-12-18T01:11:58.8228765Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_bf9988efe9be2cc7_.log 2024-12-18T01:11:58.8230290Z 2024-12-18T01:12:01.3052201Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-12-18 01:12:01.304815] 2024-12-18T01:12:01.3053036Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:01.3055821Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_skip_non_tensor.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 01:12:01.305188] 2024-12-18T01:12:02.4610120Z Running dynamo/test_reconstruct 1/1 ... [2024-12-18 01:12:02.460579] 2024-12-18T01:12:02.4610993Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:02.4613195Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_reconstruct.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 01:12:02.460931] 2024-12-18T01:12:02.4865374Z Running dynamo/test_sdpa 1/1 ... [2024-12-18 01:12:02.486146] 2024-12-18T01:12:02.4866500Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:02.4868526Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sdpa.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 01:12:02.486502] 2024-12-18T01:12:04.7325443Z 2024-12-18T01:12:04.7326762Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_b64708099da25d57_.log 2024-12-18T01:12:05.9378471Z 2024-12-18T01:12:05.9378817Z 2024-12-18T01:12:05.9379930Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_6d169ef991fdfded_.log 2024-12-18T01:12:05.9380558Z 2024-12-18T01:12:05.9387604Z 2024-12-18T01:12:05.9389073Z dynamo/test_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reconstruct_1.1_e599715f527646d4_.log 2024-12-18T01:12:05.9390184Z 2024-12-18T01:12:08.3247112Z Running dynamo/test_recompiles 1/1 ... [2024-12-18 01:12:08.324306] 2024-12-18T01:12:08.3247840Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:08.3249353Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:08.324610] 2024-12-18T01:12:09.5056125Z Running dynamo/test_pre_dispatch 1/1 ... [2024-12-18 01:12:09.505181] 2024-12-18T01:12:09.5056984Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:09.5059183Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_pre_dispatch.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 01:12:09.505549] 2024-12-18T01:12:09.5300051Z Running dynamo/test_cudagraphs 1/1 ... [2024-12-18 01:12:09.529672] 2024-12-18T01:12:09.5300795Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:09.5303938Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:09.530056] 2024-12-18T01:12:11.7126336Z 2024-12-18T01:12:11.7127427Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_7493092944f7bd49_.log 2024-12-18T01:12:11.7128188Z 2024-12-18T01:12:12.9702873Z 2024-12-18T01:12:12.9704342Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_0fed9bb64a30f22b_.log 2024-12-18T01:12:12.9705623Z 2024-12-18T01:12:12.9711235Z 2024-12-18T01:12:12.9712272Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_d088a114d224a3bb_.log 2024-12-18T01:12:12.9712964Z 2024-12-18T01:12:15.3354252Z Running dynamo/test_graph_region_tracker 1/1 ... [2024-12-18 01:12:15.335046] 2024-12-18T01:12:15.3354811Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:15.3357303Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_graph_region_tracker.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 01:12:15.335456] 2024-12-18T01:12:16.5715136Z Running dynamo/test_deviceguard 1/1 ... [2024-12-18 01:12:16.571124] 2024-12-18T01:12:16.5716011Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:16.5718236Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_deviceguard.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 01:12:16.571486] 2024-12-18T01:12:16.5790237Z Running dynamo/test_sources 1/1 ... [2024-12-18 01:12:16.578721] 2024-12-18T01:12:16.5790959Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:16.5794355Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:16.579065] 2024-12-18T01:12:18.7053744Z 2024-12-18T01:12:18.7055726Z dynamo/test_graph_region_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_region_tracker_1.1_e67ec3ce9e512880_.log 2024-12-18T01:12:18.7057027Z 2024-12-18T01:12:20.0417745Z 2024-12-18T01:12:20.0419455Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_bbedca1cbd1c8213_.log 2024-12-18T01:12:20.0420810Z 2024-12-18T01:12:20.0777184Z 2024-12-18T01:12:20.0778215Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_a614defc7e958111_.log 2024-12-18T01:12:20.0778938Z 2024-12-18T01:12:22.3091195Z Running dynamo/test_structured_trace 1/1 ... [2024-12-18 01:12:22.308676] 2024-12-18T01:12:22.3092023Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:22.3095101Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_structured_trace.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 01:12:22.309060] 2024-12-18T01:12:23.6671768Z Running dynamo/test_modes 1/1 ... [2024-12-18 01:12:23.666686] 2024-12-18T01:12:23.6672603Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:23.6673394Z Running dynamo/test_graph_deduplication 1/1 ... [2024-12-18 01:12:23.666799] 2024-12-18T01:12:23.6674432Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:23.6676225Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:23.667057] 2024-12-18T01:12:23.6679809Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_graph_deduplication.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 01:12:23.667157] 2024-12-18T01:12:25.7553308Z 2024-12-18T01:12:25.7554513Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_0cf7799d4e9b105c_.log 2024-12-18T01:12:25.7555310Z 2024-12-18T01:12:27.1834062Z 2024-12-18T01:12:27.1835405Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_94106ed71bad2f82_.log 2024-12-18T01:12:27.1836374Z 2024-12-18T01:12:27.1842826Z 2024-12-18T01:12:27.1843903Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_59cdba963b6afc49_.log 2024-12-18T01:12:27.1844545Z 2024-12-18T01:12:29.3864944Z Running dynamo/test_ctx_manager 1/1 ... [2024-12-18 01:12:29.386079] 2024-12-18T01:12:29.3865739Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:29.3867708Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.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 01:12:29.386430] 2024-12-18T01:12:30.8105315Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-12-18 01:12:30.810150] 2024-12-18T01:12:30.8106214Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:30.8108983Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.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 01:12:30.810541] 2024-12-18T01:12:30.8317996Z Running dynamo/test_trace_rules 1/1 ... [2024-12-18 01:12:30.831424] 2024-12-18T01:12:30.8318637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:30.8320542Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_trace_rules.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 01:12:30.831782] 2024-12-18T01:12:32.8843985Z 2024-12-18T01:12:32.8845283Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_464d02340bcd04c9_.log 2024-12-18T01:12:32.8845974Z 2024-12-18T01:12:34.2833155Z 2024-12-18T01:12:34.2834280Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_f6bc946161a3e671_.log 2024-12-18T01:12:34.2835089Z 2024-12-18T01:12:34.2999581Z 2024-12-18T01:12:34.3001365Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_90019c00969c77c7_.log 2024-12-18T01:12:34.3002643Z 2024-12-18T01:12:36.5465126Z Running dynamo/test_debug_utils 1/1 ... [2024-12-18 01:12:36.546074] 2024-12-18T01:12:36.5465867Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:36.5468110Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_debug_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 01:12:36.546480] 2024-12-18T01:12:37.8968803Z Running dynamo/test_bytecode_utils 1/1 ... [2024-12-18 01:12:37.896454] 2024-12-18T01:12:37.8969712Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:37.8972969Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_bytecode_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 01:12:37.896833] 2024-12-18T01:12:37.9104548Z Running dynamo/test_recompile_ux 1/1 ... [2024-12-18 01:12:37.910089] 2024-12-18T01:12:37.9105303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:37.9107400Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:37.910434] 2024-12-18T01:12:40.0388973Z 2024-12-18T01:12:40.0390234Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_fe4a45356cd73972_.log 2024-12-18T01:12:40.0391144Z 2024-12-18T01:12:41.3883019Z 2024-12-18T01:12:41.3884305Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_c5ae780e2d500659_.log 2024-12-18T01:12:41.3885021Z 2024-12-18T01:12:41.4089257Z 2024-12-18T01:12:41.4090631Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_bf254d32ecdd787a_.log 2024-12-18T01:12:41.4092117Z 2024-12-18T01:12:43.7039105Z Running dynamo/test_minifier 1/1 ... [2024-12-18 01:12:43.703455] 2024-12-18T01:12:43.7039684Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:43.7041599Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:43.703888] 2024-12-18T01:12:45.1068597Z Running dynamo/test_comptime 1/1 ... [2024-12-18 01:12:45.106461] 2024-12-18T01:12:45.1069214Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:45.1071337Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_comptime.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 01:12:45.106812] 2024-12-18T01:12:45.1251099Z Running test_hub 1/1 ... [2024-12-18 01:12:45.124784] 2024-12-18T01:12:45.1251569Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:45.1254440Z Executing ['/opt/conda/envs/py_3.12/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 01:12:45.125127] 2024-12-18T01:12:47.2473875Z 2024-12-18T01:12:47.2475204Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_827c6195a50aed39_.log 2024-12-18T01:12:47.2476197Z 2024-12-18T01:12:48.5690441Z 2024-12-18T01:12:48.5691799Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_336c88c8f284a535_.log 2024-12-18T01:12:48.5692864Z 2024-12-18T01:12:48.5713742Z 2024-12-18T01:12:48.5715059Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_79935af65143b860_.log 2024-12-18T01:12:48.5716376Z 2024-12-18T01:12:50.8715297Z Running optim/test_swa_utils 1/1 ... [2024-12-18 01:12:50.871152] 2024-12-18T01:12:50.8715940Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:50.8717536Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_swa_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 01:12:50.871494] 2024-12-18T01:12:52.1910964Z Running test_quantization 3/4 ... [2024-12-18 01:12:52.190689] 2024-12-18T01:12:52.1911879Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:12:52.1914249Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:12:52.191088] 2024-12-18T01:12:54.2586331Z 2024-12-18T01:12:54.2587944Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_1189c30c67398ebc_.log 2024-12-18T01:12:54.2589333Z 2024-12-18T01:19:14.1796934Z 2024-12-18T01:19:14.1798049Z test_quantization 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.4_c1da327c5b76ca00_.log 2024-12-18T01:19:14.1899197Z Running 294 items in this shard: test/test_quantization.py::TestQuantizedOps::test_avg_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_batch_norm_relu, test/test_quantization.py::TestQuantizedOps::test_cat_nhwc, test/test_quantization.py::TestQuantizedOps::test_channel_shuffle, test/test_quantization.py::TestQuantizedOps::test_equal, test/test_quantization.py::TestQuantizedOps::test_interpolate, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_cudnn, test/test_quantization.py::TestQuantizedOps::test_max_pool3d, test/test_quantization.py::TestQuantizedOps::test_qhardsigmoid, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_different_qparams, test/test_quantization.py::TestQuantizedOps::test_qrelu, test/test_quantization.py::TestQuantizedOps::test_qsoftmax_qnnpack, test/test_quantization.py::TestQuantizedOps::test_sigmoid_dequantize_rounding_error, test/test_quantization.py::TestQuantizedOps::test_sigmoid_non_observed, test/test_quantization.py::TestQNNPackOps::test_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_qnnpack_maxpool2d, test/test_quantization.py::TestQNNPackOps::test_qnnpack_sigmoid_sweep, test/test_quantization.py::TestQuantizedLinear::test_qlinear_cudnn, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_unpack, test/test_quantization.py::TestQuantizedConv::test_benchmark, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_add_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_float_output_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_dynamic_fp16, test/test_quantization.py::TestDynamicQuantizedOps::test_unpacked_qlinear_dynamic_fp16, test/test_quantization.py::TestDynamicQuantizedOps::test_unpacked_qlinear_dynamic_fp16_opcheck, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv1d_api, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_control, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_preserves_qparam_shapes_for_activations, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_fq_serializable_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cuda, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_backward_op_fake_quant_off, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_obs_fake_quant_backward_op, test/test_quantization.py::TestQuantizedTensor::test_bfp16_quantize, test/test_quantization.py::TestQuantizedTensor::test_choose_qparams, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dequantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dynamic_quant_pattern, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_bfloat16_input, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_group, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_fp16_saturate_op, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_to_memory_format, test/test_quantization.py::TestQuantizedTensor::test_pickle_checkpoint_qtensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_put_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_legacy_new_failure, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_permute, test/test_quantization.py::TestQuantizedTensor::test_quantize_per_channel_float_qparams, test/test_quantization.py::TestFakeQuantize::test_quant_min_max_override, test/test_quantization.py::TestObserver::test_dynamic_quant_observer_matching_choose_qparams, test/test_quantization.py::TestObserver::test_histogram_observer_ignore_infinity, test/test_quantization.py::TestObserver::test_observer_scriptable, test/test_quantization.py::TestObserver::test_per_channel_observers, test/test_quantization.py::TestObserver::test_state_dict_respects_device_affinity, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm2d_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add_relu, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv3d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_dropout_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_elu, test/test_quantization.py::TestStaticQuantizedModule::test_group_norm, test/test_quantization.py::TestStaticQuantizedModule::test_hard_swish, test/test_quantization.py::TestStaticQuantizedModule::test_linear, test/test_quantization.py::TestStaticQuantizedModule::test_linear_leaky_relu, test/test_quantization.py::TestStaticQuantizedModule::test_quant_dequant_api, test/test_quantization.py::TestDynamicQuantizedModule::test_cell_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_convtranspose2d, test/test_quantization.py::TestReferenceQuantizedModule::test_sparse, test/test_quantization.py::TestRecordHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_extreme_inputs, test/test_quantization.py::TestHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestDistributed::test_syncbn_preserves_qconfig, test/test_quantization.py::TestFusedObsFakeQuantModule::test_default_fused_qat_config, test/test_quantization.py::TestFusedObsFakeQuantModule::test_embedding_bag_qat_config, test/test_quantization.py::TestBackendConfig::test_backend_config_to_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_num_tensor_args_to_observation_type, test/test_quantization.py::TestBackendConfig::test_dtype_config_from_dict, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptdq, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_qat, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_convtranspose_per_channel_fails_early, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested1, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_normalization, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_save_load_state_dict, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_embedding_ops_dynamic, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_single_layer, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_type_match_rule, test/test_quantization.py::TestQuantizeEagerOps::test_conv_2d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_3d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_1d, test/test_quantization.py::TestQuantizeEagerOps::test_int16_reference_module, test/test_quantization.py::TestQuantizeEagerOps::test_linear, test/test_quantization.py::TestQuantizeEagerOps::test_relu, test/test_quantization.py::TestQuantizeEagerQAT::test_conv_linear_symm, test/test_quantization.py::TestQuantizeEagerQAT::test_dynamic_qat_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_eval_only_fake_quant, test/test_quantization.py::TestQuantizeEagerQAT::test_manual, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_folded_vs_unfolded, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_fixed_qparam_ops, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_precomputed_fake_quant, test/test_quantization.py::TestFuseEager::test_forward_hooks_preserved, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_eval, test/test_quantization.py::TestModelNumericsEager::test_float_quant_compare_per_tensor, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_partial, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_lstm_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_output_logger, test/test_quantization.py::TestFuseFx::test_fuse_addtional_fuser_method, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_onednn, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_eval, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_leaky_relu_onednn, test/test_quantization.py::TestFuseFx::test_fuse_linear_tanh_for_onednn_backend, test/test_quantization.py::TestFuseFx::test_fuse_module_relu, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_matchallnode, test/test_quantization.py::TestFuseFx::test_linear_tanh_not_fused_by_default, test/test_quantization.py::TestFuseFx::test_qconfig_fused_module, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_dynamic_quant, test/test_quantization.py::TestQuantizeFx::test_assert_on_size_after_quant_layer, test/test_quantization.py::TestQuantizeFx::test_backend_config_check_for_weight_and_bias, test/test_quantization.py::TestQuantizeFx::test_change_backend_config_for_fixed_qparam_ops, test/test_quantization.py::TestQuantizeFx::test_channel_shuffle_lowering, test/test_quantization.py::TestQuantizeFx::test_conv_bn_relu, test/test_quantization.py::TestQuantizeFx::test_conv_linear_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_lowering, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_relu_reference, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_custom_module_class, test/test_quantization.py::TestQuantizeFx::test_default_quant_after_none_qconfig, test/test_quantization.py::TestQuantizeFx::test_dynamic_with_fusion, test/test_quantization.py::TestQuantizeFx::test_dynamic_with_fusion_multiple_uses, test/test_quantization.py::TestQuantizeFx::test_fp32_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_fp32_sum, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_linear_shape_view, test/test_quantization.py::TestQuantizeFx::test_linear_size_view, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_transpose_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_masked_fill_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_match_pattern_with_multiple_args, test/test_quantization.py::TestQuantizeFx::test_output_lists_and_dicts, test/test_quantization.py::TestQuantizeFx::test_pattern_match, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_input_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_names, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_name, test/test_quantization.py::TestQuantizeFx::test_qat_prepare_device_affinity, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_setup, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_global, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_regex, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_regex, test/test_quantization.py::TestQuantizeFx::test_qconfig_qat_module_type, test/test_quantization.py::TestQuantizeFx::test_quant_output_always_observed, test/test_quantization.py::TestQuantizeFx::test_ref_conv_module, test/test_quantization.py::TestQuantizeFx::test_register_patterns, test/test_quantization.py::TestQuantizeFx::test_repeat_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_reroute_tuple_getitem_patterns, test/test_quantization.py::TestQuantizeFx::test_size_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_sub_scalar, test/test_quantization.py::TestQuantizeFx::test_trace_quantize_per_tensor, test/test_quantization.py::TestQuantizeFx::test_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_unsqueeze_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_bmm, test/test_quantization.py::TestQuantizeFxOps::test_bmm_int_reference, test/test_quantization.py::TestQuantizeFxOps::test_clamp, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_1d, test/test_quantization.py::TestQuantizeFxOps::test_embedding_bag, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_fp16, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_qint8, test/test_quantization.py::TestQuantizeFxOps::test_float_functional, test/test_quantization.py::TestQuantizeFxOps::test_functional_conv, test/test_quantization.py::TestQuantizeFxOps::test_functional_linear, test/test_quantization.py::TestQuantizeFxOps::test_getitem, test/test_quantization.py::TestQuantizeFxOps::test_multiple_qconfigs_for_single_value, test/test_quantization.py::TestQuantizeFxOps::test_pixel_shuffle, test/test_quantization.py::TestQuantizeFxOps::test_pixel_shuffle_module, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle_module, test/test_quantization.py::TestQuantizeFxOps::test_qbatch_norm_relu, test/test_quantization.py::TestQuantizeFxOps::test_qmatmul, test/test_quantization.py::TestQuantizeFxOps::test_rnn_cell, test/test_quantization.py::TestQuantizeFxOps::test_sub, test/test_quantization.py::TestQuantizeFxModels::test_model_dropout, test/test_quantization.py::TestQuantizeFxModels::test_qat_embedding_linear, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting_through_unknown_ops, test/test_quantization.py::TestNumericDebugger::test_simple, test/test_quantization.py::TestFXGraphMatcher::test_matching_failure_node_count, test/test_quantization.py::TestFXGraphMatcher::test_matching_failure_node_type, test/test_quantization.py::TestFXGraphMatcher::test_nodes_with_equal_types_get_matched, test/test_quantization.py::TestFXGraphMatcher::test_op_relationship_mapping, test/test_quantization.py::TestFXGraphMatcher::test_results_order, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod_multi, test/test_quantization.py::TestFXGraphMatcher::test_user_defined_function, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_mul_inputs_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_fp32_simple, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_meth_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_with_only_kwargs_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_extract_weights_linear, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_shadow, test/test_quantization.py::TestFxModelReportObserver::test_observer_after_relu, test/test_quantization.py::TestFxModelReportObserver::test_single_batch_of_ones, test/test_quantization.py::TestFxModelReportClass::test_constructor, test/test_quantization.py::TestFxModelReportClass::test_prepare_model_callibration, test/test_quantization.py::TestFxModelReportClass::test_qconfig_mapping_generation, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen_empty, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_no_match, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_graphs, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_results, test/test_quantization.py::TestSerialization::test_lstm, test/test_quantization.py::TestQuantizeJit::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizeJit::test_observer_with_ignored_function, test/test_quantization.py::TestQuantizeJit::test_single_linear, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_no_fusion, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_trivial, test/test_quantization.py::TestQuantizeJitPasses::test_inplace_option, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_child_qconfig, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_nested_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_reused_weight, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_in_submodule, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_skip_values, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant, test/test_quantization.py::TestQuantizeJitPasses::test_module_list, test/test_quantization.py::TestQuantizeJitPasses::test_quantize_fork_wait, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequant_same_value, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_quantize_for_if, test/test_quantization.py::TestQuantizeJitPasses::test_skip_dequant_constant_prop, test/test_quantization.py::TestQuantizeJitOps::test_dequantize_tuple, test/test_quantization.py::TestQuantizeJitOps::test_group_norm, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNFuncInplaceRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_relu, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_insert_quant_dequant_linear_dynamic, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_prepare_dynamic, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_quantize_dynamic_fp16, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_fuser_method_mappings, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quant_type, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize_jit, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_conv, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantized_dynamic_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_batchnorm, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_functional_modules, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_import, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_qat, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_conv_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_nn_intrinsic_fused, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_convert, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fusion_patterns, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_prepare, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_pt2_traceable_aot_eager_cpu_float8_e5m2 2024-12-18T01:19:14.1996187Z 2024-12-18T01:19:14.9238834Z Running test batch 'tests to run' cost 5293.11 seconds 2024-12-18T01:19:15.7280902Z 2024-12-18T01:19:15.7281578Z real 88m18.173s 2024-12-18T01:19:15.7281879Z user 128m1.711s 2024-12-18T01:19:15.7282107Z sys 12m24.936s 2024-12-18T01:19:15.7282346Z + assert_git_not_dirty 2024-12-18T01:19:15.7296388Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-12-18T01:19:15.7309539Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-12-18T01:19:15.7368462Z ++ git status --porcelain 2024-12-18T01:19:15.7369444Z ++ grep -v '?? third_party' 2024-12-18T01:19:44.4671351Z ++ true 2024-12-18T01:19:44.4731812Z + git_status= 2024-12-18T01:19:44.4732101Z + [[ -n '' ]] 2024-12-18T01:19:44.4737588Z + [[ 1 == 1 ]] 2024-12-18T01:19:44.4737831Z + test_aten 2024-12-18T01:19:44.4749105Z + echo 'Running ATen tests with pytorch lib' 2024-12-18T01:19:44.4749666Z Running ATen tests with pytorch lib 2024-12-18T01:19:44.4749983Z + [[ -n '' ]] 2024-12-18T01:19:44.4750806Z + echo 'Running test with the build folder' 2024-12-18T01:19:44.4751164Z Running test with the build folder 2024-12-18T01:19:44.4751484Z + TEST_BASE_DIR=build/bin 2024-12-18T01:19:44.4751965Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so build/bin 2024-12-18T01:19:44.4798722Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2*' build/bin 2024-12-18T01:19:44.4808912Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin 2024-12-18T01:19:44.4818550Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin 2024-12-18T01:19:44.4830200Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorchbind_test.so build/bin 2024-12-18T01:19:44.4845397Z + ls build/bin 2024-12-18T01:19:44.4919757Z BackoffTest cpu_generator_test 2024-12-18T01:19:44.4920535Z CMakeFiles cpu_profiling_allocator_test 2024-12-18T01:19:44.4921228Z CTestTestfile.cmake cpu_rng_test 2024-12-18T01:19:44.4921872Z CppSignature_test dispatch_key_set_test 2024-12-18T01:19:44.4923173Z Dict_test dlconvertor_test 2024-12-18T01:19:44.4923518Z Dimname_test example_allreduce 2024-12-18T01:19:44.4923879Z FileStoreTest extension_backend_test 2024-12-18T01:19:44.4924328Z HashStoreTest half_test 2024-12-18T01:19:44.4924937Z IListRef_test inline_container_test 2024-12-18T01:19:44.4925385Z KernelFunction_test ivalue_test 2024-12-18T01:19:44.4925757Z List_test kernel_function_legacy_test 2024-12-18T01:19:44.4926116Z Makefile kernel_function_test 2024-12-18T01:19:44.4926486Z MaybeOwned_test kernel_lambda_legacy_test 2024-12-18T01:19:44.4926867Z NamedTensor_test kernel_lambda_test 2024-12-18T01:19:44.4927250Z ProcessGroupGlooTest kernel_stackbased_test 2024-12-18T01:19:44.4927645Z StorageUtils_test lazy_tensor_test 2024-12-18T01:19:44.4927988Z TCPStoreTest legacy_vmap_test 2024-12-18T01:19:44.4928326Z aot_model_compiler_test libc10.so 2024-12-18T01:19:44.4928665Z apply_utils_test 'libcaffe2*' 2024-12-18T01:19:44.4928969Z atest 'libmkldnn*' 2024-12-18T01:19:44.4929265Z backend_fallback_test 'libnccl*' 2024-12-18T01:19:44.4929591Z basic libtorch.so 2024-12-18T01:19:44.4929887Z broadcast_test libtorch_cpu.so 2024-12-18T01:19:44.4930252Z c10_ArrayRef_test libtorch_global_deps.so 2024-12-18T01:19:44.4930622Z c10_Bitset_test libtorch_python.so 2024-12-18T01:19:44.4931042Z c10_CompileTimeFunctionPointer_test libtorchbind_test.so 2024-12-18T01:19:44.4931561Z c10_ConstexprCrc_test make_boxed_from_unboxed_functor_test 2024-12-18T01:19:44.4932032Z c10_DeadlockDetection_test math_kernel_test 2024-12-18T01:19:44.4932430Z c10_DeviceGuard_test memory_format_test 2024-12-18T01:19:44.4932819Z c10_Device_test memory_overlapping_test 2024-12-18T01:19:44.4933211Z c10_DispatchKeySet_test mobile_memory_cleanup 2024-12-18T01:19:44.4951586Z c10_Half_test native_test 2024-12-18T01:19:44.4952087Z c10_InlineDeviceGuard_test op_allowlist_test 2024-12-18T01:19:44.4952527Z c10_InlineStreamGuard_test op_registration_test 2024-12-18T01:19:44.4952962Z c10_LeftRight_test operator_name_test 2024-12-18T01:19:44.4953357Z c10_Metaprogramming_test operators_test 2024-12-18T01:19:44.4953786Z c10_NetworkFlow_test packedtensoraccessor_test 2024-12-18T01:19:44.4954220Z c10_Scalar_test parallel_benchmark 2024-12-18T01:19:44.4954575Z c10_SizesAndStrides_test pow_test 2024-12-18T01:19:44.4954958Z c10_StreamGuard_test protoc 2024-12-18T01:19:44.4955303Z c10_SymInt_test protoc-3.13.0.0 2024-12-18T01:19:44.4955670Z c10_Synchronized_test quantized_test 2024-12-18T01:19:44.4956053Z c10_ThreadLocal_test reduce_ops_test 2024-12-18T01:19:44.4956448Z c10_TypeIndex_test reportMemoryUsage_test 2024-12-18T01:19:44.4956835Z c10_TypeList_test scalar_tensor_test 2024-12-18T01:19:44.4957201Z c10_TypeTraits_test scalar_test 2024-12-18T01:19:44.4957578Z c10_accumulate_test static_runtime_bench 2024-12-18T01:19:44.4957968Z c10_bfloat16_test static_runtime_test 2024-12-18T01:19:44.4958355Z c10_bit_cast_test stride_properties_test 2024-12-18T01:19:44.4958749Z c10_complex_math_test tensor_iterator_test 2024-12-18T01:19:44.4959145Z c10_complex_test test_api 2024-12-18T01:19:44.4959669Z c10_cow_test test_cpp_rpc 2024-12-18T01:19:44.4960003Z c10_error_test test_dist_autograd 2024-12-18T01:19:44.4960395Z c10_exception_test test_edge_op_registration 2024-12-18T01:19:44.4960753Z c10_flags_test test_jit 2024-12-18T01:19:44.4961073Z c10_generic_math_test test_lazy 2024-12-18T01:19:44.4961444Z c10_intrusive_ptr_benchmark test_mobile_nnc 2024-12-18T01:19:44.4961836Z c10_intrusive_ptr_test test_parallel 2024-12-18T01:19:44.4962201Z c10_irange_test test_tensorexpr 2024-12-18T01:19:44.4962581Z c10_lazy_test thread_init_test 2024-12-18T01:19:44.4962930Z c10_logging_test torch_shm_manager 2024-12-18T01:19:44.4963299Z c10_optional_test tutorial_tensorexpr 2024-12-18T01:19:44.4963757Z c10_ordered_preserving_dict_test type_ptr_test 2024-12-18T01:19:44.4964141Z c10_registry_test type_test 2024-12-18T01:19:44.4964498Z c10_small_vector_test undefined_tensor_test 2024-12-18T01:19:44.4964906Z c10_ssize_test vec_test_all_types_AVX2 2024-12-18T01:19:44.4965315Z c10_string_util_test vec_test_all_types_AVX512 2024-12-18T01:19:44.4965751Z c10_string_view_test vec_test_all_types_DEFAULT 2024-12-18T01:19:44.4966165Z c10_tempfile_test verify_api_visibility 2024-12-18T01:19:44.4966519Z c10_typeid_test weakref_test 2024-12-18T01:19:44.4966863Z cmake_install.cmake wrapdim_test 2024-12-18T01:19:44.4967225Z cpu_allocator_test xla_tensor_test 2024-12-18T01:19:44.4967578Z + aten/tools/run_tests.sh build/bin 2024-12-18T01:19:44.4974828Z + set -e 2024-12-18T01:19:44.4977819Z ++ dirname aten/tools/run_tests.sh 2024-12-18T01:19:44.4987103Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-12-18T01:19:44.4987797Z + export CPP_TESTS_DIR=build/bin 2024-12-18T01:19:44.4988268Z + CPP_TESTS_DIR=build/bin 2024-12-18T01:19:44.4988703Z + VALGRIND=ON 2024-12-18T01:19:44.5011341Z + 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 2024-12-18T01:19:44.6044717Z /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 2024-12-18T01:19:44.6045624Z import pkg_resources 2024-12-18T01:19:48.3057973Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-18T01:19:48.3245568Z Found test times from artifacts 2024-12-18T01:19:48.4002718Z Found test times from artifacts 2024-12-18T01:19:48.4024156Z Running all tests 2024-12-18T01:19:48.4028951Z Running parallel tests on 3 processes 2024-12-18T01:19:48.4030620Z Name: tests to run (est. time: 0.0min) 2024-12-18T01:19:48.4030981Z Serial tests (0): 2024-12-18T01:19:48.4031242Z Parallel tests (19): 2024-12-18T01:19:48.4031527Z cpp/Dict_test 1/1 2024-12-18T01:19:48.4031787Z cpp/Dimname_test 1/1 2024-12-18T01:19:48.4032069Z cpp/NamedTensor_test 1/1 2024-12-18T01:19:48.4032370Z cpp/apply_utils_test 1/1 2024-12-18T01:19:48.4032649Z cpp/atest 1/1 2024-12-18T01:19:48.4032890Z cpp/basic 1/1 2024-12-18T01:19:48.4033140Z cpp/broadcast_test 1/1 2024-12-18T01:19:48.4033438Z cpp/cpu_generator_test 1/1 2024-12-18T01:19:48.4033738Z cpp/dlconvertor_test 1/1 2024-12-18T01:19:48.4034035Z cpp/extension_backend_test 1/1 2024-12-18T01:19:48.4034353Z cpp/lazy_tensor_test 1/1 2024-12-18T01:19:48.4034641Z cpp/legacy_vmap_test 1/1 2024-12-18T01:19:48.4034922Z cpp/native_test 1/1 2024-12-18T01:19:48.4035193Z cpp/operators_test 1/1 2024-12-18T01:19:48.4035715Z cpp/scalar_tensor_test 1/1 2024-12-18T01:19:48.4036006Z cpp/scalar_test 1/1 2024-12-18T01:19:48.4036706Z cpp/tensor_iterator_test 1/1 2024-12-18T01:19:48.4037027Z cpp/undefined_tensor_test 1/1 2024-12-18T01:19:48.4037409Z cpp/wrapdim_test 1/1 2024-12-18T01:19:48.4037691Z Name: excluded (est. time: 0.0min) 2024-12-18T01:19:48.4037993Z Serial tests (0): 2024-12-18T01:19:48.4038246Z Parallel tests (0): 2024-12-18T01:19:48.4090327Z Running cpp/Dict_test 1/1 ... [2024-12-18 01:19:48.408695] 2024-12-18T01:19:48.4091065Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:48.4097917Z 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-b7dd639dfe5e1636.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:48.409359] 2024-12-18T01:19:51.2308226Z 2024-12-18T01:19:51.2309548Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_4d9c38d8708f01c9_.log 2024-12-18T01:19:51.2310630Z 2024-12-18T01:19:51.2310942Z Running cpp/Dimname_test 1/1 ... [2024-12-18 01:19:51.230801] 2024-12-18T01:19:51.2311600Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:51.2316861Z 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-8b20096dbadd4744.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:51.231315] 2024-12-18T01:19:52.9994760Z 2024-12-18T01:19:52.9995836Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_6812915842b741c6_.log 2024-12-18T01:19:52.9996486Z 2024-12-18T01:19:52.9996712Z Running cpp/NamedTensor_test 1/1 ... [2024-12-18 01:19:52.999339] 2024-12-18T01:19:52.9997153Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:52.9999666Z 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-a3afb3805fcfc5a1.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:52.999721] 2024-12-18T01:19:54.6671386Z 2024-12-18T01:19:54.6672466Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_bb518507a02536c5_.log 2024-12-18T01:19:54.6673161Z 2024-12-18T01:19:54.6673371Z Running cpp/apply_utils_test 1/1 ... [2024-12-18 01:19:54.666990] 2024-12-18T01:19:54.6673806Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:54.6676891Z 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-277b5488b4791f58.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:54.667420] 2024-12-18T01:19:56.2850666Z 2024-12-18T01:19:56.2851964Z 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_5e6b46476a76d4dc_.log 2024-12-18T01:19:56.2852649Z 2024-12-18T01:19:56.2852812Z Running cpp/atest 1/1 ... [2024-12-18 01:19:56.284928] 2024-12-18T01:19:56.2853199Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:56.2855635Z 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-07f33269f045b59d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:56.285293] 2024-12-18T01:19:57.8524140Z 2024-12-18T01:19:57.8525004Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_22a77473e51ac1b5_.log 2024-12-18T01:19:57.8525605Z 2024-12-18T01:19:57.8525767Z Running cpp/basic 1/1 ... [2024-12-18 01:19:57.852266] 2024-12-18T01:19:57.8526154Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:57.8528548Z 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-ad29fb30fe3b49e5.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:57.852611] 2024-12-18T01:19:59.4699556Z 2024-12-18T01:19:59.4700430Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_0d4d1a2d04bf4e20_.log 2024-12-18T01:19:59.4701029Z 2024-12-18T01:19:59.4701232Z Running cpp/broadcast_test 1/1 ... [2024-12-18 01:19:59.469823] 2024-12-18T01:19:59.4701662Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:59.4704631Z 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-04c29594d44c249a.xml', '-x', '--reruns=2'] ... [2024-12-18 01:19:59.470196] 2024-12-18T01:20:01.0872656Z 2024-12-18T01:20:01.0873612Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_9549c0c904c3286a_.log 2024-12-18T01:20:01.0874311Z 2024-12-18T01:20:01.0874536Z Running cpp/cpu_generator_test 1/1 ... [2024-12-18 01:20:01.087140] 2024-12-18T01:20:01.0874973Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:01.0877520Z 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-af35b8c3ec567d81.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:01.087522] 2024-12-18T01:20:02.7047105Z 2024-12-18T01:20:02.7048143Z 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_ecc129d2cd2d0dcf_.log 2024-12-18T01:20:02.7048829Z 2024-12-18T01:20:02.7049071Z Running cpp/dlconvertor_test 1/1 ... [2024-12-18 01:20:02.704548] 2024-12-18T01:20:02.7049510Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:02.7051788Z 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-cd97053eced799e6.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:02.704916] 2024-12-18T01:20:04.3219306Z 2024-12-18T01:20:04.3220315Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_57fa8a83782ee4eb_.log 2024-12-18T01:20:04.3221011Z 2024-12-18T01:20:04.3221514Z Running cpp/extension_backend_test 1/1 ... [2024-12-18 01:20:04.321780] 2024-12-18T01:20:04.3221987Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:04.3223710Z 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-ff6deb65abf71caa.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:04.322122] 2024-12-18T01:20:05.9393299Z 2024-12-18T01:20:05.9394379Z 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_2275e45e4c073e05_.log 2024-12-18T01:20:05.9395098Z 2024-12-18T01:20:05.9395320Z Running cpp/lazy_tensor_test 1/1 ... [2024-12-18 01:20:05.939194] 2024-12-18T01:20:05.9395757Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:05.9398161Z 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-251085995457cace.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:05.939553] 2024-12-18T01:20:07.5567861Z 2024-12-18T01:20:07.5568877Z 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_51dfb149da1feaed_.log 2024-12-18T01:20:07.5569608Z 2024-12-18T01:20:07.5570425Z Running cpp/legacy_vmap_test 1/1 ... [2024-12-18 01:20:07.556611] 2024-12-18T01:20:07.5571010Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:07.5572367Z 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-e397fe25039e3695.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:07.556949] 2024-12-18T01:20:09.1237955Z 2024-12-18T01:20:09.1238924Z 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_fdb02a29c8d65cda_.log 2024-12-18T01:20:09.1240221Z 2024-12-18T01:20:09.1240418Z Running cpp/native_test 1/1 ... [2024-12-18 01:20:09.123667] 2024-12-18T01:20:09.1240967Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:09.1243473Z 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-2228c153a05cbaa9.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:09.124028] 2024-12-18T01:20:10.6911564Z 2024-12-18T01:20:10.6912543Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_1830b2e7dac2c62e_.log 2024-12-18T01:20:10.6913180Z 2024-12-18T01:20:10.6913384Z Running cpp/operators_test 1/1 ... [2024-12-18 01:20:10.691046] 2024-12-18T01:20:10.6913809Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:10.6916697Z 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-19bcd5cf387fe9d0.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:10.691393] 2024-12-18T01:20:12.2581817Z 2024-12-18T01:20:12.2582802Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_1f756e1a6c2dcbcd_.log 2024-12-18T01:20:12.2583471Z 2024-12-18T01:20:12.2583703Z Running cpp/scalar_tensor_test 1/1 ... [2024-12-18 01:20:12.258017] 2024-12-18T01:20:12.2584156Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:12.2586431Z 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-109c4b09b14157fa.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:12.258383] 2024-12-18T01:20:13.8251228Z 2024-12-18T01:20:13.8252554Z 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_eb30e41ca2dac77c_.log 2024-12-18T01:20:13.8253249Z 2024-12-18T01:20:13.8253438Z Running cpp/scalar_test 1/1 ... [2024-12-18 01:20:13.824982] 2024-12-18T01:20:13.8253908Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:13.8256779Z 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-36f9c4369ef9cf1e.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:13.825339] 2024-12-18T01:20:15.4421775Z 2024-12-18T01:20:15.4422784Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_b0f6d87eb8cb2d6d_.log 2024-12-18T01:20:15.4423464Z 2024-12-18T01:20:15.4423700Z Running cpp/tensor_iterator_test 1/1 ... [2024-12-18 01:20:15.442051] 2024-12-18T01:20:15.4424152Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:15.4426265Z 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-c453420a9c9c84df.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:15.442387] 2024-12-18T01:20:17.0597459Z 2024-12-18T01:20:17.0599118Z 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_65b512fadcf0ad44_.log 2024-12-18T01:20:17.0600823Z 2024-12-18T01:20:17.0601248Z Running cpp/undefined_tensor_test 1/1 ... [2024-12-18 01:20:17.059534] 2024-12-18T01:20:17.0602032Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:17.0603628Z 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-5139e25f4056ee5d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:17.059923] 2024-12-18T01:20:18.6268781Z 2024-12-18T01:20:18.6270988Z 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_695ad38b23bf7c51_.log 2024-12-18T01:20:18.6271811Z 2024-12-18T01:20:18.6272105Z Running cpp/wrapdim_test 1/1 ... [2024-12-18 01:20:18.626749] 2024-12-18T01:20:18.6272751Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:18.6274162Z 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-cebe1f73e17e995f.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:18.627110] 2024-12-18T01:20:20.2444299Z 2024-12-18T01:20:20.2445694Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_db18d1210ba92d93_.log 2024-12-18T01:20:20.2446880Z 2024-12-18T01:20:20.2456966Z Running cpp/Dict_test 1/1 ... [2024-12-18 01:20:20.245437] 2024-12-18T01:20:20.2457441Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:20.2459093Z Running cpp/Dimname_test 1/1 ... [2024-12-18 01:20:20.245691] 2024-12-18T01:20:20.2459561Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:20.2459988Z Running cpp/NamedTensor_test 1/1 ... [2024-12-18 01:20:20.245738] 2024-12-18T01:20:20.2460419Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:20.2462326Z 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-56a507efd107c3e3.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:20.245991] 2024-12-18T01:20:20.2465774Z 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-418bb873436e72b8.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:20.246295] 2024-12-18T01:20:20.2468755Z 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-30ac5116bfddb8d0.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:20.246324] 2024-12-18T01:20:24.3689289Z 2024-12-18T01:20:24.3694663Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_43a194d9e62ffd9b_.log 2024-12-18T01:20:24.3695841Z 2024-12-18T01:20:25.0204150Z 2024-12-18T01:20:25.0205389Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_5ed5a8019843b703_.log 2024-12-18T01:20:25.0206244Z 2024-12-18T01:20:28.2636457Z Running cpp/apply_utils_test 1/1 ... [2024-12-18 01:20:28.263096] 2024-12-18T01:20:28.2637173Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:28.2641147Z 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-da10a04d79983791.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:28.263548] 2024-12-18T01:20:28.8961281Z Running cpp/atest 1/1 ... [2024-12-18 01:20:28.895592] 2024-12-18T01:20:28.8961929Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:28.8966877Z 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-2bc5e4871b4ceb6a.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:28.896253] 2024-12-18T01:20:32.1337483Z 2024-12-18T01:20:32.1339356Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_c13e0d3ffb416dd3_.log 2024-12-18T01:20:32.1340718Z 2024-12-18T01:20:32.2357359Z 2024-12-18T01:20:32.2359211Z 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_748cdbbb8d4b1e78_.log 2024-12-18T01:20:32.2360897Z 2024-12-18T01:20:34.5220165Z 2024-12-18T01:20:34.5221549Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_a90cb4c0e921e7fb_.log 2024-12-18T01:20:34.5222600Z 2024-12-18T01:20:35.8948219Z Running cpp/basic 1/1 ... [2024-12-18 01:20:35.894410] 2024-12-18T01:20:35.8948739Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:35.8951814Z 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-8b6d73e1c6a61abf.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:35.894899] 2024-12-18T01:20:36.1924915Z Running cpp/broadcast_test 1/1 ... [2024-12-18 01:20:36.192068] 2024-12-18T01:20:36.1925664Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:36.1928562Z 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-62edde8cb9479758.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:36.192517] 2024-12-18T01:20:38.4709034Z Running cpp/cpu_generator_test 1/1 ... [2024-12-18 01:20:38.470491] 2024-12-18T01:20:38.4709889Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:38.4714076Z 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-3c7cc3aecbf3d0bd.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:38.471009] 2024-12-18T01:20:39.1126003Z 2024-12-18T01:20:39.1127420Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_648fdab38fdec1d2_.log 2024-12-18T01:20:39.1128098Z 2024-12-18T01:20:39.1150177Z 2024-12-18T01:20:39.1151648Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_9cf86b650e4bff87_.log 2024-12-18T01:20:39.1153442Z 2024-12-18T01:20:42.9269443Z Running cpp/dlconvertor_test 1/1 ... [2024-12-18 01:20:42.926512] 2024-12-18T01:20:42.9270321Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:42.9274609Z 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-c66a71ca85f10742.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:42.927025] 2024-12-18T01:20:43.1022527Z 2024-12-18T01:20:43.1023946Z 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_c2925e26a951111b_.log 2024-12-18T01:20:43.1025194Z 2024-12-18T01:20:43.2442077Z Running cpp/extension_backend_test 1/1 ... [2024-12-18 01:20:43.243815] 2024-12-18T01:20:43.2442833Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:43.2446820Z 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-51995522a5d16267.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:43.244308] 2024-12-18T01:20:45.6480850Z 2024-12-18T01:20:45.6482103Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_14893e318d1850b1_.log 2024-12-18T01:20:45.6483159Z 2024-12-18T01:20:45.8638447Z 2024-12-18T01:20:45.8640241Z 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_111e4e95e8767b27_.log 2024-12-18T01:20:47.1467611Z 2024-12-18T01:20:47.1468542Z Running cpp/lazy_tensor_test 1/1 ... [2024-12-18 01:20:47.146380] 2024-12-18T01:20:47.1469344Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:47.1472521Z 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-81c1f855ef1e79fb.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:47.146859] 2024-12-18T01:20:49.3658129Z 2024-12-18T01:20:49.3659571Z 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_ba771a70c37678f4_.log 2024-12-18T01:20:49.3660794Z 2024-12-18T01:20:49.5175313Z Running cpp/legacy_vmap_test 1/1 ... [2024-12-18 01:20:49.517116] 2024-12-18T01:20:49.5176155Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:49.5179673Z 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-63d802a9e893c345.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:49.517579] 2024-12-18T01:20:49.5427249Z Running cpp/native_test 1/1 ... [2024-12-18 01:20:49.542326] 2024-12-18T01:20:49.5427945Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:49.5431843Z 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-ec1aad414c7d1d70.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:49.542790] 2024-12-18T01:20:52.5129430Z 2024-12-18T01:20:52.5130800Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_0452fd6117696b61_.log 2024-12-18T01:20:52.5131922Z 2024-12-18T01:20:53.3890761Z Running cpp/operators_test 1/1 ... [2024-12-18 01:20:53.388588] 2024-12-18T01:20:53.3891577Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:53.3895229Z 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-1c4f12e6f3b7b55b.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:53.389110] 2024-12-18T01:20:55.9443607Z 2024-12-18T01:20:55.9444915Z 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_d662032f8a7e0b14_.log 2024-12-18T01:20:55.9445933Z 2024-12-18T01:20:56.5594771Z 2024-12-18T01:20:56.5596116Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_a0af9380b5079537_.log 2024-12-18T01:20:56.5597298Z 2024-12-18T01:20:56.8318195Z Running cpp/scalar_tensor_test 1/1 ... [2024-12-18 01:20:56.831394] 2024-12-18T01:20:56.8319021Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:56.8322067Z 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-7f13b38fe35e0d4d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:56.831859] 2024-12-18T01:20:59.4509753Z 2024-12-18T01:20:59.4511582Z 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_68bef77bbad1c84b_.log 2024-12-18T01:20:59.4512305Z 2024-12-18T01:20:59.7744946Z Running cpp/scalar_test 1/1 ... [2024-12-18 01:20:59.774099] 2024-12-18T01:20:59.7745391Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:20:59.7748511Z 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-96861027bb9adfae.xml', '-x', '--reruns=2'] ... [2024-12-18 01:20:59.774550] 2024-12-18T01:21:00.6124582Z Running cpp/tensor_iterator_test 1/1 ... [2024-12-18 01:21:00.611856] 2024-12-18T01:21:00.6125506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:21:00.6131262Z 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-df8a6cda490652f8.xml', '-x', '--reruns=2'] ... [2024-12-18 01:21:00.612444] 2024-12-18T01:21:02.8949091Z 2024-12-18T01:21:02.8950589Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_cf33e96555ef83b1_.log 2024-12-18T01:21:02.8951677Z 2024-12-18T01:21:03.5022205Z Running cpp/undefined_tensor_test 1/1 ... [2024-12-18 01:21:03.501681] 2024-12-18T01:21:03.5023173Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:21:03.5027225Z 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-7dc6775799e2e3b0.xml', '-x', '--reruns=2'] ... [2024-12-18 01:21:03.502239] 2024-12-18T01:21:06.0729722Z 2024-12-18T01:21:06.0731733Z 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_33bc2d27e97c4567_.log 2024-12-18T01:21:06.0732997Z 2024-12-18T01:21:06.9108787Z Running cpp/wrapdim_test 1/1 ... [2024-12-18 01:21:06.910436] 2024-12-18T01:21:06.9109463Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:21:06.9113693Z 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-4bc77ae90fc8fe4a.xml', '-x', '--reruns=2'] ... [2024-12-18 01:21:06.910967] 2024-12-18T01:21:09.5809293Z 2024-12-18T01:21:09.5811261Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_7ea60a7b27e2b7b1_.log 2024-12-18T01:21:09.5812438Z 2024-12-18T01:21:13.5595490Z 2024-12-18T01:21:13.5597047Z 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_edb54dcad4a30f12_.log 2024-12-18T01:21:13.5598376Z 2024-12-18T01:21:14.2810032Z Running test batch 'tests to run' cost 85.88 seconds 2024-12-18T01:21:14.8871481Z + run_if_exists tensor_interop_test 2024-12-18T01:21:14.8872170Z + local test_name=tensor_interop_test 2024-12-18T01:21:14.8872764Z + [[ -x build/bin/tensor_interop_test ]] 2024-12-18T01:21:14.8873408Z + echo 'Warning: tensor_interop_test does not exist.' 2024-12-18T01:21:14.8874160Z Warning: tensor_interop_test does not exist. 2024-12-18T01:21:14.8874768Z + run_if_exists cudnn_test 2024-12-18T01:21:14.8875264Z + local test_name=cudnn_test 2024-12-18T01:21:14.8875771Z + [[ -x build/bin/cudnn_test ]] 2024-12-18T01:21:14.8876306Z + echo 'Warning: cudnn_test does not exist.' 2024-12-18T01:21:14.8876903Z Warning: cudnn_test does not exist. 2024-12-18T01:21:14.8877448Z + run_if_exists cuda_generator_test 2024-12-18T01:21:14.8878032Z + local test_name=cuda_generator_test 2024-12-18T01:21:14.8878559Z + [[ -x build/bin/cuda_generator_test ]] 2024-12-18T01:21:14.8879119Z + echo 'Warning: cuda_generator_test does not exist.' 2024-12-18T01:21:14.8879786Z Warning: cuda_generator_test does not exist. 2024-12-18T01:21:14.8880311Z + run_if_exists apply_test 2024-12-18T01:21:14.8880753Z + local test_name=apply_test 2024-12-18T01:21:14.8881226Z + [[ -x build/bin/apply_test ]] 2024-12-18T01:21:14.8881757Z + echo 'Warning: apply_test does not exist.' 2024-12-18T01:21:14.8882181Z Warning: apply_test does not exist. 2024-12-18T01:21:14.8882765Z + run_if_exists stream_test 2024-12-18T01:21:14.8883054Z + local test_name=stream_test 2024-12-18T01:21:14.8883351Z + [[ -x build/bin/stream_test ]] 2024-12-18T01:21:14.8883665Z + echo 'Warning: stream_test does not exist.' 2024-12-18T01:21:14.8884015Z Warning: stream_test does not exist. 2024-12-18T01:21:14.8884332Z + run_if_exists cuda_half_test 2024-12-18T01:21:14.8884631Z + local test_name=cuda_half_test 2024-12-18T01:21:14.8884941Z + [[ -x build/bin/cuda_half_test ]] 2024-12-18T01:21:14.8885274Z + echo 'Warning: cuda_half_test does not exist.' 2024-12-18T01:21:14.8885641Z Warning: cuda_half_test does not exist. 2024-12-18T01:21:14.8886035Z + run_if_exists cuda_vectorized_test 2024-12-18T01:21:14.8886370Z + local test_name=cuda_vectorized_test 2024-12-18T01:21:14.8886793Z + [[ -x build/bin/cuda_vectorized_test ]] 2024-12-18T01:21:14.8887182Z + echo 'Warning: cuda_vectorized_test does not exist.' 2024-12-18T01:21:14.8887575Z Warning: cuda_vectorized_test does not exist. 2024-12-18T01:21:14.8887940Z + run_if_exists cuda_distributions_test 2024-12-18T01:21:14.8888285Z + local test_name=cuda_distributions_test 2024-12-18T01:21:14.8888643Z + [[ -x build/bin/cuda_distributions_test ]] 2024-12-18T01:21:14.8889044Z + echo 'Warning: cuda_distributions_test does not exist.' 2024-12-18T01:21:14.8889460Z Warning: cuda_distributions_test does not exist. 2024-12-18T01:21:14.8889827Z + run_if_exists cuda_optional_test 2024-12-18T01:21:14.8890148Z + local test_name=cuda_optional_test 2024-12-18T01:21:14.8890483Z + [[ -x build/bin/cuda_optional_test ]] 2024-12-18T01:21:14.8890858Z + echo 'Warning: cuda_optional_test does not exist.' 2024-12-18T01:21:14.8891236Z Warning: cuda_optional_test does not exist. 2024-12-18T01:21:14.8891590Z + run_if_exists cuda_tensor_interop_test 2024-12-18T01:21:14.8891941Z + local test_name=cuda_tensor_interop_test 2024-12-18T01:21:14.8892298Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2024-12-18T01:21:14.8892700Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2024-12-18T01:21:14.8893122Z Warning: cuda_tensor_interop_test does not exist. 2024-12-18T01:21:14.8893488Z + run_if_exists cuda_complex_test 2024-12-18T01:21:14.8893801Z + local test_name=cuda_complex_test 2024-12-18T01:21:14.8894122Z + [[ -x build/bin/cuda_complex_test ]] 2024-12-18T01:21:14.8894485Z + echo 'Warning: cuda_complex_test does not exist.' 2024-12-18T01:21:14.8894855Z Warning: cuda_complex_test does not exist. 2024-12-18T01:21:14.8895204Z + run_if_exists cuda_complex_math_test 2024-12-18T01:21:14.8895535Z + local test_name=cuda_complex_math_test 2024-12-18T01:21:14.8895879Z + [[ -x build/bin/cuda_complex_math_test ]] 2024-12-18T01:21:14.8896273Z + echo 'Warning: cuda_complex_math_test does not exist.' 2024-12-18T01:21:14.8896673Z Warning: cuda_complex_math_test does not exist. 2024-12-18T01:21:14.8897030Z + run_if_exists cuda_cub_test 2024-12-18T01:21:14.8897327Z + local test_name=cuda_cub_test 2024-12-18T01:21:14.8897626Z + [[ -x build/bin/cuda_cub_test ]] 2024-12-18T01:21:14.8897964Z + echo 'Warning: cuda_cub_test does not exist.' 2024-12-18T01:21:14.8898307Z Warning: cuda_cub_test does not exist. 2024-12-18T01:21:14.8898636Z + run_if_exists cuda_atomic_ops_test 2024-12-18T01:21:14.8898958Z + local test_name=cuda_atomic_ops_test 2024-12-18T01:21:14.8899289Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2024-12-18T01:21:14.8899666Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2024-12-18T01:21:14.8900064Z Warning: cuda_atomic_ops_test does not exist. 2024-12-18T01:21:14.8900389Z + '[' ON == ON ']' 2024-12-18T01:21:14.8901085Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-12-18T01:21:14.9204644Z ==5300== Memcheck, a memory error detector 2024-12-18T01:21:14.9205138Z ==5300== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-12-18T01:21:14.9205711Z ==5300== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-12-18T01:21:14.9206208Z ==5300== Command: build/bin/basic --gtest_filter=-*CUDA 2024-12-18T01:21:14.9206854Z ==5300== 2024-12-18T01:21:15.4803084Z ==5300== Warning: set address range perms: large range [0x4a08000, 0x1548c000) (defined) 2024-12-18T01:21:43.3118517Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-12-18T01:21:43.3392373Z Note: Google Test filter = -*CUDA 2024-12-18T01:21:43.3438772Z [==========] Running 4 tests from 1 test suite. 2024-12-18T01:21:43.3465643Z [----------] Global test environment set-up. 2024-12-18T01:21:43.3535501Z [----------] 4 tests from BasicTest 2024-12-18T01:21:43.3559646Z [ RUN ] BasicTest.BasicTestCPU 2024-12-18T01:21:44.7763704Z 386 ms 2024-12-18T01:21:44.8599254Z 53 ms 2024-12-18T01:21:44.9332183Z 65 ms 2024-12-18T01:21:45.5789927Z [ OK ] BasicTest.BasicTestCPU (2220 ms) 2024-12-18T01:21:45.5798298Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-12-18T01:21:45.7099973Z 85 ms 2024-12-18T01:21:45.7607199Z 45 ms 2024-12-18T01:21:45.8274815Z 64 ms 2024-12-18T01:21:45.8813848Z [ OK ] BasicTest.BasicTestHalfCPU (300 ms) 2024-12-18T01:21:45.8814333Z [ RUN ] BasicTest.FactoryMethodsTest 2024-12-18T01:21:45.9204944Z [ OK ] BasicTest.FactoryMethodsTest (38 ms) 2024-12-18T01:21:45.9205419Z [ RUN ] BasicTest.BasicStdTestCPU 2024-12-18T01:21:46.0455503Z Simple example: called once 2024-12-18T01:21:46.0958839Z throw: call_once will retry 2024-12-18T01:21:46.1379931Z throw: call_once will retry 2024-12-18T01:21:46.1388143Z Didn't throw, call_once will not attempt again 2024-12-18T01:21:46.1409910Z [ OK ] BasicTest.BasicStdTestCPU (220 ms) 2024-12-18T01:21:46.1433565Z [----------] 4 tests from BasicTest (2786 ms total) 2024-12-18T01:21:46.1434056Z 2024-12-18T01:21:46.1448288Z [----------] Global test environment tear-down 2024-12-18T01:21:46.1480030Z [==========] 4 tests from 1 test suite ran. (2811 ms total) 2024-12-18T01:21:46.1490895Z [ PASSED ] 4 tests. 2024-12-18T01:21:48.0190734Z ==5300== 2024-12-18T01:21:48.0194378Z ==5300== HEAP SUMMARY: 2024-12-18T01:21:48.0194878Z ==5300== in use at exit: 240,168 bytes in 3,998 blocks 2024-12-18T01:21:48.0195453Z ==5300== total heap usage: 740,937 allocs, 736,939 frees, 214,854,752 bytes allocated 2024-12-18T01:21:48.0196292Z ==5300== 2024-12-18T01:21:48.0571531Z ==5300== LEAK SUMMARY: 2024-12-18T01:21:48.0572045Z ==5300== definitely lost: 0 bytes in 0 blocks 2024-12-18T01:21:48.0572435Z ==5300== indirectly lost: 0 bytes in 0 blocks 2024-12-18T01:21:48.0572802Z ==5300== possibly lost: 0 bytes in 0 blocks 2024-12-18T01:21:48.0573198Z ==5300== still reachable: 240,168 bytes in 3,998 blocks 2024-12-18T01:21:48.0573610Z ==5300== suppressed: 0 bytes in 0 blocks 2024-12-18T01:21:48.0574056Z ==5300== Rerun with --leak-check=full to see details of leaked memory 2024-12-18T01:21:48.0574478Z ==5300== 2024-12-18T01:21:48.0574804Z ==5300== For lists of detected and suppressed errors, rerun with: -s 2024-12-18T01:21:48.0575321Z ==5300== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-12-18T01:21:48.1032729Z + [[ -x build/bin/tensor_interop_test ]] 2024-12-18T01:21:48.1034332Z + [[ -n '' ]] 2024-12-18T01:21:48.1034577Z + assert_git_not_dirty 2024-12-18T01:21:48.1034961Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-12-18T01:21:48.1035318Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-12-18T01:21:48.1041284Z ++ git status --porcelain 2024-12-18T01:21:48.1042270Z ++ grep -v '?? third_party' 2024-12-18T01:21:48.2988421Z ++ true 2024-12-18T01:21:48.2989414Z + git_status= 2024-12-18T01:21:48.2989676Z + [[ -n '' ]] 2024-12-18T01:21:48.3035449Z + cleanup_workspace 2024-12-18T01:21:48.3036180Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-12-18T01:21:48.3037081Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-12-18T01:21:48.3037728Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-12-18T01:21:48.3038467Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-12-18T01:21:48.3039029Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-12-18T01:21:48.3039644Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-12-18T01:21:48.3040128Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-12-18T01:21:51.1732260Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@release/2.6 2024-12-18T01:21:51.1732783Z with: 2024-12-18T01:21:51.1733046Z benchmark-results-dir: test/test-reports 2024-12-18T01:21:51.1733380Z dry-run: false 2024-12-18T01:21:51.1733626Z schema-version: v3 2024-12-18T01:21:51.1734091Z github-token: *** 2024-12-18T01:21:51.1734338Z env: 2024-12-18T01:21:51.1734547Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:51.1735030Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:51.1735542Z ##[endgroup] 2024-12-18T01:21:51.1762667Z ##[group]Run set -eux 2024-12-18T01:21:51.1762981Z set -eux 2024-12-18T01:21:51.1763252Z python3 -mpip install boto3==1.35.33 2024-12-18T01:21:51.1796522Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:51.1796927Z env: 2024-12-18T01:21:51.1797159Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:51.1797693Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:51.1798196Z ##[endgroup] 2024-12-18T01:21:51.1827855Z + python3 -mpip install boto3==1.35.33 2024-12-18T01:21:51.5510167Z Defaulting to user installation because normal site-packages is not writeable 2024-12-18T01:21:51.5706003Z Requirement already satisfied: boto3==1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (1.35.33) 2024-12-18T01:21:51.5757801Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.4) 2024-12-18T01:21:51.5761817Z Requirement already satisfied: botocore<1.36.0,>=1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (1.35.83) 2024-12-18T01:21:51.5766087Z 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) 2024-12-18T01:21:51.5822543Z 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) 2024-12-18T01:21:51.5828447Z 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) 2024-12-18T01:21:51.5867703Z 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) 2024-12-18T01:21:51.7116516Z ##[group]Run set -eux 2024-12-18T01:21:51.7132811Z set -eux 2024-12-18T01:21:51.7133091Z  2024-12-18T01:21:51.7133375Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2024-12-18T01:21:51.7133784Z  echo "Missing github-token input" 2024-12-18T01:21:51.7134123Z  exit 1 2024-12-18T01:21:51.7134348Z fi 2024-12-18T01:21:51.7140228Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:51.7140631Z env: 2024-12-18T01:21:51.7140860Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:51.7141354Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:51.7142163Z GITHUB_TOKEN: *** 2024-12-18T01:21:51.7142515Z ##[endgroup] 2024-12-18T01:21:51.7170659Z + [[ -z *** ]] 2024-12-18T01:21:51.7249280Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2024-12-18T01:21:51.7249731Z with: 2024-12-18T01:21:51.7250090Z github-token: *** 2024-12-18T01:21:51.7250319Z env: 2024-12-18T01:21:51.7250545Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:51.7251022Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:51.7251533Z ##[endgroup] 2024-12-18T01:21:51.7275190Z ##[group]Run set -eux 2024-12-18T01:21:51.7275472Z set -eux 2024-12-18T01:21:51.7275716Z  2024-12-18T01:21:51.7276315Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-12-18T01:21:51.7283003Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:51.7283406Z env: 2024-12-18T01:21:51.7283643Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:51.7284128Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:51.7284870Z GITHUB_TOKEN: *** 2024-12-18T01:21:51.7285125Z ##[endgroup] 2024-12-18T01:21:51.7313941Z + 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-0c373a2e3f7bf6e7f 2024-12-18T01:21:54.9928276Z setting job-id=34566046822 2024-12-18T01:21:54.9928829Z setting job-name=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-18T01:21:55.0044861Z ##[group]Run set -eux 2024-12-18T01:21:55.0045166Z set -eux 2024-12-18T01:21:55.0045408Z  2024-12-18T01:21:55.0045815Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2024-12-18T01:21:55.0046368Z  --schema-version "${SCHEMA_VERSION}" \ 2024-12-18T01:21:55.0046732Z  --repo "${REPO}" \ 2024-12-18T01:21:55.0047047Z  --head-branch "${HEAD_BRANCH}" \ 2024-12-18T01:21:55.0047393Z  --head-sha "${HEAD_SHA}" \ 2024-12-18T01:21:55.0047747Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2024-12-18T01:21:55.0048106Z  --run-attempt "${RUN_ATTEMPT}" \ 2024-12-18T01:21:55.0048447Z  --job-id "${JOB_ID}" \ 2024-12-18T01:21:55.0048761Z  --job-name "${JOB_NAME}" 2024-12-18T01:21:55.0057776Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.0058178Z env: 2024-12-18T01:21:55.0058415Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.0058901Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.0059422Z SCHEMA_VERSION: v3 2024-12-18T01:21:55.0059690Z REPO: pytorch/pytorch 2024-12-18T01:21:55.0059977Z HEAD_BRANCH: refs/heads/release/2.6 2024-12-18T01:21:55.0060350Z HEAD_SHA: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-18T01:21:55.0060699Z WORKFLOW_RUN_ID: 12383255652 2024-12-18T01:21:55.0060981Z RUN_ATTEMPT: 1 2024-12-18T01:21:55.0061226Z JOB_ID: 34566046822 2024-12-18T01:21:55.0061644Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-18T01:21:55.0062130Z ##[endgroup] 2024-12-18T01:21:55.0086871Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/release/2.6/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/release/2.6 --head-sha 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 --workflow-id 12383255652 --run-attempt 1 --job-id 34566046822 --job-name 'linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)' 2024-12-18T01:21:55.0418541Z ##[group]Run set -eux 2024-12-18T01:21:55.0418832Z set -eux 2024-12-18T01:21:55.0419057Z  2024-12-18T01:21:55.0419336Z # TODO (huydhn): Implement this part 2024-12-18T01:21:55.0419721Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2024-12-18T01:21:55.0425474Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.0425895Z env: 2024-12-18T01:21:55.0426125Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.0426602Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.0427118Z ##[endgroup] 2024-12-18T01:21:55.0449054Z + echo 'runners=[]' 2024-12-18T01:21:55.0476281Z ##[group]Run set -eux 2024-12-18T01:21:55.0476558Z set -eux 2024-12-18T01:21:55.0476786Z  2024-12-18T01:21:55.0477052Z # TODO (huydhn): Implement this part 2024-12-18T01:21:55.0477596Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2024-12-18T01:21:55.0483202Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.0483596Z env: 2024-12-18T01:21:55.0483825Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.0484417Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.0484960Z ##[endgroup] 2024-12-18T01:21:55.0506148Z + echo 'dependencies={}' 2024-12-18T01:21:55.0544487Z ##[group]Run set -eux 2024-12-18T01:21:55.0544955Z set -eux 2024-12-18T01:21:55.0545352Z  2024-12-18T01:21:55.0545802Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2024-12-18T01:21:55.0546584Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2024-12-18T01:21:55.0547445Z  # We don't want the job to fail if the directory doesn't exist 2024-12-18T01:21:55.0548148Z  exit 0 2024-12-18T01:21:55.0548635Z fi 2024-12-18T01:21:55.0548996Z  2024-12-18T01:21:55.0549392Z if [[ "${DRY_RUN}" == "true" ]]; then 2024-12-18T01:21:55.0550176Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2024-12-18T01:21:55.0551166Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2024-12-18T01:21:55.0551954Z  --metadata "${BENCHMARK_METADATA}" \ 2024-12-18T01:21:55.0552576Z  --runners "${RUNNER_INFO}" \ 2024-12-18T01:21:55.0553184Z  --dependencies "${DEPENDENCIES}" \ 2024-12-18T01:21:55.0553763Z  --dry-run 2024-12-18T01:21:55.0554174Z else 2024-12-18T01:21:55.0554847Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2024-12-18T01:21:55.0555777Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2024-12-18T01:21:55.0556485Z  --metadata "${BENCHMARK_METADATA}" \ 2024-12-18T01:21:55.0557076Z  --runners "${RUNNER_INFO}" \ 2024-12-18T01:21:55.0557676Z  --dependencies "${DEPENDENCIES}" 2024-12-18T01:21:55.0558215Z fi 2024-12-18T01:21:55.0565643Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.0566291Z env: 2024-12-18T01:21:55.0566635Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.0567465Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.0568357Z BENCHMARK_RESULTS_DIR: test/test-reports 2024-12-18T01:21:55.0568882Z DRY_RUN: false 2024-12-18T01:21:55.0571323Z BENCHMARK_METADATA: {"timestamp": 1734484915, "schema_version": "v3", "name": "linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/release/2.6", "head_sha": "0cdf8b1d09254cfda66191d1bd01e3041c3c76f7", "workflow_id": 12383255652, "run_attempt": 1, "job_id": 34566046822} 2024-12-18T01:21:55.0573997Z RUNNER_INFO: [] 2024-12-18T01:21:55.0574425Z DEPENDENCIES: {} 2024-12-18T01:21:55.0574853Z ##[endgroup] 2024-12-18T01:21:55.0600656Z + [[ ! -d test/test-reports ]] 2024-12-18T01:21:55.0600994Z + [[ false == \t\r\u\e ]] 2024-12-18T01:21:55.0603599Z + 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": 1734484915, "schema_version": "v3", "name": "linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/release/2.6", "head_sha": "0cdf8b1d09254cfda66191d1bd01e3041c3c76f7", "workflow_id": 12383255652, "run_attempt": 1, "job_id": 34566046822}' --runners '[]' --dependencies '{}' 2024-12-18T01:21:55.3173814Z ##[group]Run cat test/**/*_toprint.log || true 2024-12-18T01:21:55.3174244Z cat test/**/*_toprint.log || true 2024-12-18T01:21:55.3179774Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.3180310Z env: 2024-12-18T01:21:55.3180544Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.3181026Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.3181550Z ##[endgroup] 2024-12-18T01:21:55.3246589Z cat: 'test/**/*_toprint.log': No such file or directory 2024-12-18T01:21:55.3281270Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-12-18T01:21:55.3281632Z kill "$MONITOR_SCRIPT_PID" 2024-12-18T01:21:55.3286900Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.3287293Z env: 2024-12-18T01:21:55.3287524Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.3288007Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.3288534Z MONITOR_SCRIPT_PID: 316742 2024-12-18T01:21:55.3288806Z ##[endgroup] 2024-12-18T01:21:55.3435808Z Prepare all required actions 2024-12-18T01:21:55.3436447Z Getting action download info 2024-12-18T01:21:55.4706165Z Download action repository 'actions/upload-artifact@v4' (SHA:6f51ac03b9356f520e9adb1b1b7802705f340c2b) 2024-12-18T01:21:55.7664865Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-12-18T01:21:55.7665240Z with: 2024-12-18T01:21:55.7665572Z file-suffix: test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-18T01:21:55.7665987Z s3-bucket: gha-artifacts 2024-12-18T01:21:55.7666266Z env: 2024-12-18T01:21:55.7666492Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.7666968Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.7667481Z ##[endgroup] 2024-12-18T01:21:55.7694737Z ##[group]Run # Remove any previous test jsons if they exist 2024-12-18T01:21:55.7695221Z # Remove any previous test jsons if they exist 2024-12-18T01:21:55.7695617Z rm -f test-jsons-*.zip 2024-12-18T01:21:55.7696117Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2024-12-18T01:21:55.7701783Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.7702268Z env: 2024-12-18T01:21:55.7702486Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.7702973Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.7703593Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-18T01:21:55.7704004Z ##[endgroup] 2024-12-18T01:21:55.7830376Z adding: test/test-reports/td_exclusions-20c35d042b6915d8b0eb.json (deflated 81%) 2024-12-18T01:21:55.7831049Z adding: test/test-reports/td_exclusions-fe490d41bb6d555ce597.json (deflated 73%) 2024-12-18T01:21:55.7858092Z ##[group]Run # Remove any previous test reports if they exist 2024-12-18T01:21:55.7858584Z # Remove any previous test reports if they exist 2024-12-18T01:21:55.7858984Z rm -f test-reports-*.zip 2024-12-18T01:21:55.7859480Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2024-12-18T01:21:55.7864785Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.7865191Z env: 2024-12-18T01:21:55.7865421Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.7865895Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.7866510Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-18T01:21:55.7866903Z ##[endgroup] 2024-12-18T01:21:55.7952076Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-866c556241057339.xml (deflated 97%) 2024-12-18T01:21:55.7985970Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-a9d44e94c2ee176f.xml (deflated 98%) 2024-12-18T01:21:55.7987392Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-55419b1067ffb60b.xml (deflated 90%) 2024-12-18T01:21:55.7994901Z adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-30393d7adcfe3b4e.xml (deflated 95%) 2024-12-18T01:21:55.7996570Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-9a71cbf2ea9fe87b.xml (deflated 90%) 2024-12-18T01:21:55.7998575Z adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-8d2c33cf08c0ff60.xml (deflated 36%) 2024-12-18T01:21:55.7999996Z adding: test/test-reports/python-pytest/test_namedtuple_return_api/test_namedtuple_return_api-169cf06af96ffa93.xml (deflated 72%) 2024-12-18T01:21:55.8001006Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-5c0833b0efb4da8f.xml (deflated 56%) 2024-12-18T01:21:55.8001893Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-ca8a3361a91dc36f.xml (deflated 86%) 2024-12-18T01:21:55.8002782Z adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-dc699dfe7097f138.xml (deflated 95%) 2024-12-18T01:21:55.8005956Z adding: test/test-reports/python-pytest/test_fake_tensor/test_fake_tensor-ea2c74963bdd86f9.xml (deflated 93%) 2024-12-18T01:21:55.8033800Z adding: test/test-reports/python-pytest/test_fx/test_fx-8a6530ce8ab84671.xml (deflated 96%) 2024-12-18T01:21:55.8036336Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-f899c90fac5773f4.xml (deflated 89%) 2024-12-18T01:21:55.8038237Z adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-084a67d3b5443f3c.xml (deflated 95%) 2024-12-18T01:21:55.8041060Z adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-571af718d513f553.xml (deflated 90%) 2024-12-18T01:21:55.8043346Z adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-276647d545e340bc.xml (deflated 89%) 2024-12-18T01:21:55.8047902Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-81ad8354948651df.xml (deflated 92%) 2024-12-18T01:21:55.8049003Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-9807f4353ba10e73.xml (deflated 36%) 2024-12-18T01:21:55.8052593Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-5b26bb002adfdcee.xml (deflated 94%) 2024-12-18T01:21:55.8053903Z adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-25e0e5ed37105b84.xml (deflated 59%) 2024-12-18T01:21:55.8070427Z adding: test/test-reports/python-pytest/nn.test_convolution/nn.test_convolution-2ee72f6dc23cbdb5.xml (deflated 97%) 2024-12-18T01:21:55.8105047Z adding: test/test-reports/python-pytest/test_nn/test_nn-37af97c4d9b2a590.xml (deflated 95%) 2024-12-18T01:21:55.8135318Z adding: test/test-reports/python-pytest/test_nn/test_nn-d68961790ad52316.xml (deflated 96%) 2024-12-18T01:21:55.8137060Z adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-09c931b5ac0d463b.xml (deflated 93%) 2024-12-18T01:21:55.8160802Z adding: test/test-reports/python-pytest/test_overrides/test_overrides-56201d7079723f42.xml (deflated 96%) 2024-12-18T01:21:55.8164031Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-90c42b861eb35153.xml (deflated 94%) 2024-12-18T01:21:55.8166392Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-0225b045c2f33798.xml (deflated 93%) 2024-12-18T01:21:55.8167508Z adding: test/test-reports/python-pytest/test_quantization/test_quantization-05c6d3cb93e9ac15.xml (deflated 28%) 2024-12-18T01:21:55.8200341Z adding: test/test-reports/python-pytest/test_quantization/test_quantization-56de419f967c67fb.xml (deflated 96%) 2024-12-18T01:21:55.8201258Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-b7dd639dfe5e1636.xml (deflated 29%) 2024-12-18T01:21:55.8202123Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-8b20096dbadd4744.xml (deflated 29%) 2024-12-18T01:21:55.8202991Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-a3afb3805fcfc5a1.xml (deflated 29%) 2024-12-18T01:21:55.8203856Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-277b5488b4791f58.xml (deflated 29%) 2024-12-18T01:21:55.8204857Z adding: 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test/test-reports/python-pytest/test.run_test/test.run_test-1c4f12e6f3b7b55b.xml (deflated 57%) 2024-12-18T01:21:55.8229940Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-7f13b38fe35e0d4d.xml (deflated 58%) 2024-12-18T01:21:55.8230781Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-96861027bb9adfae.xml (deflated 59%) 2024-12-18T01:21:55.8231701Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-7dc6775799e2e3b0.xml (deflated 37%) 2024-12-18T01:21:55.8232562Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-4bc77ae90fc8fe4a.xml (deflated 36%) 2024-12-18T01:21:55.8233421Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-df8a6cda490652f8.xml (deflated 90%) 2024-12-18T01:21:55.8234387Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241218010947.xml (deflated 43%) 2024-12-18T01:21:55.8235458Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241218011010.xml (deflated 43%) 2024-12-18T01:21:55.8259978Z ##[group]Run # Remove any previous usage logs if they exist 2024-12-18T01:21:55.8260460Z # Remove any previous usage logs if they exist 2024-12-18T01:21:55.8260835Z rm -f logs-*.zip 2024-12-18T01:21:55.8261328Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-12-18T01:21:55.8261874Z # so check to see if the file exists first 2024-12-18T01:21:55.8262245Z if [ -f 'usage_log.txt' ]; then 2024-12-18T01:21:55.8262627Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-12-18T01:21:55.8262987Z fi 2024-12-18T01:21:55.8263368Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2024-12-18T01:21:55.8263933Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2024-12-18T01:21:55.8264332Z fi 2024-12-18T01:21:55.8269451Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.8269839Z env: 2024-12-18T01:21:55.8270071Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.8270546Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.8271158Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-18T01:21:55.8271562Z ##[endgroup] 2024-12-18T01:21:55.8407785Z adding: usage_log.txt (deflated 98%) 2024-12-18T01:21:55.8511723Z adding: test/test-reports/test_reductions_1.3_3d3a075aa7d77442_.log (deflated 95%) 2024-12-18T01:21:55.8545681Z adding: test/test-reports/test_reductions_3.3_5bb44456a4cdfdbf_.log (deflated 95%) 2024-12-18T01:21:55.8546906Z adding: test/test-reports/test_cuda_nvml_based_avail_1.1_5876ab19b7ef0294_.log (deflated 11%) 2024-12-18T01:21:55.8547848Z adding: test/test-reports/test_cuda_primary_ctx_1.1_b9f5e0e3295495cf_.log (deflated 11%) 2024-12-18T01:21:55.8548634Z adding: test/test-reports/test_cpp_extensions_aot_ninja_1.1_3c4e9f3ee6693490_.log (deflated 76%) 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(deflated 60%) 2024-12-18T01:21:55.8818636Z adding: test/test-reports/cpp.scalar_test_1.1_cf33e96555ef83b1_.log (deflated 59%) 2024-12-18T01:21:55.8841870Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-12-18T01:21:55.8842437Z # Remove any previous debugging artifacts if they exist 2024-12-18T01:21:55.8842865Z rm -f debug-*.zip 2024-12-18T01:21:55.8843161Z if [ -d 'test/debug' ]; then 2024-12-18T01:21:55.8843568Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-12-18T01:21:55.8843976Z fi 2024-12-18T01:21:55.8849075Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:55.8849472Z env: 2024-12-18T01:21:55.8849708Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.8850191Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.8850805Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566046822 2024-12-18T01:21:55.8851195Z ##[endgroup] 2024-12-18T01:21:55.8937121Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:21:55.8937472Z with: 2024-12-18T01:21:55.8937704Z s3-bucket: gha-artifacts 2024-12-18T01:21:55.8938232Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:55.8938597Z retention-days: 14 2024-12-18T01:21:55.8938868Z if-no-files-found: warn 2024-12-18T01:21:55.8939151Z path: test-jsons-*.zip 2024-12-18T01:21:55.8939422Z name: artifact 2024-12-18T01:21:55.8939651Z region: us-east-1 2024-12-18T01:21:55.8939891Z env: 2024-12-18T01:21:55.8940114Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:55.8940602Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:55.8941118Z ##[endgroup] 2024-12-18T01:21:56.2620880Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T01:21:56.2621586Z With the provided path, there will be 1 file uploaded 2024-12-18T01:21:56.2622055Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:56.3705339Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_34566046822.zip 2024-12-18T01:21:56.5128217Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_34566046822.zip 2024-12-18T01:21:56.5313870Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:21:56.5314225Z with: 2024-12-18T01:21:56.5314449Z s3-bucket: gha-artifacts 2024-12-18T01:21:56.5314790Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:56.5315161Z retention-days: 14 2024-12-18T01:21:56.5315436Z if-no-files-found: error 2024-12-18T01:21:56.5315751Z path: test-reports-*.zip 2024-12-18T01:21:56.5316010Z name: artifact 2024-12-18T01:21:56.5316250Z region: us-east-1 2024-12-18T01:21:56.5316485Z env: 2024-12-18T01:21:56.5316703Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:56.5317186Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:56.5317678Z ##[endgroup] 2024-12-18T01:21:56.8562012Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T01:21:56.8562758Z With the provided path, there will be 1 file uploaded 2024-12-18T01:21:56.8563641Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:56.8604320Z Starting upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_34566046822.zip 2024-12-18T01:21:56.9800955Z Finished upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_34566046822.zip 2024-12-18T01:21:56.9990791Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:21:56.9991146Z with: 2024-12-18T01:21:56.9991362Z s3-bucket: gha-artifacts 2024-12-18T01:21:56.9991715Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:56.9992079Z retention-days: 14 2024-12-18T01:21:56.9992347Z if-no-files-found: ignore 2024-12-18T01:21:56.9992817Z path: logs-*.zip 2024-12-18T01:21:56.9993050Z name: artifact 2024-12-18T01:21:56.9993292Z region: us-east-1 2024-12-18T01:21:56.9993532Z env: 2024-12-18T01:21:56.9993754Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:56.9994229Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:56.9994723Z ##[endgroup] 2024-12-18T01:21:57.3223139Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T01:21:57.3223633Z With the provided path, there will be 1 file uploaded 2024-12-18T01:21:57.3224114Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:57.3263154Z Starting upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_34566046822.zip 2024-12-18T01:21:57.5577838Z Finished upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_34566046822.zip 2024-12-18T01:21:57.5764267Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:21:57.5764619Z with: 2024-12-18T01:21:57.5764835Z s3-bucket: gha-artifacts 2024-12-18T01:21:57.5765195Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:21:57.5765560Z retention-days: 14 2024-12-18T01:21:57.5765827Z if-no-files-found: ignore 2024-12-18T01:21:57.5766106Z path: debug-*.zip 2024-12-18T01:21:57.5766337Z name: artifact 2024-12-18T01:21:57.5766573Z region: us-east-1 2024-12-18T01:21:57.5766810Z env: 2024-12-18T01:21:57.5767029Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:57.5767503Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:57.5768001Z ##[endgroup] 2024-12-18T01:21:57.8951205Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-12-18T01:21:57.9147440Z ##[group]Run # shellcheck disable=SC2156 2024-12-18T01:21:57.9147848Z # shellcheck disable=SC2156 2024-12-18T01:21:57.9148546Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-12-18T01:21:57.9154849Z shell: /usr/bin/bash -e {0} 2024-12-18T01:21:57.9155161Z env: 2024-12-18T01:21:57.9155398Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:57.9155882Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:57.9156403Z ##[endgroup] 2024-12-18T01:21:58.1382463Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@release/2.6 2024-12-18T01:21:58.1382939Z with: 2024-12-18T01:21:58.1383153Z env: 2024-12-18T01:21:58.1383364Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:58.1383840Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:58.1384350Z ##[endgroup] 2024-12-18T01:21:58.1405405Z ##[group]Run set -eou pipefail 2024-12-18T01:21:58.1405741Z set -eou pipefail 2024-12-18T01:21:58.1406023Z  2024-12-18T01:21:58.1406391Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-12-18T01:21:58.1406874Z for _ in $(seq 1440); do 2024-12-18T01:21:58.1407227Z  # Break if no ssh session exists anymore 2024-12-18T01:21:58.1407594Z  if [ "$(who)" = "" ]; then 2024-12-18T01:21:58.1407907Z  break 2024-12-18T01:21:58.1408144Z  fi 2024-12-18T01:21:58.1408431Z  echo "." 2024-12-18T01:21:58.1408684Z  sleep 5 2024-12-18T01:21:58.1408917Z done 2024-12-18T01:21:58.1414413Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:58.1414807Z env: 2024-12-18T01:21:58.1415038Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:58.1415517Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:58.1416019Z ##[endgroup] 2024-12-18T01:21:58.1438080Z Holding runner for 2 hours until all ssh sessions have logged out 2024-12-18T01:21:58.1507729Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-12-18T01:21:58.1508310Z # ignore expansion of "docker ps -q" since it could be empty 2024-12-18T01:21:58.1508993Z # shellcheck disable=SC2046 2024-12-18T01:21:58.1509334Z docker stop $(docker ps -q) || true 2024-12-18T01:21:58.1509700Z # Prune all of the docker images 2024-12-18T01:21:58.1510051Z docker system prune -af 2024-12-18T01:21:58.1515399Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:21:58.1515795Z env: 2024-12-18T01:21:58.1516020Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:21:58.1516484Z DOCKER_CONTAINER_ID: d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:58.1516999Z ##[endgroup] 2024-12-18T01:21:58.8596363Z d5aa3dabf956 2024-12-18T01:21:59.4622438Z Deleted Containers: 2024-12-18T01:21:59.4622900Z d5aa3dabf95651b5e78c39d042c839031ce3c473a94dcb0196a04da4479b14c2 2024-12-18T01:21:59.4623258Z 2024-12-18T01:22:06.4507926Z Deleted Images: 2024-12-18T01:22:06.4508878Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-18T01:22:06.4510213Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10@sha256:773389d5cd8b8fbf70c2486a99e636d02b0c002d125f9d1749ec5e7514de0f47 2024-12-18T01:22:06.4511293Z deleted: sha256:59a4df73988ee309dbf7bbeebf91f7b6258b2da2804e245afce1c5d776a178fb 2024-12-18T01:22:06.4511964Z deleted: sha256:2c53158d7d4b9bab14e31dcbde8b202bb74a58ee552f24bc0d98420c011d8891 2024-12-18T01:22:06.4512652Z deleted: sha256:0e9c5fcefe1e000cbaf36da6dede7cab1b28cdb75f313dca87de4fc0f1d402b3 2024-12-18T01:22:06.4513340Z deleted: sha256:abac89d0f83b312aeae27d9382e411887cc86ace73a4dda8bcfec8e47c8453fa 2024-12-18T01:22:06.4514020Z deleted: sha256:bbef1438f8ed69a2aacedafe7fcbd428708bc476097d4617d23a07c239da7e2e 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overriding HOME='/home/ec2-user/actions-runner/_work/_temp/2ed7fa35-90ac-478c-bde3-993234a57cfd' before making global git config changes 2024-12-18T01:22:06.5648596Z Adding repository directory to the temporary git global config as a safe directory 2024-12-18T01:22:06.5652215Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-18T01:22:06.5683357Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-12-18T01:22:06.5710366Z [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' || :" 2024-12-18T01:22:06.6019293Z Entering 'android/libs/fbjni' 2024-12-18T01:22:06.6074982Z Entering 'third_party/FP16' 2024-12-18T01:22:06.6127322Z Entering 'third_party/FXdiv' 2024-12-18T01:22:06.6179555Z Entering 'third_party/NNPACK' 2024-12-18T01:22:06.6231511Z Entering 'third_party/NVTX' 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Entering 'android/libs/fbjni' 2024-12-18T01:22:07.0205263Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0238885Z Entering 'third_party/FP16' 2024-12-18T01:22:07.0271903Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0303538Z Entering 'third_party/FXdiv' 2024-12-18T01:22:07.0338117Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0369859Z Entering 'third_party/NNPACK' 2024-12-18T01:22:07.0403569Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0434803Z Entering 'third_party/NVTX' 2024-12-18T01:22:07.0469833Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0501106Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-18T01:22:07.0533765Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0565762Z Entering 'third_party/XNNPACK' 2024-12-18T01:22:07.0599048Z http.https://github.com/.extraheader 2024-12-18T01:22:07.0647291Z Entering 'third_party/benchmark' 2024-12-18T01:22:07.0680041Z http.https://github.com/.extraheader 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2024-12-18T01:22:07.1694669Z http.https://github.com/.extraheader 2024-12-18T01:22:07.1726725Z Entering 'third_party/gloo' 2024-12-18T01:22:07.1760358Z http.https://github.com/.extraheader 2024-12-18T01:22:07.1792283Z Entering 'third_party/googletest' 2024-12-18T01:22:07.1826879Z http.https://github.com/.extraheader 2024-12-18T01:22:07.1860003Z Entering 'third_party/ideep' 2024-12-18T01:22:07.1894653Z http.https://github.com/.extraheader 2024-12-18T01:22:07.1925141Z Entering 'third_party/ideep/mkl-dnn' 2024-12-18T01:22:07.1958209Z http.https://github.com/.extraheader 2024-12-18T01:22:07.1997129Z Entering 'third_party/ittapi' 2024-12-18T01:22:07.2031799Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2064156Z Entering 'third_party/kineto' 2024-12-18T01:22:07.2099037Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2130369Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-18T01:22:07.2165500Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2197567Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-18T01:22:07.2231277Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2264696Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-18T01:22:07.2297430Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2328999Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-18T01:22:07.2362031Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2393597Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-18T01:22:07.2426579Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2457865Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-18T01:22:07.2490960Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2523989Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-18T01:22:07.2557431Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2588950Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-18T01:22:07.2622193Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2654499Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-18T01:22:07.2686927Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2719257Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-18T01:22:07.2752642Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2785133Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-18T01:22:07.2818204Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2849572Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-18T01:22:07.2882135Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2915216Z Entering 'third_party/mimalloc' 2024-12-18T01:22:07.2953011Z http.https://github.com/.extraheader 2024-12-18T01:22:07.2984325Z Entering 'third_party/nccl/nccl' 2024-12-18T01:22:07.3019345Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3055445Z Entering 'third_party/nlohmann' 2024-12-18T01:22:07.3089851Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3123575Z Entering 'third_party/onnx' 2024-12-18T01:22:07.3158450Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3208228Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-18T01:22:07.3243227Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3278856Z Entering 'third_party/opentelemetry-cpp' 2024-12-18T01:22:07.3315603Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3351201Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-18T01:22:07.3386948Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3419242Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-18T01:22:07.3453189Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3484928Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-18T01:22:07.3519285Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3550230Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-18T01:22:07.3583445Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3616602Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-18T01:22:07.3653176Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3684474Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-18T01:22:07.3719180Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3751035Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-18T01:22:07.3784667Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3816148Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-18T01:22:07.3850159Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3884552Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-18T01:22:07.3918942Z http.https://github.com/.extraheader 2024-12-18T01:22:07.3952447Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-18T01:22:07.3985624Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4039200Z Entering 'third_party/pocketfft' 2024-12-18T01:22:07.4074230Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4106613Z Entering 'third_party/protobuf' 2024-12-18T01:22:07.4142218Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4177969Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-18T01:22:07.4211418Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4243069Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-18T01:22:07.4276792Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4311052Z Entering 'third_party/psimd' 2024-12-18T01:22:07.4346609Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4378441Z Entering 'third_party/pthreadpool' 2024-12-18T01:22:07.4412590Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4445138Z Entering 'third_party/pybind11' 2024-12-18T01:22:07.4479060Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4510813Z Entering 'third_party/python-peachpy' 2024-12-18T01:22:07.4546105Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4577544Z Entering 'third_party/sleef' 2024-12-18T01:22:07.4612008Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4643365Z Entering 'third_party/tensorpipe' 2024-12-18T01:22:07.4678618Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4710197Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-18T01:22:07.4743709Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4775035Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-18T01:22:07.4808139Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4839524Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-18T01:22:07.4872954Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4904174Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-18T01:22:07.4937455Z http.https://github.com/.extraheader 2024-12-18T01:22:07.4969070Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-18T01:22:07.5003089Z http.https://github.com/.extraheader 2024-12-18T01:22:07.5117914Z A job completed hook has been configured by the self-hosted runner administrator 2024-12-18T01:22:07.5144336Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-12-18T01:22:07.5149488Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:22:07.5149900Z ##[endgroup] 2024-12-18T01:22:15.1773517Z Cleaning up orphan processes